diff --git a/.github/workflows/tests-conda.yml b/.github/workflows/tests-conda.yml
index 7da872d0..db323cff 100644
--- a/.github/workflows/tests-conda.yml
+++ b/.github/workflows/tests-conda.yml
@@ -21,7 +21,7 @@ jobs:
       fail-fast: true
       matrix:
         os: ["windows-latest"]
-        python-version: ["3.11"]
+        python-version: ["3.12"]
 
     env:
       OS: ${{ matrix.os }}
diff --git a/.github/workflows/tests-python.yml b/.github/workflows/tests-python.yml
index 12b39830..f7dc69d2 100644
--- a/.github/workflows/tests-python.yml
+++ b/.github/workflows/tests-python.yml
@@ -21,7 +21,7 @@ jobs:
       fail-fast: true
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
         # In order to save resources, only run particular
         # matrix slots on other OS than Linux.
         include:
diff --git a/.gitignore b/.gitignore
index f7fadb6f..28795a46 100644
--- a/.gitignore
+++ b/.gitignore
@@ -43,3 +43,5 @@ herbie/_version.py
 
 *.pkl
 .coverage
+
+.ruff_cache/
diff --git a/ci/environment.yml b/ci/environment.yml
index 450521be..c882e0ee 100644
--- a/ci/environment.yml
+++ b/ci/environment.yml
@@ -9,7 +9,7 @@ dependencies:
   - metpy
   - numpy>=1.26.2
   - pandas>=2.1.4
-  - pygrib
+  - pygrib>=2.1.6
   - pytest
   - requests>=2.31.0
   - scikit-learn
diff --git a/ci/requirements.txt b/ci/requirements.txt
index 8909e96d..3ce36f2a 100644
--- a/ci/requirements.txt
+++ b/ci/requirements.txt
@@ -1,7 +1,8 @@
-cfgrib>=0.9.9.1
+cfgrib>=0.9.15
 metpy>=1.3.0
 numpy>=1.22.3
-pandas>=1.4.1
-requests>=2.27.1
+pandas>=2.2.3
+pygrib>=2.1.6
+requests>=2.32.3
 toml>=0.10.2
-xarray>=2022.3.0
+xarray>=2025.1.1
diff --git a/docs/gallery/noaa_models/cfs.ipynb b/docs/gallery/noaa_models/cfs.ipynb
new file mode 100644
index 00000000..d604672a
--- /dev/null
+++ b/docs/gallery/noaa_models/cfs.ipynb
@@ -0,0 +1,2780 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CFS\n",
+    "\n",
+    "NCEP's Climate Forecast System (version 2) has been operational since 2011.\n",
+    "\n",
+    "<https://cfs.ncep.noaa.gov/>\n",
+    "\n",
+    "There are three products available\n",
+    "\n",
+    "- [`product=\"time_series\"`](#time-series-product)\n",
+    "- [`product=\"6_hourly\"`](#6-hourly-product)\n",
+    "- [`product=\"monthly\"`](#monthly-product)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from herbie import Herbie\n",
+    "from herbie.toolbox import EasyMap, pc, ccrs\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series product\n",
+    "\n",
+    "For reference of what variables are avaialble: <https://www.nco.ncep.noaa.gov/pmb/products/cfs/#DAILY>\n",
+    "\n",
+    "Herbie expects the following arguments for CFS times series product:\n",
+    "- `member`, an integer 1, 2, 3, or 4\n",
+    "- `variable`, a file's variable short name like `tmp2m`, or `z500`, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/blaylock/GITHUB/Herbie/herbie/models/cfs.py:139: UserWarning: Herbie's CFS templates are and subject to major changes. PRs are welcome to improve it.\n",
+      "  warnings.warn(\n",
+      "/home/blaylock/GITHUB/Herbie/herbie/models/cfs.py:178: UserWarning: Variable tmp2m is not in the list of available time series variables. Expected one of {'wnd10m': 'Wind u and v at 10 m', 'wnd1000': 'Wind u and v at 1000 hPa', 'wnd925': 'Wind u and v at 925 hPa', 'wnd850': 'Wind u and v at 850 hPa', 'wnd500': 'Wind u and v at 500 hPa', 'wnd700': 'Wind u and v at 700 hPa', 'wnd250': 'Wind u and v at 250 hPa', 'wnd200': 'Wind u and v at 200 hPa', 'wndstrs': 'Stress at surface', 'prmsl': 'Pressure Reduced to MSL', 'pressfc': 'Surface Pressure', 'z1000': 'GeoPotential Height at 1000 hPa', 'z850': 'GeoPotential Height at 850 hPa', 'z700': 'GeoPotential Height at 700 hPa', 'z500': 'GeoPotential Height at 500 hPa', 'z200': 'GeoPotential Height at 200 hPa', 'tmpsfc': 'Temperature at the Surface', 'tmp2m': 'Temperature at 2 m', 'tmin': 'Minimum Temperature at 2 m', 'tmax': 'Maximum Temperature at 2 m', 't1000': 'Temperature at 1000 hPa', 't850': 'Temperature at 850 hPa', 't700': 'Temperature at 700 hPa', 't500': 'Temperature at 500 hPa', 't250': 'Temperature at 250 hPa', 't200': 'Temperature at 200 hPa', 't50': 'Temperature at 50 hPa', 't2': 'Temperature at 2 hPa', 'tmphy1': 'Temperature at hybrid level 1', 'q2m': 'Specific Humidity at 2 m', 'q925': 'Specific Humidity at 925 hPa', 'q850': 'Specific Humidity at 850 hPa', 'q700': 'Specific Humidity at 700 hPa', 'q500': 'Specific Humidity at 500 hPa', 'chi850': 'Velocity Potential at 850 hPa', 'chi200': 'Velocity Potential at 200 hPa', 'dlwsfc': 'Downward Longwave Radiation at the Surface', 'dswsfc': 'Downward Shortwave Radiation at the Surface', 'ulwsfc': 'Upward Longwave Radiation at the Surface', 'uswsfc': 'Upward Shortwave Radiation at the Surface', 'ulwtoa': 'Upward Longwave Radiation at the Top of the Atmosphere', 'uswtoa': 'Upward Shortwave Radiation at the Top of the Atmosphere', 'gflux': 'Ground Heat Flux', 'lhtfl': 'Latent Heat Net Flux at the Surface', 'shtfl': 'Sensible Heat Net Flux at the Surface', 'cprat': 'Convective Precipitation Rate', 'csdlf': 'Clear Sky Downward Longwave Flux', 'csdsf': 'Clear Sky Downward Solar Flux', 'csusf': 'Clear Sky Upward Solar Flux', 'icecon': 'Surface Ice Cover', 'icethk': 'Surface Ice Thickness', 'ipv450': 'Isentropic Potential Vorticity at 450 K', 'ipv550': 'Isentropic Potential Vorticity at 550 K', 'ipv650': 'Isentropic Potential Vorticity at 650 K', 'nddsf': 'Near IR Diffuse Downward Solar Flux', 'tcdcclm': 'Total Cloud Cover', 'prate': 'Total Precipitation Rate', 'psi200': 'Stream Function at 200 hPa', 'psi850': 'Stream Function at 850 hPa', 'pwat': 'Precipitable Water', 'runoff': 'Surface Runoff', 'snohf': 'Snowfall Rate Water Equivalent', 'soilm1': 'Vol. Soil Moisture Content, 0-10 cm Below Ground', 'soilm2': 'Vol. Soil Moisture Content, 10-40 cm Below Ground', 'soilm3': 'Vol. Soil Moisture Content, 40-100 cm Below Ground', 'soilm4': 'Vol. Soil Moisture Content, 100-200 cm Below Ground', 'soilt1': 'Soil Temperature, 0-0.1 m Below Ground', 'vddsf': 'Visible Diffuse Downward Solar Flux', 'vvel500': 'Vertical Velocity at 500 hPa', 'weasd': 'Water Equivalent of Accumulated Snow Depth', 'ocnmld': 'Mixed Layer Depth', 'ocnslh': 'Sea Level Height', 'ocnsild': 'Surface Isothermal Layer Depth', 'ocndt2.5c': 'Isothermal Layer Depth 2.5 Celsius', 'ocndt5c': 'Isothermal Layer Depth 5 Celsius', 'ocndt10c': 'Isothermal Layer Depth 10 Celsius', 'ocndt15c': 'Isothermal Layer Depth 15 Celsius', 'ocndt20c': 'Isothermal Layer Depth 20 Celsius', 'ocndt25c': 'Isothermal Layer Depth 25 Celsius', 'ocndt28c': 'Isothermal Layer Depth 28 Celsius', 'ocnsst': 'Temperature at depth 5 meters', 'ocnt15': 'Potential temperature 15 meters below', 'ocnheat': 'Heat Content', 'ocntchp': 'Tropical Cyclone Heat Potential', 'ocnsal5': 'Salinity at depth 5 meters', 'ocnsal15': 'Salinity at depth 15 meters', 'ocnu5': 'Zonal Current at depth 5 meters', 'ocnu15': 'Zonal Current at depth 15 meters', 'ocnv5': 'Meridional Current at depth 5 meters', 'ocnv15': 'Meridional Current at depth 15 meters', 'ocnvv55': 'Vertical Velocity at depth 55 meters'}\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Found ┊ model=cfs ┊ \u001b[3mproduct=time_series\u001b[0m ┊ \u001b[38;2;41;130;13m2025-Jan-01 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ aws\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[48;2;255;255;255m\u001b[38;2;136;33;27m▌\u001b[0m\u001b[38;2;12;53;118m\u001b[48;2;240;234;210m▌\u001b[38;2;0;0;0m\u001b[1mHerbie\u001b[0m CFS model \u001b[3mtime_series\u001b[0m product initialized \u001b[38;2;41;130;13m2025-Jan-01 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3msource=aws\u001b[0m"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H = Herbie(\n",
+    "    \"2025-01-01\",\n",
+    "    model=\"cfs\",\n",
+    "    product=\"time_series\",\n",
+    "    member=1,\n",
+    "    variable=\"tmp2m\",\n",
+    ")\n",
+    "\n",
+    "H"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'aws': 'https://noaa-cfs-pds.s3.amazonaws.com/cfs.20250101/00/time_grib_01/tmp2m.01.2025010100.daily.grb2',\n",
+       " 'nomads': 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/cfs.20250101/00/time_grib_01/tmp2m.01.2025010100.daily.grb2'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.SOURCES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'wnd10m': 'Wind u and v at 10 m',\n",
+       " 'wnd1000': 'Wind u and v at 1000 hPa',\n",
+       " 'wnd925': 'Wind u and v at 925 hPa',\n",
+       " 'wnd850': 'Wind u and v at 850 hPa',\n",
+       " 'wnd500': 'Wind u and v at 500 hPa',\n",
+       " 'wnd700': 'Wind u and v at 700 hPa',\n",
+       " 'wnd250': 'Wind u and v at 250 hPa',\n",
+       " 'wnd200': 'Wind u and v at 200 hPa',\n",
+       " 'wndstrs': 'Stress at surface',\n",
+       " 'prmsl': 'Pressure Reduced to MSL',\n",
+       " 'pressfc': 'Surface Pressure',\n",
+       " 'z1000': 'GeoPotential Height at 1000 hPa',\n",
+       " 'z850': 'GeoPotential Height at 850 hPa',\n",
+       " 'z700': 'GeoPotential Height at 700 hPa',\n",
+       " 'z500': 'GeoPotential Height at 500 hPa',\n",
+       " 'z200': 'GeoPotential Height at 200 hPa',\n",
+       " 'tmpsfc': 'Temperature at the Surface',\n",
+       " 'tmp2m': 'Temperature at 2 m',\n",
+       " 'tmin': 'Minimum Temperature at 2 m',\n",
+       " 'tmax': 'Maximum Temperature at 2 m',\n",
+       " 't1000': 'Temperature at 1000 hPa',\n",
+       " 't850': 'Temperature at 850 hPa',\n",
+       " 't700': 'Temperature at 700 hPa',\n",
+       " 't500': 'Temperature at 500 hPa',\n",
+       " 't250': 'Temperature at 250 hPa',\n",
+       " 't200': 'Temperature at 200 hPa',\n",
+       " 't50': 'Temperature at 50 hPa',\n",
+       " 't2': 'Temperature at 2 hPa',\n",
+       " 'tmphy1': 'Temperature at hybrid level 1',\n",
+       " 'q2m': 'Specific Humidity at 2 m',\n",
+       " 'q925': 'Specific Humidity at 925 hPa',\n",
+       " 'q850': 'Specific Humidity at 850 hPa',\n",
+       " 'q700': 'Specific Humidity at 700 hPa',\n",
+       " 'q500': 'Specific Humidity at 500 hPa',\n",
+       " 'chi850': 'Velocity Potential at 850 hPa',\n",
+       " 'chi200': 'Velocity Potential at 200 hPa',\n",
+       " 'dlwsfc': 'Downward Longwave Radiation at the Surface',\n",
+       " 'dswsfc': 'Downward Shortwave Radiation at the Surface',\n",
+       " 'ulwsfc': 'Upward Longwave Radiation at the Surface',\n",
+       " 'uswsfc': 'Upward Shortwave Radiation at the Surface',\n",
+       " 'ulwtoa': 'Upward Longwave Radiation at the Top of the Atmosphere',\n",
+       " 'uswtoa': 'Upward Shortwave Radiation at the Top of the Atmosphere',\n",
+       " 'gflux': 'Ground Heat Flux',\n",
+       " 'lhtfl': 'Latent Heat Net Flux at the Surface',\n",
+       " 'shtfl': 'Sensible Heat Net Flux at the Surface',\n",
+       " 'cprat': 'Convective Precipitation Rate',\n",
+       " 'csdlf': 'Clear Sky Downward Longwave Flux',\n",
+       " 'csdsf': 'Clear Sky Downward Solar Flux',\n",
+       " 'csusf': 'Clear Sky Upward Solar Flux',\n",
+       " 'icecon': 'Surface Ice Cover',\n",
+       " 'icethk': 'Surface Ice Thickness',\n",
+       " 'ipv450': 'Isentropic Potential Vorticity at 450 K',\n",
+       " 'ipv550': 'Isentropic Potential Vorticity at 550 K',\n",
+       " 'ipv650': 'Isentropic Potential Vorticity at 650 K',\n",
+       " 'nddsf': 'Near IR Diffuse Downward Solar Flux',\n",
+       " 'tcdcclm': 'Total Cloud Cover',\n",
+       " 'prate': 'Total Precipitation Rate',\n",
+       " 'psi200': 'Stream Function at 200 hPa',\n",
+       " 'psi850': 'Stream Function at 850 hPa',\n",
+       " 'pwat': 'Precipitable Water',\n",
+       " 'runoff': 'Surface Runoff',\n",
+       " 'snohf': 'Snowfall Rate Water Equivalent',\n",
+       " 'soilm1': 'Vol. Soil Moisture Content, 0-10 cm Below Ground',\n",
+       " 'soilm2': 'Vol. Soil Moisture Content, 10-40 cm Below Ground',\n",
+       " 'soilm3': 'Vol. Soil Moisture Content, 40-100 cm Below Ground',\n",
+       " 'soilm4': 'Vol. Soil Moisture Content, 100-200 cm Below Ground',\n",
+       " 'soilt1': 'Soil Temperature, 0-0.1 m Below Ground',\n",
+       " 'vddsf': 'Visible Diffuse Downward Solar Flux',\n",
+       " 'vvel500': 'Vertical Velocity at 500 hPa',\n",
+       " 'weasd': 'Water Equivalent of Accumulated Snow Depth',\n",
+       " 'ocnmld': 'Mixed Layer Depth',\n",
+       " 'ocnslh': 'Sea Level Height',\n",
+       " 'ocnsild': 'Surface Isothermal Layer Depth',\n",
+       " 'ocndt2.5c': 'Isothermal Layer Depth 2.5 Celsius',\n",
+       " 'ocndt5c': 'Isothermal Layer Depth 5 Celsius',\n",
+       " 'ocndt10c': 'Isothermal Layer Depth 10 Celsius',\n",
+       " 'ocndt15c': 'Isothermal Layer Depth 15 Celsius',\n",
+       " 'ocndt20c': 'Isothermal Layer Depth 20 Celsius',\n",
+       " 'ocndt25c': 'Isothermal Layer Depth 25 Celsius',\n",
+       " 'ocndt28c': 'Isothermal Layer Depth 28 Celsius',\n",
+       " 'ocnsst': 'Temperature at depth 5 meters',\n",
+       " 'ocnt15': 'Potential temperature 15 meters below',\n",
+       " 'ocnheat': 'Heat Content',\n",
+       " 'ocntchp': 'Tropical Cyclone Heat Potential',\n",
+       " 'ocnsal5': 'Salinity at depth 5 meters',\n",
+       " 'ocnsal15': 'Salinity at depth 15 meters',\n",
+       " 'ocnu5': 'Zonal Current at depth 5 meters',\n",
+       " 'ocnu15': 'Zonal Current at depth 15 meters',\n",
+       " 'ocnv5': 'Meridional Current at depth 5 meters',\n",
+       " 'ocnv15': 'Meridional Current at depth 15 meters',\n",
+       " 'ocnvv55': 'Vertical Velocity at depth 55 meters'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What are the available time series variables values?\n",
+    "# You may import the dictionary of values or refer to\n",
+    "# https://www.nco.ncep.noaa.gov/pmb/products/cfs/\n",
+    "\n",
+    "from herbie.models.cfs import time_series_variables\n",
+    "\n",
+    "time_series_variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/blaylock/GITHUB/Herbie/herbie/models/cfs.py:178: UserWarning: Variable wnd10m is not in the list of available time series variables. Expected one of {'wnd10m': 'Wind u and v at 10 m', 'wnd1000': 'Wind u and v at 1000 hPa', 'wnd925': 'Wind u and v at 925 hPa', 'wnd850': 'Wind u and v at 850 hPa', 'wnd500': 'Wind u and v at 500 hPa', 'wnd700': 'Wind u and v at 700 hPa', 'wnd250': 'Wind u and v at 250 hPa', 'wnd200': 'Wind u and v at 200 hPa', 'wndstrs': 'Stress at surface', 'prmsl': 'Pressure Reduced to MSL', 'pressfc': 'Surface Pressure', 'z1000': 'GeoPotential Height at 1000 hPa', 'z850': 'GeoPotential Height at 850 hPa', 'z700': 'GeoPotential Height at 700 hPa', 'z500': 'GeoPotential Height at 500 hPa', 'z200': 'GeoPotential Height at 200 hPa', 'tmpsfc': 'Temperature at the Surface', 'tmp2m': 'Temperature at 2 m', 'tmin': 'Minimum Temperature at 2 m', 'tmax': 'Maximum Temperature at 2 m', 't1000': 'Temperature at 1000 hPa', 't850': 'Temperature at 850 hPa', 't700': 'Temperature at 700 hPa', 't500': 'Temperature at 500 hPa', 't250': 'Temperature at 250 hPa', 't200': 'Temperature at 200 hPa', 't50': 'Temperature at 50 hPa', 't2': 'Temperature at 2 hPa', 'tmphy1': 'Temperature at hybrid level 1', 'q2m': 'Specific Humidity at 2 m', 'q925': 'Specific Humidity at 925 hPa', 'q850': 'Specific Humidity at 850 hPa', 'q700': 'Specific Humidity at 700 hPa', 'q500': 'Specific Humidity at 500 hPa', 'chi850': 'Velocity Potential at 850 hPa', 'chi200': 'Velocity Potential at 200 hPa', 'dlwsfc': 'Downward Longwave Radiation at the Surface', 'dswsfc': 'Downward Shortwave Radiation at the Surface', 'ulwsfc': 'Upward Longwave Radiation at the Surface', 'uswsfc': 'Upward Shortwave Radiation at the Surface', 'ulwtoa': 'Upward Longwave Radiation at the Top of the Atmosphere', 'uswtoa': 'Upward Shortwave Radiation at the Top of the Atmosphere', 'gflux': 'Ground Heat Flux', 'lhtfl': 'Latent Heat Net Flux at the Surface', 'shtfl': 'Sensible Heat Net Flux at the Surface', 'cprat': 'Convective Precipitation Rate', 'csdlf': 'Clear Sky Downward Longwave Flux', 'csdsf': 'Clear Sky Downward Solar Flux', 'csusf': 'Clear Sky Upward Solar Flux', 'icecon': 'Surface Ice Cover', 'icethk': 'Surface Ice Thickness', 'ipv450': 'Isentropic Potential Vorticity at 450 K', 'ipv550': 'Isentropic Potential Vorticity at 550 K', 'ipv650': 'Isentropic Potential Vorticity at 650 K', 'nddsf': 'Near IR Diffuse Downward Solar Flux', 'tcdcclm': 'Total Cloud Cover', 'prate': 'Total Precipitation Rate', 'psi200': 'Stream Function at 200 hPa', 'psi850': 'Stream Function at 850 hPa', 'pwat': 'Precipitable Water', 'runoff': 'Surface Runoff', 'snohf': 'Snowfall Rate Water Equivalent', 'soilm1': 'Vol. Soil Moisture Content, 0-10 cm Below Ground', 'soilm2': 'Vol. Soil Moisture Content, 10-40 cm Below Ground', 'soilm3': 'Vol. Soil Moisture Content, 40-100 cm Below Ground', 'soilm4': 'Vol. Soil Moisture Content, 100-200 cm Below Ground', 'soilt1': 'Soil Temperature, 0-0.1 m Below Ground', 'vddsf': 'Visible Diffuse Downward Solar Flux', 'vvel500': 'Vertical Velocity at 500 hPa', 'weasd': 'Water Equivalent of Accumulated Snow Depth', 'ocnmld': 'Mixed Layer Depth', 'ocnslh': 'Sea Level Height', 'ocnsild': 'Surface Isothermal Layer Depth', 'ocndt2.5c': 'Isothermal Layer Depth 2.5 Celsius', 'ocndt5c': 'Isothermal Layer Depth 5 Celsius', 'ocndt10c': 'Isothermal Layer Depth 10 Celsius', 'ocndt15c': 'Isothermal Layer Depth 15 Celsius', 'ocndt20c': 'Isothermal Layer Depth 20 Celsius', 'ocndt25c': 'Isothermal Layer Depth 25 Celsius', 'ocndt28c': 'Isothermal Layer Depth 28 Celsius', 'ocnsst': 'Temperature at depth 5 meters', 'ocnt15': 'Potential temperature 15 meters below', 'ocnheat': 'Heat Content', 'ocntchp': 'Tropical Cyclone Heat Potential', 'ocnsal5': 'Salinity at depth 5 meters', 'ocnsal15': 'Salinity at depth 15 meters', 'ocnu5': 'Zonal Current at depth 5 meters', 'ocnu15': 'Zonal Current at depth 15 meters', 'ocnv5': 'Meridional Current at depth 5 meters', 'ocnv15': 'Meridional Current at depth 15 meters', 'ocnvv55': 'Vertical Velocity at depth 55 meters'}\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Found ┊ model=cfs ┊ \u001b[3mproduct=time_series\u001b[0m ┊ \u001b[38;2;41;130;13m2025-Jan-01 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ aws\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grib_message</th>\n",
+       "      <th>start_byte</th>\n",
+       "      <th>end_byte</th>\n",
+       "      <th>range</th>\n",
+       "      <th>reference_time</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>level</th>\n",
+       "      <th>forecast_time</th>\n",
+       "      <th>search_this</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59189.0</td>\n",
+       "      <td>0-59189</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>UGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>6 hour fcst</td>\n",
+       "      <td>:UGRD:10 m above ground:6 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>59190</td>\n",
+       "      <td>118009.0</td>\n",
+       "      <td>59190-118009</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>UGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:UGRD:10 m above ground:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>118010</td>\n",
+       "      <td>176851.0</td>\n",
+       "      <td>118010-176851</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>UGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>18 hour fcst</td>\n",
+       "      <td>:UGRD:10 m above ground:18 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>176852</td>\n",
+       "      <td>235807.0</td>\n",
+       "      <td>176852-235807</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>UGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>24 hour fcst</td>\n",
+       "      <td>:UGRD:10 m above ground:24 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>235808</td>\n",
+       "      <td>295010.0</td>\n",
+       "      <td>235808-295010</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>VGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>6 hour fcst</td>\n",
+       "      <td>:VGRD:10 m above ground:6 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2427</th>\n",
+       "      <td>2428</td>\n",
+       "      <td>144814943</td>\n",
+       "      <td>144875086.0</td>\n",
+       "      <td>144814943-144875086</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>VGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>7272 hour fcst</td>\n",
+       "      <td>:VGRD:10 m above ground:7272 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2428</th>\n",
+       "      <td>2429</td>\n",
+       "      <td>144875087</td>\n",
+       "      <td>144934966.0</td>\n",
+       "      <td>144875087-144934966</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>VGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>7278 hour fcst</td>\n",
+       "      <td>:VGRD:10 m above ground:7278 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2429</th>\n",
+       "      <td>2430</td>\n",
+       "      <td>144934967</td>\n",
+       "      <td>144994972.0</td>\n",
+       "      <td>144934967-144994972</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>VGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>7284 hour fcst</td>\n",
+       "      <td>:VGRD:10 m above ground:7284 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2430</th>\n",
+       "      <td>2431</td>\n",
+       "      <td>144994973</td>\n",
+       "      <td>145054851.0</td>\n",
+       "      <td>144994973-145054851</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>VGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>7290 hour fcst</td>\n",
+       "      <td>:VGRD:10 m above ground:7290 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2431</th>\n",
+       "      <td>2432</td>\n",
+       "      <td>145054852</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>145054852-</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>VGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>7296 hour fcst</td>\n",
+       "      <td>:VGRD:10 m above ground:7296 hour fcst</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2432 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      grib_message  start_byte     end_byte                range  \\\n",
+       "0                1           0      59189.0              0-59189   \n",
+       "1                2       59190     118009.0         59190-118009   \n",
+       "2                3      118010     176851.0        118010-176851   \n",
+       "3                4      176852     235807.0        176852-235807   \n",
+       "4                5      235808     295010.0        235808-295010   \n",
+       "...            ...         ...          ...                  ...   \n",
+       "2427          2428   144814943  144875086.0  144814943-144875086   \n",
+       "2428          2429   144875087  144934966.0  144875087-144934966   \n",
+       "2429          2430   144934967  144994972.0  144934967-144994972   \n",
+       "2430          2431   144994973  145054851.0  144994973-145054851   \n",
+       "2431          2432   145054852          NaN           145054852-   \n",
+       "\n",
+       "     reference_time valid_time variable              level   forecast_time  \\\n",
+       "0        2025-01-01 2025-01-01     UGRD  10 m above ground     6 hour fcst   \n",
+       "1        2025-01-01 2025-01-01     UGRD  10 m above ground    12 hour fcst   \n",
+       "2        2025-01-01 2025-01-01     UGRD  10 m above ground    18 hour fcst   \n",
+       "3        2025-01-01 2025-01-01     UGRD  10 m above ground    24 hour fcst   \n",
+       "4        2025-01-01 2025-01-01     VGRD  10 m above ground     6 hour fcst   \n",
+       "...             ...        ...      ...                ...             ...   \n",
+       "2427     2025-01-01 2025-01-01     VGRD  10 m above ground  7272 hour fcst   \n",
+       "2428     2025-01-01 2025-01-01     VGRD  10 m above ground  7278 hour fcst   \n",
+       "2429     2025-01-01 2025-01-01     VGRD  10 m above ground  7284 hour fcst   \n",
+       "2430     2025-01-01 2025-01-01     VGRD  10 m above ground  7290 hour fcst   \n",
+       "2431     2025-01-01 2025-01-01     VGRD  10 m above ground  7296 hour fcst   \n",
+       "\n",
+       "                                 search_this  \n",
+       "0        :UGRD:10 m above ground:6 hour fcst  \n",
+       "1       :UGRD:10 m above ground:12 hour fcst  \n",
+       "2       :UGRD:10 m above ground:18 hour fcst  \n",
+       "3       :UGRD:10 m above ground:24 hour fcst  \n",
+       "4        :VGRD:10 m above ground:6 hour fcst  \n",
+       "...                                      ...  \n",
+       "2427  :VGRD:10 m above ground:7272 hour fcst  \n",
+       "2428  :VGRD:10 m above ground:7278 hour fcst  \n",
+       "2429  :VGRD:10 m above ground:7284 hour fcst  \n",
+       "2430  :VGRD:10 m above ground:7290 hour fcst  \n",
+       "2431  :VGRD:10 m above ground:7296 hour fcst  \n",
+       "\n",
+       "[2432 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H = Herbie(\n",
+    "    \"2025-01-01\",\n",
+    "    model=\"cfs\",\n",
+    "    product=\"time_series\",\n",
+    "    member=1,\n",
+    "    variable=\"wnd10m\",\n",
+    ")\n",
+    "\n",
+    "H.inventory()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grib_message</th>\n",
+       "      <th>start_byte</th>\n",
+       "      <th>end_byte</th>\n",
+       "      <th>range</th>\n",
+       "      <th>reference_time</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>level</th>\n",
+       "      <th>forecast_time</th>\n",
+       "      <th>search_this</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59189.0</td>\n",
+       "      <td>0-59189</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>UGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>6 hour fcst</td>\n",
+       "      <td>:UGRD:10 m above ground:6 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>235808</td>\n",
+       "      <td>295010.0</td>\n",
+       "      <td>235808-295010</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "      <td>VGRD</td>\n",
+       "      <td>10 m above ground</td>\n",
+       "      <td>6 hour fcst</td>\n",
+       "      <td>:VGRD:10 m above ground:6 hour fcst</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   grib_message  start_byte  end_byte          range reference_time  \\\n",
+       "0             1           0   59189.0        0-59189     2025-01-01   \n",
+       "4             5      235808  295010.0  235808-295010     2025-01-01   \n",
+       "\n",
+       "  valid_time variable              level forecast_time  \\\n",
+       "0 2025-01-01     UGRD  10 m above ground   6 hour fcst   \n",
+       "4 2025-01-01     VGRD  10 m above ground   6 hour fcst   \n",
+       "\n",
+       "                           search_this  \n",
+       "0  :UGRD:10 m above ground:6 hour fcst  \n",
+       "4  :VGRD:10 m above ground:6 hour fcst  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Only get 6 hour forecast\n",
+    "H.inventory(\"[U,V]GRD:10 m.*:6 hour\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 880kB\n",
+       "Dimensions:              (latitude: 190, longitude: 384)\n",
+       "Coordinates:\n",
+       "    time                 datetime64[ns] 8B 2025-01-01\n",
+       "    step                 timedelta64[ns] 8B 06:00:00\n",
+       "    heightAboveGround    float64 8B 10.0\n",
+       "  * latitude             (latitude) float64 2kB 89.28 88.34 ... -88.34 -89.28\n",
+       "  * longitude            (longitude) float64 3kB 0.0 0.9375 ... 358.1 359.1\n",
+       "    valid_time           datetime64[ns] 8B 2025-01-01T06:00:00\n",
+       "    gribfile_projection  object 8B None\n",
+       "Data variables:\n",
+       "    u10                  (latitude, longitude) float32 292kB 2.62 2.5 ... -3.2\n",
+       "    v10                  (latitude, longitude) float32 292kB -7.05 ... 1.77\n",
+       "    wind_speed           (latitude, longitude) float32 292kB 7.521 ... 3.657\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             kwbc\n",
+       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             US National Weather Service - NCEP\n",
+       "    model:                   cfs\n",
+       "    product:                 time_series\n",
+       "    description:             Climate Forecast System\n",
+       "    remote_grib:             https://noaa-cfs-pds.s3.amazonaws.com/cfs.202501...\n",
+       "    local_grib:              /home/blaylock/data/cfs/20250101/subset_6aef9f3c...\n",
+       "    search:                  [U,V]GRD:10 m.*:6 hour</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-859087cc-be8e-48be-986c-83fb76194df6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-859087cc-be8e-48be-986c-83fb76194df6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 190</li><li><span class='xr-has-index'>longitude</span>: 384</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1362fda4-dbaf-41b8-a40f-ec95346f9a3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1362fda4-dbaf-41b8-a40f-ec95346f9a3f' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-01-01</div><input id='attrs-0520c2c6-71b2-412c-b292-c3779c328f4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0520c2c6-71b2-412c-b292-c3779c328f4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48e00147-b206-4257-a36a-57c2b42eb23b' class='xr-var-data-in' type='checkbox'><label for='data-48e00147-b206-4257-a36a-57c2b42eb23b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2025-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>06:00:00</div><input id='attrs-92b38b89-fc47-43bd-84c6-712478eb8d76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92b38b89-fc47-43bd-84c6-712478eb8d76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eb38153-7bb5-4255-b445-9738d5cd2d0c' class='xr-var-data-in' type='checkbox'><label for='data-1eb38153-7bb5-4255-b445-9738d5cd2d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(21600000000000, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heightAboveGround</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-f365f1c2-54e2-442d-98be-61bbafb75683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f365f1c2-54e2-442d-98be-61bbafb75683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd4e44b7-cbcf-4ac5-bad5-380ac89676ac' class='xr-var-data-in' type='checkbox'><label for='data-fd4e44b7-cbcf-4ac5-bad5-380ac89676ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height above the surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(10.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.28 88.34 87.4 ... -88.34 -89.28</div><input id='attrs-3d69bef6-22de-464e-92fd-f94395219cbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d69bef6-22de-464e-92fd-f94395219cbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-515af879-8ac4-424b-9d5e-6e2329609cb8' class='xr-var-data-in' type='checkbox'><label for='data-515af879-8ac4-424b-9d5e-6e2329609cb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 89.276713,  88.339754,  87.397262,  86.45352 ,  85.509304,  84.564858,\n",
+       "        83.620284,  82.675631,  81.730926,  80.786186,  79.841419,  78.896633,\n",
+       "        77.951832,  77.007019,  76.062197,  75.117368,  74.172532,  73.227692,\n",
+       "        72.282847,  71.337998,  70.393147,  69.448293,  68.503436,  67.558577,\n",
+       "        66.613717,  65.668855,  64.723992,  63.779127,  62.834261,  61.889394,\n",
+       "        60.944527,  59.999658,  59.054789,  58.109919,  57.165049,  56.220177,\n",
+       "        55.275306,  54.330434,  53.385561,  52.440688,  51.495815,  50.550941,\n",
+       "        49.606067,  48.661193,  47.716318,  46.771444,  45.826569,  44.881693,\n",
+       "        43.936818,  42.991942,  42.047066,  41.10219 ,  40.157314,  39.212437,\n",
+       "        38.267561,  37.322684,  36.377807,  35.43293 ,  34.488053,  33.543176,\n",
+       "        32.598299,  31.653422,  30.708544,  29.763667,  28.818789,  27.873911,\n",
+       "        26.929034,  25.984156,  25.039278,  24.0944  ,  23.149522,  22.204644,\n",
+       "        21.259766,  20.314888,  19.370009,  18.425131,  17.480253,  16.535374,\n",
+       "        15.590496,  14.645618,  13.700739,  12.755861,  11.810982,  10.866104,\n",
+       "         9.921225,   8.976347,   8.031468,   7.08659 ,   6.141711,   5.196832,\n",
+       "         4.251954,   3.307075,   2.362197,   1.417318,   0.472439,  -0.472439,\n",
+       "        -1.417318,  -2.362197,  -3.307075,  -4.251954,  -5.196832,  -6.141711,\n",
+       "        -7.08659 ,  -8.031468,  -8.976347,  -9.921225, -10.866104, -11.810982,\n",
+       "       -12.755861, -13.700739, -14.645618, -15.590496, -16.535374, -17.480253,\n",
+       "       -18.425131, -19.370009, -20.314888, -21.259766, -22.204644, -23.149522,\n",
+       "       -24.0944  , -25.039278, -25.984156, -26.929034, -27.873911, -28.818789,\n",
+       "       -29.763667, -30.708544, -31.653422, -32.598299, -33.543176, -34.488053,\n",
+       "       -35.43293 , -36.377807, -37.322684, -38.267561, -39.212437, -40.157314,\n",
+       "       -41.10219 , -42.047066, -42.991942, -43.936818, -44.881693, -45.826569,\n",
+       "       -46.771444, -47.716318, -48.661193, -49.606067, -50.550941, -51.495815,\n",
+       "       -52.440688, -53.385561, -54.330434, -55.275306, -56.220177, -57.165049,\n",
+       "       -58.109919, -59.054789, -59.999658, -60.944527, -61.889394, -62.834261,\n",
+       "       -63.779127, -64.723992, -65.668855, -66.613717, -67.558577, -68.503436,\n",
+       "       -69.448293, -70.393147, -71.337998, -72.282847, -73.227692, -74.172532,\n",
+       "       -75.117368, -76.062197, -77.007019, -77.951832, -78.896633, -79.841419,\n",
+       "       -80.786186, -81.730926, -82.675631, -83.620284, -84.564858, -85.509304,\n",
+       "       -86.45352 , -87.397262, -88.339754, -89.276713])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-59a87dd8-9f3f-4f49-baeb-09e02c656477' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59a87dd8-9f3f-4f49-baeb-09e02c656477' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88d3ae16-d0e2-45da-ac1c-6d09a1743bc8' class='xr-var-data-in' type='checkbox'><label for='data-88d3ae16-d0e2-45da-ac1c-6d09a1743bc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.      ,   0.937499,   1.874997, ..., 357.187003, 358.124501,\n",
+       "       359.062   ], shape=(384,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-01-01T06:00:00</div><input id='attrs-167223df-b19c-4150-974c-7afa7a09f5d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-167223df-b19c-4150-974c-7afa7a09f5d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-025a2090-df67-484c-88bb-fd585fc2caec' class='xr-var-data-in' type='checkbox'><label for='data-025a2090-df67-484c-88bb-fd585fc2caec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2025-01-01T06:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-53dcc4b0-6a9b-4065-bfc0-8123b64972f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53dcc4b0-6a9b-4065-bfc0-8123b64972f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25962eee-bd23-4f1a-ab9f-09e703fdc2e3' class='xr-var-data-in' type='checkbox'><label for='data-25962eee-bd23-4f1a-ab9f-09e703fdc2e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>CFS model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9abccf59-9464-4454-af4d-0fc14c36bd53' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9abccf59-9464-4454-af4d-0fc14c36bd53' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u10</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.62 2.5 2.37 ... -3.12 -3.16 -3.2</div><input id='attrs-bd96ea8c-2a8c-4273-be46-b59ec1803bd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd96ea8c-2a8c-4273-be46-b59ec1803bd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf5ea20-57d5-4091-80a2-f8699316a515' class='xr-var-data-in' type='checkbox'><label for='data-6cf5ea20-57d5-4091-80a2-f8699316a515' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>72960</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>95</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Ni :</span></dt><dd>384</dd><dt><span>GRIB_Nj :</span></dt><dd>190</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.938</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.277</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.277</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.062</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_parameterName :</span></dt><dd>u-component of wind</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>m s-1</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_stepRange :</span></dt><dd>6</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.62,  2.5 ,  2.37, ...,  2.96,  2.85,  2.74],\n",
+       "       [ 2.87,  2.71,  2.56, ...,  3.25,  3.12,  2.99],\n",
+       "       [ 2.96,  2.78,  2.6 , ...,  3.43,  3.27,  3.12],\n",
+       "       ...,\n",
+       "       [-3.  , -3.08, -3.16, ..., -2.79, -2.86, -2.93],\n",
+       "       [-2.83, -2.88, -2.93, ..., -2.68, -2.73, -2.78],\n",
+       "       [-3.24, -3.27, -3.3 , ..., -3.12, -3.16, -3.2 ]],\n",
+       "      shape=(190, 384), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.05 -7.08 -7.11 ... 1.82 1.77</div><input id='attrs-a46b3c7d-3f0a-4604-8d63-093bf640b30b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a46b3c7d-3f0a-4604-8d63-093bf640b30b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe156da8-9047-418a-a073-261d2a3316ed' class='xr-var-data-in' type='checkbox'><label for='data-fe156da8-9047-418a-a073-261d2a3316ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>72960</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>95</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Ni :</span></dt><dd>384</dd><dt><span>GRIB_Nj :</span></dt><dd>190</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.938</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.277</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.277</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.062</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_parameterName :</span></dt><dd>v-component of wind</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>m s-1</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_stepRange :</span></dt><dd>6</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[-7.05, -7.08, -7.11, ..., -6.86, -6.92, -6.99],\n",
+       "       [-8.21, -8.24, -8.28, ..., -7.96, -8.04, -8.12],\n",
+       "       [-9.33, -9.37, -9.41, ..., -9.01, -9.11, -9.22],\n",
+       "       ...,\n",
+       "       [ 2.26,  2.18,  2.1 , ...,  2.36,  2.33,  2.29],\n",
+       "       [ 1.81,  1.74,  1.67, ...,  1.93,  1.89,  1.85],\n",
+       "       [ 1.72,  1.67,  1.61, ...,  1.86,  1.82,  1.77]],\n",
+       "      shape=(190, 384), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.521 7.508 7.495 ... 3.647 3.657</div><input id='attrs-0206fd14-6ef4-4738-bafb-6beb4dca1b8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0206fd14-6ef4-4738-bafb-6beb4dca1b8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c043e40d-2c9a-437b-9281-c2d241436036' class='xr-var-data-in' type='checkbox'><label for='data-c043e40d-2c9a-437b-9281-c2d241436036' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.521097 , 7.508422 , 7.494598 , ..., 7.471359 , 7.4839096,\n",
+       "        7.507842 ],\n",
+       "       [8.697184 , 8.674197 , 8.666718 , ..., 8.597913 , 8.624152 ,\n",
+       "        8.653005 ],\n",
+       "       [9.788284 , 9.773705 , 9.762587 , ..., 9.6407995, 9.679101 ,\n",
+       "        9.733592 ],\n",
+       "       ...,\n",
+       "       [3.7560084, 3.7734334, 3.7941532, ..., 3.6542714, 3.68897  ,\n",
+       "        3.7187364],\n",
+       "       [3.3593154, 3.364818 , 3.3725066, ..., 3.3026202, 3.3203914,\n",
+       "        3.3392963],\n",
+       "       [3.668242 , 3.671757 , 3.6717978, ..., 3.6323545, 3.6466424,\n",
+       "        3.6568978]], shape=(190, 384), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-387ea997-aba3-4570-a390-a8f2bfbc9dc0' class='xr-section-summary-in' type='checkbox'  ><label for='section-387ea997-aba3-4570-a390-a8f2bfbc9dc0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f1013a8a-665a-49c9-9a1e-cb4158f9d47d' class='xr-index-data-in' type='checkbox'/><label for='index-f1013a8a-665a-49c9-9a1e-cb4158f9d47d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 89.27671287810583,  88.33975425118209,   87.3972623773714,\n",
+       "        86.45352030735796,  85.50930400872335,  84.56485823005325,\n",
+       "        83.62028420514433,   82.6756313087777,  81.73092647110545,\n",
+       "        80.78618562235694,\n",
+       "       ...\n",
+       "       -80.78618562235694, -81.73092647110545,  -82.6756313087777,\n",
+       "       -83.62028420514433, -84.56485823005325, -85.50930400872335,\n",
+       "       -86.45352030735796,  -87.3972623773714, -88.33975425118209,\n",
+       "       -89.27671287810583],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=190))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-92131c8a-1787-439f-a34c-8319685dfabf' class='xr-index-data-in' type='checkbox'/><label for='index-92131c8a-1787-439f-a34c-8319685dfabf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0, 0.9374986945169713, 1.8749973890339426,\n",
+       "        2.812496083550914, 3.7499947780678853, 4.6874934725848565,\n",
+       "        5.624992167101828,    6.5624908616188, 7.4999895561357715,\n",
+       "        8.437488250652743,\n",
+       "       ...\n",
+       "       350.62451174934785, 351.56201044386484,  352.4995091383818,\n",
+       "        353.4370078328988,  354.3745065274158,  355.3120052219328,\n",
+       "        356.2495039164498,  357.1870026109668, 358.12450130548376,\n",
+       "                  359.062],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=384))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-601d87f2-5404-49b5-9527-d43a0726848c' class='xr-section-summary-in' type='checkbox'  ><label for='section-601d87f2-5404-49b5-9527-d43a0726848c' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>cfs</dd><dt><span>product :</span></dt><dd>time_series</dd><dt><span>description :</span></dt><dd>Climate Forecast System</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-cfs-pds.s3.amazonaws.com/cfs.20250101/00/time_grib_01/wnd10m.01.2025010100.daily.grb2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/cfs/20250101/subset_6aef9f3c__wnd10m.01.2025010100.daily.grb2</dd><dt><span>search :</span></dt><dd>[U,V]GRD:10 m.*:6 hour</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 880kB\n",
+       "Dimensions:              (latitude: 190, longitude: 384)\n",
+       "Coordinates:\n",
+       "    time                 datetime64[ns] 8B 2025-01-01\n",
+       "    step                 timedelta64[ns] 8B 06:00:00\n",
+       "    heightAboveGround    float64 8B 10.0\n",
+       "  * latitude             (latitude) float64 2kB 89.28 88.34 ... -88.34 -89.28\n",
+       "  * longitude            (longitude) float64 3kB 0.0 0.9375 ... 358.1 359.1\n",
+       "    valid_time           datetime64[ns] 8B 2025-01-01T06:00:00\n",
+       "    gribfile_projection  object 8B None\n",
+       "Data variables:\n",
+       "    u10                  (latitude, longitude) float32 292kB 2.62 2.5 ... -3.2\n",
+       "    v10                  (latitude, longitude) float32 292kB -7.05 ... 1.77\n",
+       "    wind_speed           (latitude, longitude) float32 292kB 7.521 ... 3.657\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             kwbc\n",
+       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             US National Weather Service - NCEP\n",
+       "    model:                   cfs\n",
+       "    product:                 time_series\n",
+       "    description:             Climate Forecast System\n",
+       "    remote_grib:             https://noaa-cfs-pds.s3.amazonaws.com/cfs.202501...\n",
+       "    local_grib:              /home/blaylock/data/cfs/20250101/subset_6aef9f3c...\n",
+       "    search:                  [U,V]GRD:10 m.*:6 hour"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = H.xarray(\"[U,V]GRD:10 m.*:6 hour\")\n",
+    "\n",
+    "# Calculate wind speed\n",
+    "ds[\"wind_speed\"] = (ds.u10**2 + ds.v10**2) ** 0.5\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3d33a557f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds[\"wind_speed\"].plot()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6 Hourly Product\n",
+    "\n",
+    "Herbie expects the following additional arguments for CFS 6-hourly product:\n",
+    "- `kind`, which is flxf, pgbf, ocnf, or ipvf\n",
+    "- `member`, an integer 1, 2, 3, or 4\n",
+    "- `fxx`, integer forecast lead time, in hours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'flxf': 'CFS Surface, Radiative Fluxes',\n",
+       " 'pgbf': 'CFS 3D Pressure Level, 1 degree resolution',\n",
+       " 'ocnh': 'CFS 3D Ocean Data, 0.5 degree resolution',\n",
+       " 'ocnf': 'CFS 3D Ocean Data, 1.0 degree resolution',\n",
+       " 'ipvf': 'CFS 3D Isentropic Level, 1.0 degree resolution'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What are the available product kind values?\n",
+    "# You may import the dictionary of values or refer to\n",
+    "# https://www.nco.ncep.noaa.gov/pmb/products/cfs/\n",
+    "\n",
+    "from herbie.models.cfs import product_kind\n",
+    "\n",
+    "product_kind\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/blaylock/GITHUB/Herbie/herbie/models/cfs.py:139: UserWarning: Herbie's CFS templates are and subject to major changes. PRs are welcome to improve it.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Found ┊ model=cfs ┊ \u001b[3mproduct=6_hourly\u001b[0m ┊ \u001b[38;2;41;130;13m2024-Dec-25 00:00 UTC\u001b[92m F12\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ aws\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('https://noaa-cfs-pds.s3.amazonaws.com/cfs.20241225/00/6hrly_grib_01/flxf2024122512.01.2024122500.grb2',\n",
+       " 'https://noaa-cfs-pds.s3.amazonaws.com/cfs.20241225/00/6hrly_grib_01/flxf2024122512.01.2024122500.grb2.idx')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H = Herbie(\n",
+    "    \"2024-12-25\",\n",
+    "    model=\"cfs\",\n",
+    "    product=\"6_hourly\",\n",
+    "    kind=\"flxf\",\n",
+    "    member=1,\n",
+    "    fxx=12,\n",
+    ")\n",
+    "\n",
+    "# Show the grib file and index file location\n",
+    "H.grib, H.idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grib_message</th>\n",
+       "      <th>start_byte</th>\n",
+       "      <th>end_byte</th>\n",
+       "      <th>range</th>\n",
+       "      <th>reference_time</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>level</th>\n",
+       "      <th>forecast_time</th>\n",
+       "      <th>search_this</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46418.0</td>\n",
+       "      <td>0-46418</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>UFLX</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:UFLX:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>46419</td>\n",
+       "      <td>92097.0</td>\n",
+       "      <td>46419-92097</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>VFLX</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:VFLX:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>92098</td>\n",
+       "      <td>129279.0</td>\n",
+       "      <td>92098-129279</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>SHTFL</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:SHTFL:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>129280</td>\n",
+       "      <td>169431.0</td>\n",
+       "      <td>129280-169431</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>LHTFL</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:LHTFL:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>169432</td>\n",
+       "      <td>252904.0</td>\n",
+       "      <td>169432-252904</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>TMP</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:TMP:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>97.0</td>\n",
+       "      <td>3939672</td>\n",
+       "      <td>3965411.0</td>\n",
+       "      <td>3939672-3965411</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>VBDSF</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:VBDSF:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>98.0</td>\n",
+       "      <td>3965412</td>\n",
+       "      <td>3990233.0</td>\n",
+       "      <td>3965412-3990233</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>VDDSF</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:VDDSF:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3990234</td>\n",
+       "      <td>4020806.0</td>\n",
+       "      <td>3990234-4020806</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>NBDSF</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:NBDSF:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>4020807</td>\n",
+       "      <td>4048869.0</td>\n",
+       "      <td>4020807-4048869</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>NDDSF</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:NDDSF:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>101.0</td>\n",
+       "      <td>4048870</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4048870-</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>SRWEQ</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:SRWEQ:surface:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>103 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     grib_message  start_byte   end_byte            range reference_time  \\\n",
+       "0             1.0           0    46418.0          0-46418     2024-12-25   \n",
+       "1             2.0       46419    92097.0      46419-92097     2024-12-25   \n",
+       "2             3.0       92098   129279.0     92098-129279     2024-12-25   \n",
+       "3             4.0      129280   169431.0    129280-169431     2024-12-25   \n",
+       "4             5.0      169432   252904.0    169432-252904     2024-12-25   \n",
+       "..            ...         ...        ...              ...            ...   \n",
+       "98           97.0     3939672  3965411.0  3939672-3965411     2024-12-25   \n",
+       "99           98.0     3965412  3990233.0  3965412-3990233     2024-12-25   \n",
+       "100          99.0     3990234  4020806.0  3990234-4020806     2024-12-25   \n",
+       "101         100.0     4020807  4048869.0  4020807-4048869     2024-12-25   \n",
+       "102         101.0     4048870        NaN         4048870-     2024-12-25   \n",
+       "\n",
+       "             valid_time variable    level forecast_time  \\\n",
+       "0   2024-12-25 12:00:00     UFLX  surface  12 hour fcst   \n",
+       "1   2024-12-25 12:00:00     VFLX  surface  12 hour fcst   \n",
+       "2   2024-12-25 12:00:00    SHTFL  surface  12 hour fcst   \n",
+       "3   2024-12-25 12:00:00    LHTFL  surface  12 hour fcst   \n",
+       "4   2024-12-25 12:00:00      TMP  surface  12 hour fcst   \n",
+       "..                  ...      ...      ...           ...   \n",
+       "98  2024-12-25 12:00:00    VBDSF  surface  12 hour fcst   \n",
+       "99  2024-12-25 12:00:00    VDDSF  surface  12 hour fcst   \n",
+       "100 2024-12-25 12:00:00    NBDSF  surface  12 hour fcst   \n",
+       "101 2024-12-25 12:00:00    NDDSF  surface  12 hour fcst   \n",
+       "102 2024-12-25 12:00:00    SRWEQ  surface  12 hour fcst   \n",
+       "\n",
+       "                     search_this  \n",
+       "0     :UFLX:surface:12 hour fcst  \n",
+       "1     :VFLX:surface:12 hour fcst  \n",
+       "2    :SHTFL:surface:12 hour fcst  \n",
+       "3    :LHTFL:surface:12 hour fcst  \n",
+       "4      :TMP:surface:12 hour fcst  \n",
+       "..                           ...  \n",
+       "98   :VBDSF:surface:12 hour fcst  \n",
+       "99   :VDDSF:surface:12 hour fcst  \n",
+       "100  :NBDSF:surface:12 hour fcst  \n",
+       "101  :NDDSF:surface:12 hour fcst  \n",
+       "102  :SRWEQ:surface:12 hour fcst  \n",
+       "\n",
+       "[103 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.inventory()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grib_message</th>\n",
+       "      <th>start_byte</th>\n",
+       "      <th>end_byte</th>\n",
+       "      <th>range</th>\n",
+       "      <th>reference_time</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>level</th>\n",
+       "      <th>forecast_time</th>\n",
+       "      <th>search_this</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1863350</td>\n",
+       "      <td>1941530.0</td>\n",
+       "      <td>1863350-1941530</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>2024-12-25 12:00:00</td>\n",
+       "      <td>TMP</td>\n",
+       "      <td>2 m above ground</td>\n",
+       "      <td>12 hour fcst</td>\n",
+       "      <td>:TMP:2 m above ground:12 hour fcst</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    grib_message  start_byte   end_byte            range reference_time  \\\n",
+       "37          37.0     1863350  1941530.0  1863350-1941530     2024-12-25   \n",
+       "\n",
+       "            valid_time variable             level forecast_time  \\\n",
+       "37 2024-12-25 12:00:00      TMP  2 m above ground  12 hour fcst   \n",
+       "\n",
+       "                           search_this  \n",
+       "37  :TMP:2 m above ground:12 hour fcst  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.inventory(\"TMP:2 m above\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Unhandled projection: unknown",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m ds \u001b[38;5;241m=\u001b[39m H\u001b[38;5;241m.\u001b[39mxarray(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTMP:2 m above\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m ax \u001b[38;5;241m=\u001b[39m EasyMap(crs\u001b[38;5;241m=\u001b[39m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mherbie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcrs\u001b[49m)\u001b[38;5;241m.\u001b[39max\n\u001b[1;32m      4\u001b[0m art \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mpcolormesh(ds\u001b[38;5;241m.\u001b[39mlongitude, ds\u001b[38;5;241m.\u001b[39mlatitude, ds\u001b[38;5;241m.\u001b[39mt2m, transform\u001b[38;5;241m=\u001b[39mpc)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mcolorbar(\n\u001b[1;32m      7\u001b[0m     art,\n\u001b[1;32m      8\u001b[0m     ax\u001b[38;5;241m=\u001b[39max,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m     label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean 2-m Temperature (K)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     13\u001b[0m )\n",
+      "File \u001b[0;32m~/miniforge3/envs/herbie-dev/lib/python3.13/functools.py:1039\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m   1037\u001b[0m val \u001b[38;5;241m=\u001b[39m cache\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname, _NOT_FOUND)\n\u001b[1;32m   1038\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val \u001b[38;5;129;01mis\u001b[39;00m _NOT_FOUND:\n\u001b[0;32m-> 1039\u001b[0m     val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstance\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1040\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1041\u001b[0m         cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname] \u001b[38;5;241m=\u001b[39m val\n",
+      "File \u001b[0;32m~/GITHUB/Herbie/herbie/accessors.py:150\u001b[0m, in \u001b[0;36mHerbieAccessor.crs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    147\u001b[0m variables \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(ds) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ds[i]\u001b[38;5;241m.\u001b[39mdims) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    149\u001b[0m ds \u001b[38;5;241m=\u001b[39m ds\u001b[38;5;241m.\u001b[39mmetpy\u001b[38;5;241m.\u001b[39mparse_cf(varname\u001b[38;5;241m=\u001b[39mvariables)\n\u001b[0;32m--> 150\u001b[0m crs \u001b[38;5;241m=\u001b[39m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetpy_crs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_cartopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m crs\n",
+      "File \u001b[0;32m~/miniforge3/envs/herbie-dev/lib/python3.13/site-packages/metpy/plots/mapping.py:87\u001b[0m, in \u001b[0;36mCFProjection.to_cartopy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     85\u001b[0m     proj_handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprojection_registry[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname]\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m---> 87\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnhandled projection: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m proj_handler(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs, globe)\n",
+      "\u001b[0;31mValueError\u001b[0m: Unhandled projection: unknown"
+     ]
+    }
+   ],
+   "source": [
+    "ds = H.xarray(\"TMP:2 m above\")\n",
+    "\n",
+    "ax = EasyMap(crs=ds.herbie.crs).ax\n",
+    "art = ax.pcolormesh(ds.longitude, ds.latitude, ds.t2m, transform=pc)\n",
+    "\n",
+    "plt.colorbar(\n",
+    "    art,\n",
+    "    ax=ax,\n",
+    "    shrink=0.5,\n",
+    "    pad=0.01,\n",
+    "    orientation=\"horizontal\",\n",
+    "    label=\"Mean 2-m Temperature (K)\",\n",
+    ")\n",
+    "\n",
+    "ax.set_title(f\"Initialized:{H.date:%Y-%m-%d}\\nMember:{H.member} Forecast Hour:{H.fxx}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Monthly Product\n",
+    "\n",
+    "Herbie expects the following additional arguments for CFS Monthly product:\n",
+    "- `kind`, which is flxf, pgbf, ocnh, ocnf, or ipvf\n",
+    "- `member`, an integer 1, 2, 3, or 4\n",
+    "- `month`, an integer representing the month forecast (1 through 9)\n",
+    "- `hour`, None for daily mean, or 0, 6, 12, or 18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/blaylock/GITHUB/Herbie/herbie/models/cfs.py:139: UserWarning: Herbie's CFS templates are and subject to major changes. PRs are welcome to improve it.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Found ┊ model=cfs ┊ \u001b[3mproduct=monthly_means\u001b[0m ┊ \u001b[38;2;41;130;13m2024-May-25 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ aws\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "H = Herbie(\n",
+    "    \"2024-05-25\",\n",
+    "    model=\"cfs\",\n",
+    "    product=\"monthly_means\",\n",
+    "    kind=\"pgbf\",\n",
+    "    member=1,\n",
+    "    month=1,\n",
+    "    hour=None,  # daily mean\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grib_message</th>\n",
+       "      <th>start_byte</th>\n",
+       "      <th>end_byte</th>\n",
+       "      <th>range</th>\n",
+       "      <th>reference_time</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>level</th>\n",
+       "      <th>forecast_time</th>\n",
+       "      <th>search_this</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59628.0</td>\n",
+       "      <td>0-59628</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>PRES</td>\n",
+       "      <td>mean sea level</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:PRES:mean sea level:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>59629</td>\n",
+       "      <td>81460.0</td>\n",
+       "      <td>59629-81460</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>HGT</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:HGT:1 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>81461</td>\n",
+       "      <td>100657.0</td>\n",
+       "      <td>81461-100657</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>TMP</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:TMP:1 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>100658</td>\n",
+       "      <td>104304.0</td>\n",
+       "      <td>100658-104304</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>RH</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:RH:1 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>104305</td>\n",
+       "      <td>131107.0</td>\n",
+       "      <td>104305-131107</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>SPFH</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:SPFH:1 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>473.0</td>\n",
+       "      <td>23632602</td>\n",
+       "      <td>23656841.0</td>\n",
+       "      <td>23632602-23656841</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>VPOT</td>\n",
+       "      <td>3 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:VPOT:3 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>474.0</td>\n",
+       "      <td>23656842</td>\n",
+       "      <td>23674971.0</td>\n",
+       "      <td>23656842-23674971</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>STRM</td>\n",
+       "      <td>2 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:STRM:2 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>475.0</td>\n",
+       "      <td>23674972</td>\n",
+       "      <td>23699670.0</td>\n",
+       "      <td>23674972-23699670</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>VPOT</td>\n",
+       "      <td>2 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:VPOT:2 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>476.0</td>\n",
+       "      <td>23699671</td>\n",
+       "      <td>23717971.0</td>\n",
+       "      <td>23699671-23717971</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>STRM</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:STRM:1 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>477.0</td>\n",
+       "      <td>23717972</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23717972-</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>VPOT</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>1-2 month ave fcst</td>\n",
+       "      <td>:VPOT:1 mb:1-2 month ave fcst</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>524 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     grib_message  start_byte    end_byte              range reference_time  \\\n",
+       "0             1.0           0     59628.0            0-59628     2024-05-25   \n",
+       "1             2.0       59629     81460.0        59629-81460     2024-05-25   \n",
+       "2             3.0       81461    100657.0       81461-100657     2024-05-25   \n",
+       "3             4.0      100658    104304.0      100658-104304     2024-05-25   \n",
+       "4             5.0      104305    131107.0      104305-131107     2024-05-25   \n",
+       "..            ...         ...         ...                ...            ...   \n",
+       "519         473.0    23632602  23656841.0  23632602-23656841     2024-05-25   \n",
+       "520         474.0    23656842  23674971.0  23656842-23674971     2024-05-25   \n",
+       "521         475.0    23674972  23699670.0  23674972-23699670     2024-05-25   \n",
+       "522         476.0    23699671  23717971.0  23699671-23717971     2024-05-25   \n",
+       "523         477.0    23717972         NaN          23717972-     2024-05-25   \n",
+       "\n",
+       "    valid_time variable           level       forecast_time  \\\n",
+       "0   2024-05-25     PRES  mean sea level  1-2 month ave fcst   \n",
+       "1   2024-05-25      HGT            1 mb  1-2 month ave fcst   \n",
+       "2   2024-05-25      TMP            1 mb  1-2 month ave fcst   \n",
+       "3   2024-05-25       RH            1 mb  1-2 month ave fcst   \n",
+       "4   2024-05-25     SPFH            1 mb  1-2 month ave fcst   \n",
+       "..         ...      ...             ...                 ...   \n",
+       "519 2024-05-25     VPOT            3 mb  1-2 month ave fcst   \n",
+       "520 2024-05-25     STRM            2 mb  1-2 month ave fcst   \n",
+       "521 2024-05-25     VPOT            2 mb  1-2 month ave fcst   \n",
+       "522 2024-05-25     STRM            1 mb  1-2 month ave fcst   \n",
+       "523 2024-05-25     VPOT            1 mb  1-2 month ave fcst   \n",
+       "\n",
+       "                                 search_this  \n",
+       "0    :PRES:mean sea level:1-2 month ave fcst  \n",
+       "1               :HGT:1 mb:1-2 month ave fcst  \n",
+       "2               :TMP:1 mb:1-2 month ave fcst  \n",
+       "3                :RH:1 mb:1-2 month ave fcst  \n",
+       "4              :SPFH:1 mb:1-2 month ave fcst  \n",
+       "..                                       ...  \n",
+       "519            :VPOT:3 mb:1-2 month ave fcst  \n",
+       "520            :STRM:2 mb:1-2 month ave fcst  \n",
+       "521            :VPOT:2 mb:1-2 month ave fcst  \n",
+       "522            :STRM:1 mb:1-2 month ave fcst  \n",
+       "523            :VPOT:1 mb:1-2 month ave fcst  \n",
+       "\n",
+       "[524 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.inventory()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grib_message</th>\n",
+       "      <th>start_byte</th>\n",
+       "      <th>end_byte</th>\n",
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+       "      <th>207</th>\n",
+       "      <td>187.0</td>\n",
+       "      <td>8023672</td>\n",
+       "      <td>8050166.0</td>\n",
+       "      <td>8023672-8050166</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>TMP</td>\n",
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+      "text/plain": [
+       "     grib_message  start_byte   end_byte            range reference_time  \\\n",
+       "207         187.0     8023672  8050166.0  8023672-8050166     2024-05-25   \n",
+       "\n",
+       "    valid_time variable   level       forecast_time  \\\n",
+       "207 2024-05-25      TMP  500 mb  1-2 month ave fcst   \n",
+       "\n",
+       "                        search_this  \n",
+       "207  :TMP:500 mb:1-2 month ave fcst  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.inventory(\"TMP:500\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
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+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 265kB\n",
+       "Dimensions:              (latitude: 181, longitude: 360)\n",
+       "Coordinates:\n",
+       "    time                 datetime64[ns] 8B 2024-05-25\n",
+       "    step                 timedelta64[ns] 8B 60 days\n",
+       "    isobaricInhPa        float64 8B 500.0\n",
+       "  * latitude             (latitude) float64 1kB 90.0 89.0 88.0 ... -89.0 -90.0\n",
+       "  * longitude            (longitude) float64 3kB 0.0 1.0 2.0 ... 358.0 359.0\n",
+       "    valid_time           datetime64[ns] 8B 2024-07-24\n",
+       "    gribfile_projection  object 8B None\n",
+       "Data variables:\n",
+       "    avg_t                (latitude, longitude) float32 261kB 248.5 ... 228.8\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             kwbc\n",
+       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             US National Weather Service - NCEP\n",
+       "    model:                   cfs\n",
+       "    product:                 monthly_means\n",
+       "    description:             Climate Forecast System\n",
+       "    remote_grib:             https://noaa-cfs-pds.s3.amazonaws.com/cfs.202405...\n",
+       "    local_grib:              /home/blaylock/data/cfs/20240525/subset_07ef4109...\n",
+       "    search:                  TMP:500 mb</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-45dd622e-785b-49c9-9fe8-68fa873ce592' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45dd622e-785b-49c9-9fe8-68fa873ce592' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 181</li><li><span class='xr-has-index'>longitude</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-69a07768-097b-4cbd-94dc-c605a7046d4e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69a07768-097b-4cbd-94dc-c605a7046d4e' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-05-25</div><input id='attrs-08489766-36c2-4250-9e0d-911d1194a388' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08489766-36c2-4250-9e0d-911d1194a388' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af2b69ea-59b6-452e-92de-89444c17e81f' class='xr-var-data-in' type='checkbox'><label for='data-af2b69ea-59b6-452e-92de-89444c17e81f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-05-25T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>60 days</div><input id='attrs-e75c061d-029c-49d5-822c-5ebcb617ba72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e75c061d-029c-49d5-822c-5ebcb617ba72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-721d93f9-ab2e-4e78-8d31-c00d142e1694' class='xr-var-data-in' type='checkbox'><label for='data-721d93f9-ab2e-4e78-8d31-c00d142e1694' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(5184000000000000, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isobaricInhPa</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>500.0</div><input id='attrs-68642726-3741-4535-9865-6e6c98eb1ab7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68642726-3741-4535-9865-6e6c98eb1ab7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb957979-cfe5-4efd-b2fb-6cace1b57e8a' class='xr-var-data-in' type='checkbox'><label for='data-bb957979-cfe5-4efd-b2fb-6cace1b57e8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array(500.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.0 88.0 ... -89.0 -90.0</div><input id='attrs-a4d085c1-07dd-444b-b48f-d9a1ef69bc85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4d085c1-07dd-444b-b48f-d9a1ef69bc85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3df2f7e5-6ceb-47fd-9200-a3d0211c34c4' class='xr-var-data-in' type='checkbox'><label for='data-3df2f7e5-6ceb-47fd-9200-a3d0211c34c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90.,  89.,  88.,  87.,  86.,  85.,  84.,  83.,  82.,  81.,  80.,  79.,\n",
+       "        78.,  77.,  76.,  75.,  74.,  73.,  72.,  71.,  70.,  69.,  68.,  67.,\n",
+       "        66.,  65.,  64.,  63.,  62.,  61.,  60.,  59.,  58.,  57.,  56.,  55.,\n",
+       "        54.,  53.,  52.,  51.,  50.,  49.,  48.,  47.,  46.,  45.,  44.,  43.,\n",
+       "        42.,  41.,  40.,  39.,  38.,  37.,  36.,  35.,  34.,  33.,  32.,  31.,\n",
+       "        30.,  29.,  28.,  27.,  26.,  25.,  24.,  23.,  22.,  21.,  20.,  19.,\n",
+       "        18.,  17.,  16.,  15.,  14.,  13.,  12.,  11.,  10.,   9.,   8.,   7.,\n",
+       "         6.,   5.,   4.,   3.,   2.,   1.,   0.,  -1.,  -2.,  -3.,  -4.,  -5.,\n",
+       "        -6.,  -7.,  -8.,  -9., -10., -11., -12., -13., -14., -15., -16., -17.,\n",
+       "       -18., -19., -20., -21., -22., -23., -24., -25., -26., -27., -28., -29.,\n",
+       "       -30., -31., -32., -33., -34., -35., -36., -37., -38., -39., -40., -41.,\n",
+       "       -42., -43., -44., -45., -46., -47., -48., -49., -50., -51., -52., -53.,\n",
+       "       -54., -55., -56., -57., -58., -59., -60., -61., -62., -63., -64., -65.,\n",
+       "       -66., -67., -68., -69., -70., -71., -72., -73., -74., -75., -76., -77.,\n",
+       "       -78., -79., -80., -81., -82., -83., -84., -85., -86., -87., -88., -89.,\n",
+       "       -90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-123df904-9162-4610-88de-146e8a6a81e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-123df904-9162-4610-88de-146e8a6a81e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3750187-9046-4b75-9299-e7bad4c9fb3e' class='xr-var-data-in' type='checkbox'><label for='data-d3750187-9046-4b75-9299-e7bad4c9fb3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 357., 358., 359.], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-07-24</div><input id='attrs-b5230b61-f855-4305-a7c6-e27c5dd9da97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5230b61-f855-4305-a7c6-e27c5dd9da97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5d0db37-8fc6-4867-994d-7728821ab0ef' class='xr-var-data-in' type='checkbox'><label for='data-f5d0db37-8fc6-4867-994d-7728821ab0ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-07-24T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gribfile_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-7061c93e-1013-4405-83c8-a53b54e98dc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7061c93e-1013-4405-83c8-a53b54e98dc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84c05430-bd9a-41d4-9578-c3463f3421d5' class='xr-var-data-in' type='checkbox'><label for='data-84c05430-bd9a-41d4-9578-c3463f3421d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>CFS model grid projection</dd></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48f7359f-da8c-40ce-9095-738079a89e9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48f7359f-da8c-40ce-9095-738079a89e9b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>avg_t</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>248.5 248.5 248.5 ... 228.8 228.8</div><input id='attrs-d3163e5a-0bab-4aaf-a329-eee5ad12f0b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3163e5a-0bab-4aaf-a329-eee5ad12f0b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d95a414a-b19d-4496-920b-daba0eb77d37' class='xr-var-data-in' type='checkbox'><label for='data-d95a414a-b19d-4496-920b-daba0eb77d37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>235130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>65160</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>avg</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>360</dd><dt><span>GRIB_Ny :</span></dt><dd>181</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>avg_t</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/longitude. Also called equidistant cylindrical, or Plate Carree</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Time-mean temperature</dd><dt><span>GRIB_parameterName :</span></dt><dd>Temperature</dd><dt><span>GRIB_parameterUnits :</span></dt><dd>K</dd><dt><span>GRIB_shortName :</span></dt><dd>avg_t</dd><dt><span>GRIB_stepRange :</span></dt><dd>720-1440</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Time-mean temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>grid_mapping :</span></dt><dd>gribfile_projection</dd></dl></div><div class='xr-var-data'><pre>array([[248.5 , 248.5 , 248.5 , ..., 248.5 , 248.5 , 248.5 ],\n",
+       "       [248.56, 248.57, 248.56, ..., 248.56, 248.56, 248.56],\n",
+       "       [248.74, 248.75, 248.75, ..., 248.74, 248.74, 248.74],\n",
+       "       ...,\n",
+       "       [229.57, 229.56, 229.56, ..., 229.58, 229.57, 229.57],\n",
+       "       [229.14, 229.13, 229.13, ..., 229.15, 229.14, 229.14],\n",
+       "       [228.81, 228.81, 228.81, ..., 228.81, 228.81, 228.81]],\n",
+       "      shape=(181, 360), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7dbed075-2ee0-46e3-b8e2-6c16681c60ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-7dbed075-2ee0-46e3-b8e2-6c16681c60ed' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-975bffc1-7e07-4da5-b40b-b4ca698ef66d' class='xr-index-data-in' type='checkbox'/><label for='index-975bffc1-7e07-4da5-b40b-b4ca698ef66d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 90.0,  89.0,  88.0,  87.0,  86.0,  85.0,  84.0,  83.0,  82.0,  81.0,\n",
+       "       ...\n",
+       "       -81.0, -82.0, -83.0, -84.0, -85.0, -86.0, -87.0, -88.0, -89.0, -90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=181))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f7a2939e-6e61-4a77-bd08-fd4eaf93dbdb' class='xr-index-data-in' type='checkbox'/><label for='index-f7a2939e-6e61-4a77-bd08-fd4eaf93dbdb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,\n",
+       "       ...\n",
+       "       350.0, 351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0c15f60-3c26-4ecf-98a3-e8c93036d27a' class='xr-section-summary-in' type='checkbox'  ><label for='section-b0c15f60-3c26-4ecf-98a3-e8c93036d27a' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>model :</span></dt><dd>cfs</dd><dt><span>product :</span></dt><dd>monthly_means</dd><dt><span>description :</span></dt><dd>Climate Forecast System</dd><dt><span>remote_grib :</span></dt><dd>https://noaa-cfs-pds.s3.amazonaws.com/cfs.20240525/00/monthly_grib_01/pgbf.01.2024052500.202406.avrg.grib.grb2</dd><dt><span>local_grib :</span></dt><dd>/home/blaylock/data/cfs/20240525/subset_07ef4109__pgbf.01.2024052500.202406.avrg.grib.grb2</dd><dt><span>search :</span></dt><dd>TMP:500 mb</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 265kB\n",
+       "Dimensions:              (latitude: 181, longitude: 360)\n",
+       "Coordinates:\n",
+       "    time                 datetime64[ns] 8B 2024-05-25\n",
+       "    step                 timedelta64[ns] 8B 60 days\n",
+       "    isobaricInhPa        float64 8B 500.0\n",
+       "  * latitude             (latitude) float64 1kB 90.0 89.0 88.0 ... -89.0 -90.0\n",
+       "  * longitude            (longitude) float64 3kB 0.0 1.0 2.0 ... 358.0 359.0\n",
+       "    valid_time           datetime64[ns] 8B 2024-07-24\n",
+       "    gribfile_projection  object 8B None\n",
+       "Data variables:\n",
+       "    avg_t                (latitude, longitude) float32 261kB 248.5 ... 228.8\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             kwbc\n",
+       "    GRIB_centreDescription:  US National Weather Service - NCEP\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             US National Weather Service - NCEP\n",
+       "    model:                   cfs\n",
+       "    product:                 monthly_means\n",
+       "    description:             Climate Forecast System\n",
+       "    remote_grib:             https://noaa-cfs-pds.s3.amazonaws.com/cfs.202405...\n",
+       "    local_grib:              /home/blaylock/data/cfs/20240525/subset_07ef4109...\n",
+       "    search:                  TMP:500 mb"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = H.xarray(\"TMP:500 mb\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3d29467890>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds.avg_t.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Unhandled projection: unknown",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ax \u001b[38;5;241m=\u001b[39m EasyMap(crs\u001b[38;5;241m=\u001b[39m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mherbie\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcrs\u001b[49m)\u001b[38;5;241m.\u001b[39max\n\u001b[1;32m      2\u001b[0m art \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mpcolormesh(ds\u001b[38;5;241m.\u001b[39mlongitude, ds\u001b[38;5;241m.\u001b[39mlatitude, ds\u001b[38;5;241m.\u001b[39mt, transform\u001b[38;5;241m=\u001b[39mpc)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mcolorbar(\n\u001b[1;32m      5\u001b[0m     art,\n\u001b[1;32m      6\u001b[0m     ax\u001b[38;5;241m=\u001b[39max,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m     label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean 2-m Temperature (K)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     11\u001b[0m )\n",
+      "File \u001b[0;32m~/miniforge3/envs/herbie-dev/lib/python3.13/functools.py:1039\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m   1037\u001b[0m val \u001b[38;5;241m=\u001b[39m cache\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname, _NOT_FOUND)\n\u001b[1;32m   1038\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val \u001b[38;5;129;01mis\u001b[39;00m _NOT_FOUND:\n\u001b[0;32m-> 1039\u001b[0m     val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstance\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1040\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1041\u001b[0m         cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname] \u001b[38;5;241m=\u001b[39m val\n",
+      "File \u001b[0;32m~/GITHUB/Herbie/herbie/accessors.py:150\u001b[0m, in \u001b[0;36mHerbieAccessor.crs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    147\u001b[0m variables \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(ds) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ds[i]\u001b[38;5;241m.\u001b[39mdims) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    149\u001b[0m ds \u001b[38;5;241m=\u001b[39m ds\u001b[38;5;241m.\u001b[39mmetpy\u001b[38;5;241m.\u001b[39mparse_cf(varname\u001b[38;5;241m=\u001b[39mvariables)\n\u001b[0;32m--> 150\u001b[0m crs \u001b[38;5;241m=\u001b[39m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetpy_crs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_cartopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m crs\n",
+      "File \u001b[0;32m~/miniforge3/envs/herbie-dev/lib/python3.13/site-packages/metpy/plots/mapping.py:87\u001b[0m, in \u001b[0;36mCFProjection.to_cartopy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     85\u001b[0m     proj_handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprojection_registry[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname]\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m---> 87\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnhandled projection: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m proj_handler(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs, globe)\n",
+      "\u001b[0;31mValueError\u001b[0m: Unhandled projection: unknown"
+     ]
+    }
+   ],
+   "source": [
+    "ax = EasyMap(crs=ds.herbie.crs).ax\n",
+    "art = ax.pcolormesh(ds.longitude, ds.latitude, ds.t, transform=pc)\n",
+    "\n",
+    "plt.colorbar(\n",
+    "    art,\n",
+    "    ax=ax,\n",
+    "    shrink=0.5,\n",
+    "    pad=0.01,\n",
+    "    orientation=\"horizontal\",\n",
+    "    label=\"Mean 2-m Temperature (K)\",\n",
+    ")\n",
+    "\n",
+    "ax.set_title(\n",
+    "    f\"Initialized:{H.date:%Y-%m-%d}\\nMember:{H.member}; Month Forecast:{H.month}; Hour:{H.hour}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monthly means by hour (0, 6, 12, or 18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/blaylock/GITHUB/Herbie/herbie/models/cfs.py:139: UserWarning: Herbie's CFS templates are and subject to major changes. PRs are welcome to improve it.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Found ┊ model=cfs ┊ \u001b[3mproduct=monthly_means\u001b[0m ┊ \u001b[38;2;41;130;13m2024-May-25 00:00 UTC\u001b[92m F00\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mGRIB2 @ aws\u001b[0m ┊ \u001b[38;2;255;153;0m\u001b[3mIDX @ aws\u001b[0m\n",
+      "https://noaa-cfs-pds.s3.amazonaws.com/cfs.20240525/00/monthly_grib_01/pgbf.01.2024052500.202407.avrg.grib.06Z.grb2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grib_message</th>\n",
+       "      <th>start_byte</th>\n",
+       "      <th>end_byte</th>\n",
+       "      <th>range</th>\n",
+       "      <th>reference_time</th>\n",
+       "      <th>valid_time</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>level</th>\n",
+       "      <th>forecast_time</th>\n",
+       "      <th>search_this</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61046.0</td>\n",
+       "      <td>0-61046</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>PRES</td>\n",
+       "      <td>mean sea level</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:PRES:mean sea level:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>61047</td>\n",
+       "      <td>85673.0</td>\n",
+       "      <td>61047-85673</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>HGT</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:HGT:1 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>85674</td>\n",
+       "      <td>110975.0</td>\n",
+       "      <td>85674-110975</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>TMP</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:TMP:1 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>110976</td>\n",
+       "      <td>115617.0</td>\n",
+       "      <td>110976-115617</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>RH</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:RH:1 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>115618</td>\n",
+       "      <td>148871.0</td>\n",
+       "      <td>115618-148871</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>SPFH</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:SPFH:1 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>473.0</td>\n",
+       "      <td>25462570</td>\n",
+       "      <td>25496004.0</td>\n",
+       "      <td>25462570-25496004</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>VPOT</td>\n",
+       "      <td>3 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:VPOT:3 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>474.0</td>\n",
+       "      <td>25496005</td>\n",
+       "      <td>25515605.0</td>\n",
+       "      <td>25496005-25515605</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>STRM</td>\n",
+       "      <td>2 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:STRM:2 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>475.0</td>\n",
+       "      <td>25515606</td>\n",
+       "      <td>25549621.0</td>\n",
+       "      <td>25515606-25549621</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>VPOT</td>\n",
+       "      <td>2 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:VPOT:2 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>476.0</td>\n",
+       "      <td>25549622</td>\n",
+       "      <td>25566670.0</td>\n",
+       "      <td>25549622-25566670</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>STRM</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:STRM:1 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>477.0</td>\n",
+       "      <td>25566671</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25566671-</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>2024-05-25</td>\n",
+       "      <td>VPOT</td>\n",
+       "      <td>1 mb</td>\n",
+       "      <td>2-3 month ave fcst</td>\n",
+       "      <td>:VPOT:1 mb:2-3 month ave fcst</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>524 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     grib_message  start_byte    end_byte              range reference_time  \\\n",
+       "0             1.0           0     61046.0            0-61046     2024-05-25   \n",
+       "1             2.0       61047     85673.0        61047-85673     2024-05-25   \n",
+       "2             3.0       85674    110975.0       85674-110975     2024-05-25   \n",
+       "3             4.0      110976    115617.0      110976-115617     2024-05-25   \n",
+       "4             5.0      115618    148871.0      115618-148871     2024-05-25   \n",
+       "..            ...         ...         ...                ...            ...   \n",
+       "519         473.0    25462570  25496004.0  25462570-25496004     2024-05-25   \n",
+       "520         474.0    25496005  25515605.0  25496005-25515605     2024-05-25   \n",
+       "521         475.0    25515606  25549621.0  25515606-25549621     2024-05-25   \n",
+       "522         476.0    25549622  25566670.0  25549622-25566670     2024-05-25   \n",
+       "523         477.0    25566671         NaN          25566671-     2024-05-25   \n",
+       "\n",
+       "    valid_time variable           level       forecast_time  \\\n",
+       "0   2024-05-25     PRES  mean sea level  2-3 month ave fcst   \n",
+       "1   2024-05-25      HGT            1 mb  2-3 month ave fcst   \n",
+       "2   2024-05-25      TMP            1 mb  2-3 month ave fcst   \n",
+       "3   2024-05-25       RH            1 mb  2-3 month ave fcst   \n",
+       "4   2024-05-25     SPFH            1 mb  2-3 month ave fcst   \n",
+       "..         ...      ...             ...                 ...   \n",
+       "519 2024-05-25     VPOT            3 mb  2-3 month ave fcst   \n",
+       "520 2024-05-25     STRM            2 mb  2-3 month ave fcst   \n",
+       "521 2024-05-25     VPOT            2 mb  2-3 month ave fcst   \n",
+       "522 2024-05-25     STRM            1 mb  2-3 month ave fcst   \n",
+       "523 2024-05-25     VPOT            1 mb  2-3 month ave fcst   \n",
+       "\n",
+       "                                 search_this  \n",
+       "0    :PRES:mean sea level:2-3 month ave fcst  \n",
+       "1               :HGT:1 mb:2-3 month ave fcst  \n",
+       "2               :TMP:1 mb:2-3 month ave fcst  \n",
+       "3                :RH:1 mb:2-3 month ave fcst  \n",
+       "4              :SPFH:1 mb:2-3 month ave fcst  \n",
+       "..                                       ...  \n",
+       "519            :VPOT:3 mb:2-3 month ave fcst  \n",
+       "520            :STRM:2 mb:2-3 month ave fcst  \n",
+       "521            :VPOT:2 mb:2-3 month ave fcst  \n",
+       "522            :STRM:1 mb:2-3 month ave fcst  \n",
+       "523            :VPOT:1 mb:2-3 month ave fcst  \n",
+       "\n",
+       "[524 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H = Herbie(\n",
+    "    \"2024-05-25\",\n",
+    "    model=\"cfs\",\n",
+    "    product=\"monthly_means\",\n",
+    "    kind=\"pgbf\",\n",
+    "    member=1,\n",
+    "    month=2,\n",
+    "    hour=6,  # 2-3 month forecast average for 06z\n",
+    ")\n",
+    "print(H.grib)\n",
+    "H.inventory()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "herbie-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/environment-dev.yml b/environment-dev.yml
index 87e040fa..8279dd91 100644
--- a/environment-dev.yml
+++ b/environment-dev.yml
@@ -4,51 +4,46 @@ name: herbie-dev
 channels:
   - conda-forge
 dependencies:
-  - python>=3.11
-  - pip
-  - git
   - curl
-  - rclone # Not required, but is an awesome alternative (https://rclone.org/)
-  - wgrib2 # ONLY AVAILABLE ON LINUX. Comment if not using Linux.
+  - eccodes>=2.31
+  - git
+  - pip
+  - python>=3.13
+  - rclone
+  - uv
+  - wgrib2 # Linux only
 
-  # ===========
-  # Handy Tools
-  # ===========
+  # ============
+  # Dependencies
+  # ============
   - cartopy>=0.22
   - cfgrib>=0.9.10.4
-  - eccodes>=2.31
   - geopandas
   - h5py
   - jupyter
-  - jupyterlab
   - matplotlib>=3.4.3
   - metpy
   - netcdf4
   - numpy>=1.25
   - pandas>=2.0
-  - polars>=1.0
+  - polars>=1.17.1
   - pygrib>=2.1.4
   - pylint
   - pyproj>=3.6
   - requests>=2.31
   - s3fs
-  - scipy
+  - scipy>=1.15.1
   - scikit-learn
   - toml
   - xarray>=2023.7
   - zarr
 
-  # ===================
-  # Formatter & Testing
-  # ===================
+  # ===========
+  # Development
+  # ===========
   - ruff
-  - black
-  - black-jupyter
-  - isort
-  - pylint
   - pytest
   - pytest-cov
-  - line_profiler
 
   # =============
   # Documentation
@@ -56,37 +51,26 @@ dependencies:
   - sphinx>=4.4.0
   - nbsphinx
   - nbconvert>=6.5
-  - pydata-sphinx-theme # PyData Sphinx Theme (i.e, Numpy, Pandas, MetPy)
+  - pydata-sphinx-theme
   - sphinx-design
   - recommonmark
   - sphinx-markdown-tables
-  - sphinxcontrib-mermaid # For mermaid diagram support
+  - sphinxcontrib-mermaid
   - autodocsumm
-  - sphinx-autosummary-accessors # pandas and xarray accessor docs
+  - sphinx-autosummary-accessors
   - latexmk
   - myst-parser
   - linkify-it-py
 
-  #- pip:
-      #- cartopy>=0.22
-  # Herbie: Development version from GitHub
-  #- git+https://github.com/blaylockbk/Herbie.git
-
-  # Install a specific branch
-  #- git+https://github.com/blaylockbk/Herbie.git@blaylockbk/issue98
-
-  # Borrow some tools from my garage
-  #- git+https://github.com/blaylockbk/Carpenter_Workshop.git
-
-  # Create idx files on the fly!!  -- Waiting for package maturity
-  #- git+https://github.com/joxtoby/beltzer.git
 # =============================================================================
-#                             Extra Setup Steps
+#                                Setup Steps
 # =============================================================================
-# conda activate herbie-dev
+# mamba activate herbie-dev
+# git clone https://github.com/blaylockbk/Herbie.git
 # cd Herbie
 # pip install -e .
 #
+# cd ~
 # git clone https://github.com/blaylockbk/Carpenter_Workshop.git
 # cd Carpenter_Workshop
 # pi install -e .
diff --git a/herbie/core.py b/herbie/core.py
index 710f7c9f..d8ae9996 100644
--- a/herbie/core.py
+++ b/herbie/core.py
@@ -345,9 +345,9 @@ def _validate(self) -> None:
         _models = {m for m in dir(model_templates) if not m.startswith("__")}
         _products = set(self.PRODUCTS)
 
-        assert self.date < pd.Timestamp.utcnow().tz_localize(
-            None
-        ), "🔮 `date` cannot be in the future."
+        assert self.date < pd.Timestamp.utcnow().tz_localize(None), (
+            "🔮 `date` cannot be in the future."
+        )
         assert self.model in _models, f"`model` must be one of {_models}"
         assert self.product in _products, f"`product` must be one of {_products}"
 
@@ -910,7 +910,7 @@ def subset(search, outFile):
                 grib_source = f"file://{str(self.grib)}"
             if verbose:
                 print(
-                    f'📇 Download subset: {self.__repr__()}{" ":60s}\n cURL from {grib_source}'
+                    f"📇 Download subset: {self.__repr__()}{' ':60s}\n cURL from {grib_source}"
                 )
 
             # -----------------------------------------------------
@@ -1142,16 +1142,26 @@ def xarray(
         # Get CF grid projection information with pygrib and pyproj because
         # this is something cfgrib doesn't do (https://github.com/ecmwf/cfgrib/issues/251)
         # NOTE: Assumes the projection is the same for all variables
-        with pygrib.open(str(local_file)) as grb:
-            msg = grb.message(1)
-            cf_params = CRS(msg.projparams).to_cf()
-
-        # Funny stuff with polar stereographic (https://github.com/pyproj4/pyproj/issues/856)
-        # TODO: Is there a better way to handle this? What about south pole?
-        if cf_params["grid_mapping_name"] == "polar_stereographic":
-            cf_params["latitude_of_projection_origin"] = cf_params.get(
-                "latitude_of_projection_origin", 90
-            )
+        # TODO: Issues with pygrib in tests. Segmentation Fault. Is it Numpy 2???
+        use_pygrib = False
+        if use_pygrib:
+            with pygrib.open(str(local_file)) as grb:
+               msg = grb.message(1)
+               cf_params = CRS(msg.projparams).to_cf()
+
+            #grb = pygrib.open(str(local_file))
+            #msg = grb.message(1)
+            #cf_params = CRS(msg.projparams).to_cf()
+            #grb.close()
+
+            # Funny stuff with polar stereographic (https://github.com/pyproj4/pyproj/issues/856)
+            # TODO: Is there a better way to handle this? What about south pole?
+            if cf_params["grid_mapping_name"] == "polar_stereographic":
+                cf_params["latitude_of_projection_origin"] = cf_params.get(
+                    "latitude_of_projection_origin", 90
+                )
+        else:
+            cf_params = {}
 
         # Here I'm looping over each dataset in the list returned by cfgrib
         for ds in Hxr:
diff --git a/herbie/models/__init__.py b/herbie/models/__init__.py
index 877c0901..c3f1f1cf 100644
--- a/herbie/models/__init__.py
+++ b/herbie/models/__init__.py
@@ -35,6 +35,7 @@
 from .rtma import *
 from .usnavy import *
 from .urma import *
+from .cfs import *
 
 # ======================================================================
 #                     Import Private Model Templates
diff --git a/herbie/models/cfs.py b/herbie/models/cfs.py
new file mode 100644
index 00000000..6fa4ea90
--- /dev/null
+++ b/herbie/models/cfs.py
@@ -0,0 +1,259 @@
+## Added by Brian Blaylock
+## January 8, 2025
+
+"""A Herbie template for the CFS (Climate Forecast System).
+
+Details at https://cfs.ncep.noaa.gov/
+"""
+
+from pandas import to_datetime, Timedelta
+
+import warnings
+
+
+time_series_variables = {
+    # -----------------------
+    # 3-D Pressure Level Data
+    # -----------------------
+    # Wind
+    "wnd10m": "Wind u and v at 10 m",
+    "wnd1000": "Wind u and v at 1000 hPa",
+    "wnd925": "Wind u and v at 925 hPa",
+    "wnd850": "Wind u and v at 850 hPa",
+    "wnd500": "Wind u and v at 500 hPa",
+    "wnd700": "Wind u and v at 700 hPa",
+    "wnd250": "Wind u and v at 250 hPa",
+    "wnd200": "Wind u and v at 200 hPa",
+    "wndstrs": "Stress at surface",
+    # Pressure
+    "prmsl": "Pressure Reduced to MSL",
+    "pressfc": "Surface Pressure",
+    # Geopotential Height
+    "z1000": "GeoPotential Height at 1000 hPa",
+    "z850": "GeoPotential Height at 850 hPa",
+    "z700": "GeoPotential Height at 700 hPa",
+    "z500": "GeoPotential Height at 500 hPa",
+    "z200": "GeoPotential Height at 200 hPa",
+    # Temperature
+    "tmpsfc": "Temperature at the Surface",
+    "tmp2m": "Temperature at 2 m",
+    "tmin": "Minimum Temperature at 2 m",
+    "tmax": "Maximum Temperature at 2 m",
+    "t1000": "Temperature at 1000 hPa",
+    "t850": "Temperature at 850 hPa",
+    "t700": "Temperature at 700 hPa",
+    "t500": "Temperature at 500 hPa",
+    "t250": "Temperature at 250 hPa",
+    "t200": "Temperature at 200 hPa",
+    "t50": "Temperature at 50 hPa",
+    "t2": "Temperature at 2 hPa",
+    "tmphy1": "Temperature at hybrid level 1",
+    # Specific Humidity
+    "q2m": "Specific Humidity at 2 m",
+    "q925": "Specific Humidity at 925 hPa",
+    "q850": "Specific Humidity at 850 hPa",
+    "q700": "Specific Humidity at 700 hPa",
+    "q500": "Specific Humidity at 500 hPa",
+    # Velocity Potential
+    "chi850": "Velocity Potential at 850 hPa",
+    "chi200": "Velocity Potential at 200 hPa",
+    # Radiation
+    "dlwsfc": "Downward Longwave Radiation at the Surface",
+    "dswsfc": "Downward Shortwave Radiation at the Surface",
+    "ulwsfc": "Upward Longwave Radiation at the Surface",
+    "uswsfc": "Upward Shortwave Radiation at the Surface",
+    "ulwtoa": "Upward Longwave Radiation at the Top of the Atmosphere",
+    "uswtoa": "Upward Shortwave Radiation at the Top of the Atmosphere",
+    "gflux": "Ground Heat Flux",
+    # Heat Flux
+    "lhtfl": "Latent Heat Net Flux at the Surface",
+    "shtfl": "Sensible Heat Net Flux at the Surface",
+    # Other
+    "cprat": "Convective Precipitation Rate",
+    "csdlf": "Clear Sky Downward Longwave Flux",
+    "csdsf": "Clear Sky Downward Solar Flux",
+    "csusf": "Clear Sky Upward Solar Flux",
+    "icecon": "Surface Ice Cover",
+    "icethk": "Surface Ice Thickness",
+    "ipv450": "Isentropic Potential Vorticity at 450 K",
+    "ipv550": "Isentropic Potential Vorticity at 550 K",
+    "ipv650": "Isentropic Potential Vorticity at 650 K",
+    "nddsf": "Near IR Diffuse Downward Solar Flux",
+    "tcdcclm": "Total Cloud Cover",
+    "prate": "Total Precipitation Rate",
+    "psi200": "Stream Function at 200 hPa",
+    "psi850": "Stream Function at 850 hPa",
+    "pwat": "Precipitable Water",
+    "runoff": "Surface Runoff",
+    "snohf": "Snowfall Rate Water Equivalent",
+    "soilm1": "Vol. Soil Moisture Content, 0-10 cm Below Ground",
+    "soilm2": "Vol. Soil Moisture Content, 10-40 cm Below Ground",
+    "soilm3": "Vol. Soil Moisture Content, 40-100 cm Below Ground",
+    "soilm4": "Vol. Soil Moisture Content, 100-200 cm Below Ground",
+    "soilt1": "Soil Temperature, 0-0.1 m Below Ground",
+    "vddsf": "Visible Diffuse Downward Solar Flux",
+    "vvel500": "Vertical Velocity at 500 hPa",
+    "weasd": "Water Equivalent of Accumulated Snow Depth",
+    # --------------
+    # 3-D Ocean Data
+    # --------------
+    # CFS Ocean Height
+    "ocnmld": "Mixed Layer Depth",
+    "ocnslh": "Sea Level Height",
+    # CFS Ocean Depth of Isotherm
+    "ocnsild": "Surface Isothermal Layer Depth",
+    "ocndt2.5c": "Isothermal Layer Depth 2.5 Celsius",
+    "ocndt5c": "Isothermal Layer Depth 5 Celsius",
+    "ocndt10c": "Isothermal Layer Depth 10 Celsius",
+    "ocndt15c": "Isothermal Layer Depth 15 Celsius",
+    "ocndt20c": "Isothermal Layer Depth 20 Celsius",
+    "ocndt25c": "Isothermal Layer Depth 25 Celsius",
+    "ocndt28c": "Isothermal Layer Depth 28 Celsius",
+    # CFS Ocean Temperature
+    "ocnsst": "Temperature at depth 5 meters",
+    "ocnt15": "Potential temperature 15 meters below",
+    "ocnheat": "Heat Content",
+    "ocntchp": "Tropical Cyclone Heat Potential",
+    # CFS Ocean Salinity
+    "ocnsal5": "Salinity at depth 5 meters",
+    "ocnsal15": "Salinity at depth 15 meters",
+    # CFS Ocean Currents
+    "ocnu5": "Zonal Current at depth 5 meters",
+    "ocnu15": "Zonal Current at depth 15 meters",
+    "ocnv5": "Meridional Current at depth 5 meters",
+    "ocnv15": "Meridional Current at depth 15 meters",
+    "ocnvv55": "Vertical Velocity at depth 55 meters",
+}
+
+product_kind = {
+    "flxf": "CFS Surface, Radiative Fluxes",
+    "pgbf": "CFS 3D Pressure Level, 1 degree resolution",
+    "ocnh": "CFS 3D Ocean Data, 0.5 degree resolution",
+    "ocnf": "CFS 3D Ocean Data, 1.0 degree resolution",
+    "ipvf": "CFS 3D Isentropic Level, 1.0 degree resolution",
+}
+
+
+class cfs:
+    def template(self):
+        warnings.warn(
+            "Herbie's CFS templates are and subject to major changes. PRs are welcome to improve it."
+        )
+
+        self.DESCRIPTION = "Climate Forecast System"
+        self.DETAILS = {
+            "NOMADS product description": "https://www.nco.ncep.noaa.gov/pmb/products/cfs/",
+            "Amazon Open Data": "https://registry.opendata.aws/noaa-cfs/",
+            "Microsoft Azure": "https://planetarycomputer.microsoft.com/dataset/storage/noaa-cfs",
+            "NCEI": "https://www.ncei.noaa.gov/products/weather-climate-models/climate-forecast-system",
+        }
+
+        # For reference:
+        # https://www.nco.ncep.noaa.gov/pmb/products/cfs/
+        # filename.member.initialdate.verificationmonth.avrg.grib.cycle.grb2
+        self.PRODUCTS = {
+            "time_series": "CFS time series products",
+            "6_hourly": "CFS 6 hourly products",
+            "monthly_means": "CFS monthly products",
+        }
+
+        # All products require a member
+        try:
+            self.member
+        except AttributeError:
+            warnings.warn(
+                "'member' is not defined. Expected `member=x` where x is 1, 2, 3, or 4. Setting to 1"
+            )
+            self.member = 1
+
+        if self.product == "time_series":
+            try:
+                self.variable
+            except AttributeError:
+                warnings.warn(
+                    "'variable' is not defined. Expected `variable='name'` where 'name' is one of the time series variables."
+                )
+
+            if self.variable != time_series_variables.keys():
+                warnings.warn(
+                    f"Variable {self.variable} is not in the list of available time series variables. Expected one of {time_series_variables}"
+                )
+
+            post_root = f"cfs.{self.date:%Y%m%d/%H}/time_grib_{self.member:02d}/{self.variable}.{self.member:02d}.{self.date:%Y%m%d%H}.daily.grb2"
+
+            self.SOURCES = {
+                "aws": f"https://noaa-cfs-pds.s3.amazonaws.com/{post_root}",
+                "nomads": f"https://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/{post_root}",
+                # "azure": f"https://noaacfs.blob.core.windows.net/cfs/{PATH}"
+            }
+
+        elif self.product == "6_hourly":
+            try:
+                self.kind
+            except AttributeError:
+                warnings.warn(
+                    f"'kind' is not defined. Expected `kind='x'` where 'x' is one of the product types {product_kind.keys()}. Default to `kind='pgbf'`"
+                )
+                self.kind = "pgbf"
+
+            if self.kind == "ocnh":
+                raise ValueError(
+                    "kind='ocnh' is not a valid 6 hourly product available. https://www.nco.ncep.noaa.gov/pmb/products/cfs/#6HRLY"
+                )
+
+            valid_date = to_datetime(self.date) + Timedelta(hours=self.fxx)
+
+            post_root = f"cfs.{self.date:%Y%m%d/%H}/6hrly_grib_{self.member:02d}/{self.kind}{valid_date:%Y%m%d%H}.{self.member:02d}.{self.date:%Y%m%d%H}.grb2"
+
+            self.SOURCES = {
+                "aws": f"https://noaa-cfs-pds.s3.amazonaws.com/{post_root}",
+                "nomads": f"https://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/{post_root}",
+                # "azure": f"https://noaacfs.blob.core.windows.net/cfs/{post_root}"
+            }
+
+        elif self.product == "monthly_means":
+            try:
+                self.kind
+            except Exception as e:
+                warnings.warn(
+                    f"'kind' is not defined. Expected `kind='x'` where 'x' is one of the product types {product_kind.keys()}. Default to `kind='pgbf'`"
+                )
+                self.kind = "pgbf"
+
+            try:
+                self.month
+            except Exception as e:
+                raise AttributeError(
+                    f"{e} Herbie expects an argument 'month' to be set for model='cfs', product='monthly_means'."
+                )
+
+            try:
+                self.hour
+            except Exception as e:
+                warnings.warn(
+                    "'hour' is not defined. Please set `hour` to one of {0, 6, 12, 18, None}. Defaulting to None for daily average."
+                )
+                self.hour = None
+
+            # TODO: My logic might not always be correct here, or is it ok?
+            valid_month = to_datetime(self.date) + Timedelta(days=30 * self.month - 1)
+
+            if self.hour is None:
+                # Daily average
+                post_root = f"cfs.{self.date:%Y%m%d/%H}/monthly_grib_{self.member:02d}/{self.kind}.{self.member:02d}.{self.date:%Y%m%d%H}.{valid_month:%Y%m}.avrg.grib.grb2"
+            else:
+                post_root = f"cfs.{self.date:%Y%m%d/%H}/monthly_grib_{self.member:02d}/{self.kind}.{self.member:02d}.{self.date:%Y%m%d%H}.{valid_month:%Y%m}.avrg.grib.{self.hour:02d}Z.grb2"
+
+            self.SOURCES = {
+                "aws": f"https://noaa-cfs-pds.s3.amazonaws.com/{post_root}",
+                "nomads": f"https://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/{post_root}",
+                # "azure": f"https://noaacfs.blob.core.windows.net/cfs/{post_root}"
+            }
+
+        else:
+            raise NotImplementedError(
+                f"{self.product} is not a valid product. Must be one of {self.PRODUCTS.keys()}"
+            )
+
+        self.IDX_SUFFIX = [".grb2.idx", ".idx", ".grib.idx"]
+        self.LOCALFILE = f"{self.get_remoteFileName}"
diff --git a/pyproject.toml b/pyproject.toml
index 68aefe57..3517d18f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,7 +2,7 @@
 name = "herbie-data"
 description = "Download numerical weather prediction GRIB2 model data."
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = { file = "LICENSE" }
 authors = [{ name = "Brian K. Blaylock", email = "blaylockbk@gmail.com" }]
 maintainers = [{ name = "Brian K. Blaylock", email = "blaylockbk@gmail.com" }]
@@ -30,13 +30,13 @@ keywords = [
     "HRRR",
 ]
 dependencies = [
-    "cfgrib",
-    "numpy",
-    "pandas",
-    "pygrib",
-    "requests",
-    "toml",
-    "xarray",
+    "cfgrib>=0.9.15",
+    "numpy>=2.2.1",
+    "pandas>=2.2.3",
+    "pygrib==2.1.6",
+    "requests>=2.23.3",
+    "toml>=0.10.2",
+    "xarray>=2025.1.1",
 ]
 dynamic = ["version"]
 
@@ -92,9 +92,6 @@ extend-select = [
 [tool.ruff.lint.pydocstyle]
 convention = "numpy"
 
-[tool.isort]
-profile = "black"
-
 [tool.pytest.ini_options]
 minversion = "2.0"
 addopts = "-rsfEX -p pytester --strict-markers --verbosity=3 --cov=herbie"
diff --git a/references/cfs_filenames.ipynb b/references/cfs_filenames.ipynb
new file mode 100644
index 00000000..511e2b20
--- /dev/null
+++ b/references/cfs_filenames.ipynb
@@ -0,0 +1,556 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unique filenames for Climate Forecast System (CFS)\n",
+    "\n",
+    "[NCEP Description](https://www.nco.ncep.noaa.gov/pmb/products/cfs/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (91, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>filename</th></tr><tr><td>str</td></tr></thead><tbody><tr><td>&quot;chi200.01.2024122906.daily.grb…</td></tr><tr><td>&quot;chi850.01.2024122906.daily.grb…</td></tr><tr><td>&quot;cprat.01.2024122906.daily.grb2&quot;</td></tr><tr><td>&quot;csdlf.01.2024122906.daily.grb2&quot;</td></tr><tr><td>&quot;csdsf.01.2024122906.daily.grb2&quot;</td></tr><tr><td>&hellip;</td></tr><tr><td>&quot;z200.01.2024122906.daily.grb2&quot;</td></tr><tr><td>&quot;z500.01.2024122906.daily.grb2&quot;</td></tr><tr><td>&quot;z700.01.2024122906.daily.grb2&quot;</td></tr><tr><td>&quot;z850.01.2024122906.daily.grb2&quot;</td></tr><tr><td>&quot;z1000.01.2024122906.daily.grb2&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (91, 1)\n",
+       "┌─────────────────────────────────┐\n",
+       "│ filename                        │\n",
+       "│ ---                             │\n",
+       "│ str                             │\n",
+       "╞═════════════════════════════════╡\n",
+       "│ chi200.01.2024122906.daily.grb… │\n",
+       "│ chi850.01.2024122906.daily.grb… │\n",
+       "│ cprat.01.2024122906.daily.grb2  │\n",
+       "│ csdlf.01.2024122906.daily.grb2  │\n",
+       "│ csdsf.01.2024122906.daily.grb2  │\n",
+       "│ …                               │\n",
+       "│ z200.01.2024122906.daily.grb2   │\n",
+       "│ z500.01.2024122906.daily.grb2   │\n",
+       "│ z700.01.2024122906.daily.grb2   │\n",
+       "│ z850.01.2024122906.daily.grb2   │\n",
+       "│ z1000.01.2024122906.daily.grb2  │\n",
+       "└─────────────────────────────────┘"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import polars as pl\n",
+    "\n",
+    "# URL of the web directory\n",
+    "url = \"https://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/cfs.20241229/06/time_grib_01/\"\n",
+    "\n",
+    "# Step 1: Fetch the directory listing\n",
+    "response = requests.get(url)\n",
+    "response.raise_for_status()  # Raise an error if the request failed\n",
+    "\n",
+    "# Step 2: Parse the HTML content\n",
+    "soup = BeautifulSoup(response.text)\n",
+    "\n",
+    "# Step 3: Extract file names and additional metadata if available\n",
+    "files = []\n",
+    "for link in soup.find_all(\"a\"):\n",
+    "    href = link.get(\"href\")\n",
+    "    if href and not href.startswith(\"?\") and not href.endswith(\"/\"):\n",
+    "        files.append({\"filename\": href})\n",
+    "\n",
+    "# Step 4: Convert to a Polars DataFrame\n",
+    "df = pl.DataFrame(files)\n",
+    "\n",
+    "# Only grib files (not index files)\n",
+    "df = df.filter(pl.col(\"filename\").str.ends_with(\".grb2\"))\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (91, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>a</th><th>b</th><th>c</th><th>d</th><th>e</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;chi200&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;chi850&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;cprat&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;csdlf&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;csdsf&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;z200&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;z500&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;z700&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;z850&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;z1000&quot;</td><td>&quot;01&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;daily&quot;</td><td>&quot;grb2&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (91, 5)\n",
+       "┌────────┬─────┬────────────┬───────┬──────┐\n",
+       "│ a      ┆ b   ┆ c          ┆ d     ┆ e    │\n",
+       "│ ---    ┆ --- ┆ ---        ┆ ---   ┆ ---  │\n",
+       "│ str    ┆ str ┆ str        ┆ str   ┆ str  │\n",
+       "╞════════╪═════╪════════════╪═══════╪══════╡\n",
+       "│ chi200 ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ chi850 ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ cprat  ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ csdlf  ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ csdsf  ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ …      ┆ …   ┆ …          ┆ …     ┆ …    │\n",
+       "│ z200   ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ z500   ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ z700   ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ z850   ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "│ z1000  ┆ 01  ┆ 2024122906 ┆ daily ┆ grb2 │\n",
+       "└────────┴─────┴────────────┴───────┴──────┘"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = df.select(pl.col(\"filename\").str.split(\".\")).select(\n",
+    "    a=pl.col(\"filename\")\n",
+    "    .list.slice(0, pl.col(\"filename\").list.len() - 4)\n",
+    "    .list.join(\".\"),\n",
+    "    b=pl.col(\"filename\").list.get(-4),\n",
+    "    c=pl.col(\"filename\").list.get(-3),\n",
+    "    d=pl.col(\"filename\").list.get(-2),\n",
+    "    e=pl.col(\"filename\").list.get(-1),\n",
+    ")\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a ['chi200' 'chi850' 'cprat' 'csdlf' 'csdsf' 'csusf' 'dlwsfc' 'dswsfc'\n",
+      " 'gflux' 'icecon' 'icethk' 'ipv450' 'ipv550' 'ipv650' 'lhtfl' 'nddsf'\n",
+      " 'ocndt10c' 'ocndt15c' 'ocndt2.5c' 'ocndt20c' 'ocndt25c' 'ocndt28c'\n",
+      " 'ocndt5c' 'ocnheat' 'ocnmld' 'ocnsal15' 'ocnsal5' 'ocnsild' 'ocnslh'\n",
+      " 'ocnsst' 'ocnt15' 'ocntchp' 'ocnu15' 'ocnu5' 'ocnv15' 'ocnv5' 'ocnvv55'\n",
+      " 'prate' 'pressfc' 'prmsl' 'psi200' 'psi850' 'pwat' 'q2m' 'q500' 'q700'\n",
+      " 'q850' 'q925' 'runoff' 'shtfl' 'snohf' 'soilm1' 'soilm2' 'soilm3'\n",
+      " 'soilm4' 'soilt1' 't1000' 't2' 't200' 't250' 't50' 't500' 't700' 't850'\n",
+      " 'tcdcclm' 'tmax' 'tmin' 'tmp2m' 'tmphy1' 'tmpsfc' 'ulwsfc' 'ulwtoa'\n",
+      " 'uswsfc' 'uswtoa' 'vddsf' 'vvel500' 'weasd' 'wnd1000' 'wnd10m' 'wnd200'\n",
+      " 'wnd250' 'wnd500' 'wnd700' 'wnd850' 'wnd925' 'wndstrs' 'z1000' 'z200'\n",
+      " 'z500' 'z700' 'z850']\n",
+      "b ['01']\n",
+      "c ['2024122906']\n",
+      "d ['daily']\n",
+      "e ['grb2']\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in a.columns:\n",
+    "    print(i, a[i].unique().sort().to_numpy())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6 hour mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (726, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>filename</th></tr><tr><td>str</td></tr></thead><tbody><tr><td>&quot;flxf2024122906.02.2024122906.g…</td></tr><tr><td>&quot;flxf2024122912.02.2024122906.g…</td></tr><tr><td>&quot;flxf2024122918.02.2024122906.g…</td></tr><tr><td>&quot;flxf2024123000.02.2024122906.g…</td></tr><tr><td>&quot;flxf2024123006.02.2024122906.g…</td></tr><tr><td>&hellip;</td></tr><tr><td>&quot;pgbf2025021112.02.2024122906.g…</td></tr><tr><td>&quot;pgbf2025021118.02.2024122906.g…</td></tr><tr><td>&quot;pgbf2025021200.02.2024122906.g…</td></tr><tr><td>&quot;pgbf2025021206.02.2024122906.g…</td></tr><tr><td>&quot;splanl.02.2024122906.grb2&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (726, 1)\n",
+       "┌─────────────────────────────────┐\n",
+       "│ filename                        │\n",
+       "│ ---                             │\n",
+       "│ str                             │\n",
+       "╞═════════════════════════════════╡\n",
+       "│ flxf2024122906.02.2024122906.g… │\n",
+       "│ flxf2024122912.02.2024122906.g… │\n",
+       "│ flxf2024122918.02.2024122906.g… │\n",
+       "│ flxf2024123000.02.2024122906.g… │\n",
+       "│ flxf2024123006.02.2024122906.g… │\n",
+       "│ …                               │\n",
+       "│ pgbf2025021112.02.2024122906.g… │\n",
+       "│ pgbf2025021118.02.2024122906.g… │\n",
+       "│ pgbf2025021200.02.2024122906.g… │\n",
+       "│ pgbf2025021206.02.2024122906.g… │\n",
+       "│ splanl.02.2024122906.grb2       │\n",
+       "└─────────────────────────────────┘"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import polars as pl\n",
+    "\n",
+    "# URL of the web directory\n",
+    "url = \"https://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/cfs.20241229/06/6hrly_grib_02/\"\n",
+    "\n",
+    "# Step 1: Fetch the directory listing\n",
+    "response = requests.get(url)\n",
+    "response.raise_for_status()  # Raise an error if the request failed\n",
+    "\n",
+    "# Step 2: Parse the HTML content\n",
+    "soup = BeautifulSoup(response.text)\n",
+    "\n",
+    "# Step 3: Extract file names and additional metadata if available\n",
+    "files = []\n",
+    "for link in soup.find_all(\"a\"):\n",
+    "    href = link.get(\"href\")\n",
+    "    if href and not href.startswith(\"?\") and not href.endswith(\"/\"):\n",
+    "        files.append({\"filename\": href})\n",
+    "\n",
+    "# Step 4: Convert to a Polars DataFrame\n",
+    "df = pl.DataFrame(files)\n",
+    "\n",
+    "# Only grib files (not index files)\n",
+    "df = df.filter(pl.col(\"filename\").str.ends_with(\".grb2\"))\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (726, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>a</th><th>b</th><th>c</th><th>d</th><th>e</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;&quot;</td><td>&quot;flxf2024122906&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;flxf2024122912&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;flxf2024122918&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;flxf2024123000&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;flxf2024123006&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;&quot;</td><td>&quot;pgbf2025021112&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;pgbf2025021118&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;pgbf2025021200&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;pgbf2025021206&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr><tr><td>&quot;&quot;</td><td>&quot;splanl&quot;</td><td>&quot;02&quot;</td><td>&quot;2024122906&quot;</td><td>&quot;grb2&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (726, 5)\n",
+       "┌─────┬────────────────┬─────┬────────────┬──────┐\n",
+       "│ a   ┆ b              ┆ c   ┆ d          ┆ e    │\n",
+       "│ --- ┆ ---            ┆ --- ┆ ---        ┆ ---  │\n",
+       "│ str ┆ str            ┆ str ┆ str        ┆ str  │\n",
+       "╞═════╪════════════════╪═════╪════════════╪══════╡\n",
+       "│     ┆ flxf2024122906 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ flxf2024122912 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ flxf2024122918 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ flxf2024123000 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ flxf2024123006 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│ …   ┆ …              ┆ …   ┆ …          ┆ …    │\n",
+       "│     ┆ pgbf2025021112 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ pgbf2025021118 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ pgbf2025021200 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ pgbf2025021206 ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "│     ┆ splanl         ┆ 02  ┆ 2024122906 ┆ grb2 │\n",
+       "└─────┴────────────────┴─────┴────────────┴──────┘"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = df.select(pl.col(\"filename\").str.split(\".\")).select(\n",
+    "    a=pl.col(\"filename\")\n",
+    "    .list.slice(0, pl.col(\"filename\").list.len() - 4)\n",
+    "    .list.join(\".\"),\n",
+    "    b=pl.col(\"filename\").list.get(-4),\n",
+    "    c=pl.col(\"filename\").list.get(-3),\n",
+    "    d=pl.col(\"filename\").list.get(-2),\n",
+    "    e=pl.col(\"filename\").list.get(-1),\n",
+    ")\n",
+    "a\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a ['']\n",
+      "b ['flxf2024122906' 'flxf2024122912' 'flxf2024122918' 'flxf2024123000'\n",
+      " 'flxf2024123006' 'flxf2024123012' 'flxf2024123018' 'flxf2024123100'\n",
+      " 'flxf2024123106' 'flxf2024123112' 'flxf2024123118' 'flxf2025010100'\n",
+      " 'flxf2025010106' 'flxf2025010112' 'flxf2025010118' 'flxf2025010200'\n",
+      " 'flxf2025010206' 'flxf2025010212' 'flxf2025010218' 'flxf2025010300'\n",
+      " 'flxf2025010306' 'flxf2025010312' 'flxf2025010318' 'flxf2025010400'\n",
+      " 'flxf2025010406' 'flxf2025010412' 'flxf2025010418' 'flxf2025010500'\n",
+      " 'flxf2025010506' 'flxf2025010512' 'flxf2025010518' 'flxf2025010600'\n",
+      " 'flxf2025010606' 'flxf2025010612' 'flxf2025010618' 'flxf2025010700'\n",
+      " 'flxf2025010706' 'flxf2025010712' 'flxf2025010718' 'flxf2025010800'\n",
+      " 'flxf2025010806' 'flxf2025010812' 'flxf2025010818' 'flxf2025010900'\n",
+      " 'flxf2025010906' 'flxf2025010912' 'flxf2025010918' 'flxf2025011000'\n",
+      " 'flxf2025011006' 'flxf2025011012' 'flxf2025011018' 'flxf2025011100'\n",
+      " 'flxf2025011106' 'flxf2025011112' 'flxf2025011118' 'flxf2025011200'\n",
+      " 'flxf2025011206' 'flxf2025011212' 'flxf2025011218' 'flxf2025011300'\n",
+      " 'flxf2025011306' 'flxf2025011312' 'flxf2025011318' 'flxf2025011400'\n",
+      " 'flxf2025011406' 'flxf2025011412' 'flxf2025011418' 'flxf2025011500'\n",
+      " 'flxf2025011506' 'flxf2025011512' 'flxf2025011518' 'flxf2025011600'\n",
+      " 'flxf2025011606' 'flxf2025011612' 'flxf2025011618' 'flxf2025011700'\n",
+      " 'flxf2025011706' 'flxf2025011712' 'flxf2025011718' 'flxf2025011800'\n",
+      " 'flxf2025011806' 'flxf2025011812' 'flxf2025011818' 'flxf2025011900'\n",
+      " 'flxf2025011906' 'flxf2025011912' 'flxf2025011918' 'flxf2025012000'\n",
+      " 'flxf2025012006' 'flxf2025012012' 'flxf2025012018' 'flxf2025012100'\n",
+      " 'flxf2025012106' 'flxf2025012112' 'flxf2025012118' 'flxf2025012200'\n",
+      " 'flxf2025012206' 'flxf2025012212' 'flxf2025012218' 'flxf2025012300'\n",
+      " 'flxf2025012306' 'flxf2025012312' 'flxf2025012318' 'flxf2025012400'\n",
+      " 'flxf2025012406' 'flxf2025012412' 'flxf2025012418' 'flxf2025012500'\n",
+      " 'flxf2025012506' 'flxf2025012512' 'flxf2025012518' 'flxf2025012600'\n",
+      " 'flxf2025012606' 'flxf2025012612' 'flxf2025012618' 'flxf2025012700'\n",
+      " 'flxf2025012706' 'flxf2025012712' 'flxf2025012718' 'flxf2025012800'\n",
+      " 'flxf2025012806' 'flxf2025012812' 'flxf2025012818' 'flxf2025012900'\n",
+      " 'flxf2025012906' 'flxf2025012912' 'flxf2025012918' 'flxf2025013000'\n",
+      " 'flxf2025013006' 'flxf2025013012' 'flxf2025013018' 'flxf2025013100'\n",
+      " 'flxf2025013106' 'flxf2025013112' 'flxf2025013118' 'flxf2025020100'\n",
+      " 'flxf2025020106' 'flxf2025020112' 'flxf2025020118' 'flxf2025020200'\n",
+      " 'flxf2025020206' 'flxf2025020212' 'flxf2025020218' 'flxf2025020300'\n",
+      " 'flxf2025020306' 'flxf2025020312' 'flxf2025020318' 'flxf2025020400'\n",
+      " 'flxf2025020406' 'flxf2025020412' 'flxf2025020418' 'flxf2025020500'\n",
+      " 'flxf2025020506' 'flxf2025020512' 'flxf2025020518' 'flxf2025020600'\n",
+      " 'flxf2025020606' 'flxf2025020612' 'flxf2025020618' 'flxf2025020700'\n",
+      " 'flxf2025020706' 'flxf2025020712' 'flxf2025020718' 'flxf2025020800'\n",
+      " 'flxf2025020806' 'flxf2025020812' 'flxf2025020818' 'flxf2025020900'\n",
+      " 'flxf2025020906' 'flxf2025020912' 'flxf2025020918' 'flxf2025021000'\n",
+      " 'flxf2025021006' 'flxf2025021012' 'flxf2025021018' 'flxf2025021100'\n",
+      " 'flxf2025021106' 'flxf2025021112' 'flxf2025021118' 'flxf2025021200'\n",
+      " 'flxf2025021206' 'ipvanl' 'ipvf2024122906' 'ipvf2024122912'\n",
+      " 'ipvf2024122918' 'ipvf2024123000' 'ipvf2024123006' 'ipvf2024123012'\n",
+      " 'ipvf2024123018' 'ipvf2024123100' 'ipvf2024123106' 'ipvf2024123112'\n",
+      " 'ipvf2024123118' 'ipvf2025010100' 'ipvf2025010106' 'ipvf2025010112'\n",
+      " 'ipvf2025010118' 'ipvf2025010200' 'ipvf2025010206' 'ipvf2025010212'\n",
+      " 'ipvf2025010218' 'ipvf2025010300' 'ipvf2025010306' 'ipvf2025010312'\n",
+      " 'ipvf2025010318' 'ipvf2025010400' 'ipvf2025010406' 'ipvf2025010412'\n",
+      " 'ipvf2025010418' 'ipvf2025010500' 'ipvf2025010506' 'ipvf2025010512'\n",
+      " 'ipvf2025010518' 'ipvf2025010600' 'ipvf2025010606' 'ipvf2025010612'\n",
+      " 'ipvf2025010618' 'ipvf2025010700' 'ipvf2025010706' 'ipvf2025010712'\n",
+      " 'ipvf2025010718' 'ipvf2025010800' 'ipvf2025010806' 'ipvf2025010812'\n",
+      " 'ipvf2025010818' 'ipvf2025010900' 'ipvf2025010906' 'ipvf2025010912'\n",
+      " 'ipvf2025010918' 'ipvf2025011000' 'ipvf2025011006' 'ipvf2025011012'\n",
+      " 'ipvf2025011018' 'ipvf2025011100' 'ipvf2025011106' 'ipvf2025011112'\n",
+      " 'ipvf2025011118' 'ipvf2025011200' 'ipvf2025011206' 'ipvf2025011212'\n",
+      " 'ipvf2025011218' 'ipvf2025011300' 'ipvf2025011306' 'ipvf2025011312'\n",
+      " 'ipvf2025011318' 'ipvf2025011400' 'ipvf2025011406' 'ipvf2025011412'\n",
+      " 'ipvf2025011418' 'ipvf2025011500' 'ipvf2025011506' 'ipvf2025011512'\n",
+      " 'ipvf2025011518' 'ipvf2025011600' 'ipvf2025011606' 'ipvf2025011612'\n",
+      " 'ipvf2025011618' 'ipvf2025011700' 'ipvf2025011706' 'ipvf2025011712'\n",
+      " 'ipvf2025011718' 'ipvf2025011800' 'ipvf2025011806' 'ipvf2025011812'\n",
+      " 'ipvf2025011818' 'ipvf2025011900' 'ipvf2025011906' 'ipvf2025011912'\n",
+      " 'ipvf2025011918' 'ipvf2025012000' 'ipvf2025012006' 'ipvf2025012012'\n",
+      " 'ipvf2025012018' 'ipvf2025012100' 'ipvf2025012106' 'ipvf2025012112'\n",
+      " 'ipvf2025012118' 'ipvf2025012200' 'ipvf2025012206' 'ipvf2025012212'\n",
+      " 'ipvf2025012218' 'ipvf2025012300' 'ipvf2025012306' 'ipvf2025012312'\n",
+      " 'ipvf2025012318' 'ipvf2025012400' 'ipvf2025012406' 'ipvf2025012412'\n",
+      " 'ipvf2025012418' 'ipvf2025012500' 'ipvf2025012506' 'ipvf2025012512'\n",
+      " 'ipvf2025012518' 'ipvf2025012600' 'ipvf2025012606' 'ipvf2025012612'\n",
+      " 'ipvf2025012618' 'ipvf2025012700' 'ipvf2025012706' 'ipvf2025012712'\n",
+      " 'ipvf2025012718' 'ipvf2025012800' 'ipvf2025012806' 'ipvf2025012812'\n",
+      " 'ipvf2025012818' 'ipvf2025012900' 'ipvf2025012906' 'ipvf2025012912'\n",
+      " 'ipvf2025012918' 'ipvf2025013000' 'ipvf2025013006' 'ipvf2025013012'\n",
+      " 'ipvf2025013018' 'ipvf2025013100' 'ipvf2025013106' 'ipvf2025013112'\n",
+      " 'ipvf2025013118' 'ipvf2025020100' 'ipvf2025020106' 'ipvf2025020112'\n",
+      " 'ipvf2025020118' 'ipvf2025020200' 'ipvf2025020206' 'ipvf2025020212'\n",
+      " 'ipvf2025020218' 'ipvf2025020300' 'ipvf2025020306' 'ipvf2025020312'\n",
+      " 'ipvf2025020318' 'ipvf2025020400' 'ipvf2025020406' 'ipvf2025020412'\n",
+      " 'ipvf2025020418' 'ipvf2025020500' 'ipvf2025020506' 'ipvf2025020512'\n",
+      " 'ipvf2025020518' 'ipvf2025020600' 'ipvf2025020606' 'ipvf2025020612'\n",
+      " 'ipvf2025020618' 'ipvf2025020700' 'ipvf2025020706' 'ipvf2025020712'\n",
+      " 'ipvf2025020718' 'ipvf2025020800' 'ipvf2025020806' 'ipvf2025020812'\n",
+      " 'ipvf2025020818' 'ipvf2025020900' 'ipvf2025020906' 'ipvf2025020912'\n",
+      " 'ipvf2025020918' 'ipvf2025021000' 'ipvf2025021006' 'ipvf2025021012'\n",
+      " 'ipvf2025021018' 'ipvf2025021100' 'ipvf2025021106' 'ipvf2025021112'\n",
+      " 'ipvf2025021118' 'ipvf2025021200' 'ipvf2025021206' 'ocnf2024122912'\n",
+      " 'ocnf2024122918' 'ocnf2024123000' 'ocnf2024123006' 'ocnf2024123012'\n",
+      " 'ocnf2024123018' 'ocnf2024123100' 'ocnf2024123106' 'ocnf2024123112'\n",
+      " 'ocnf2024123118' 'ocnf2025010100' 'ocnf2025010106' 'ocnf2025010112'\n",
+      " 'ocnf2025010118' 'ocnf2025010200' 'ocnf2025010206' 'ocnf2025010212'\n",
+      " 'ocnf2025010218' 'ocnf2025010300' 'ocnf2025010306' 'ocnf2025010312'\n",
+      " 'ocnf2025010318' 'ocnf2025010400' 'ocnf2025010406' 'ocnf2025010412'\n",
+      " 'ocnf2025010418' 'ocnf2025010500' 'ocnf2025010506' 'ocnf2025010512'\n",
+      " 'ocnf2025010518' 'ocnf2025010600' 'ocnf2025010606' 'ocnf2025010612'\n",
+      " 'ocnf2025010618' 'ocnf2025010700' 'ocnf2025010706' 'ocnf2025010712'\n",
+      " 'ocnf2025010718' 'ocnf2025010800' 'ocnf2025010806' 'ocnf2025010812'\n",
+      " 'ocnf2025010818' 'ocnf2025010900' 'ocnf2025010906' 'ocnf2025010912'\n",
+      " 'ocnf2025010918' 'ocnf2025011000' 'ocnf2025011006' 'ocnf2025011012'\n",
+      " 'ocnf2025011018' 'ocnf2025011100' 'ocnf2025011106' 'ocnf2025011112'\n",
+      " 'ocnf2025011118' 'ocnf2025011200' 'ocnf2025011206' 'ocnf2025011212'\n",
+      " 'ocnf2025011218' 'ocnf2025011300' 'ocnf2025011306' 'ocnf2025011312'\n",
+      " 'ocnf2025011318' 'ocnf2025011400' 'ocnf2025011406' 'ocnf2025011412'\n",
+      " 'ocnf2025011418' 'ocnf2025011500' 'ocnf2025011506' 'ocnf2025011512'\n",
+      " 'ocnf2025011518' 'ocnf2025011600' 'ocnf2025011606' 'ocnf2025011612'\n",
+      " 'ocnf2025011618' 'ocnf2025011700' 'ocnf2025011706' 'ocnf2025011712'\n",
+      " 'ocnf2025011718' 'ocnf2025011800' 'ocnf2025011806' 'ocnf2025011812'\n",
+      " 'ocnf2025011818' 'ocnf2025011900' 'ocnf2025011906' 'ocnf2025011912'\n",
+      " 'ocnf2025011918' 'ocnf2025012000' 'ocnf2025012006' 'ocnf2025012012'\n",
+      " 'ocnf2025012018' 'ocnf2025012100' 'ocnf2025012106' 'ocnf2025012112'\n",
+      " 'ocnf2025012118' 'ocnf2025012200' 'ocnf2025012206' 'ocnf2025012212'\n",
+      " 'ocnf2025012218' 'ocnf2025012300' 'ocnf2025012306' 'ocnf2025012312'\n",
+      " 'ocnf2025012318' 'ocnf2025012400' 'ocnf2025012406' 'ocnf2025012412'\n",
+      " 'ocnf2025012418' 'ocnf2025012500' 'ocnf2025012506' 'ocnf2025012512'\n",
+      " 'ocnf2025012518' 'ocnf2025012600' 'ocnf2025012606' 'ocnf2025012612'\n",
+      " 'ocnf2025012618' 'ocnf2025012700' 'ocnf2025012706' 'ocnf2025012712'\n",
+      " 'ocnf2025012718' 'ocnf2025012800' 'ocnf2025012806' 'ocnf2025012812'\n",
+      " 'ocnf2025012818' 'ocnf2025012900' 'ocnf2025012906' 'ocnf2025012912'\n",
+      " 'ocnf2025012918' 'ocnf2025013000' 'ocnf2025013006' 'ocnf2025013012'\n",
+      " 'ocnf2025013018' 'ocnf2025013100' 'ocnf2025013106' 'ocnf2025013112'\n",
+      " 'ocnf2025013118' 'ocnf2025020100' 'ocnf2025020106' 'ocnf2025020112'\n",
+      " 'ocnf2025020118' 'ocnf2025020200' 'ocnf2025020206' 'ocnf2025020212'\n",
+      " 'ocnf2025020218' 'ocnf2025020300' 'ocnf2025020306' 'ocnf2025020312'\n",
+      " 'ocnf2025020318' 'ocnf2025020400' 'ocnf2025020406' 'ocnf2025020412'\n",
+      " 'ocnf2025020418' 'ocnf2025020500' 'ocnf2025020506' 'ocnf2025020512'\n",
+      " 'ocnf2025020518' 'ocnf2025020600' 'ocnf2025020606' 'ocnf2025020612'\n",
+      " 'ocnf2025020618' 'ocnf2025020700' 'ocnf2025020706' 'ocnf2025020712'\n",
+      " 'ocnf2025020718' 'ocnf2025020800' 'ocnf2025020806' 'ocnf2025020812'\n",
+      " 'ocnf2025020818' 'ocnf2025020900' 'ocnf2025020906' 'ocnf2025020912'\n",
+      " 'ocnf2025020918' 'ocnf2025021000' 'ocnf2025021006' 'ocnf2025021012'\n",
+      " 'ocnf2025021018' 'ocnf2025021100' 'ocnf2025021106' 'ocnf2025021112'\n",
+      " 'ocnf2025021118' 'ocnf2025021200' 'ocnf2025021206' 'pgbanl'\n",
+      " 'pgbf2024122906' 'pgbf2024122912' 'pgbf2024122918' 'pgbf2024123000'\n",
+      " 'pgbf2024123006' 'pgbf2024123012' 'pgbf2024123018' 'pgbf2024123100'\n",
+      " 'pgbf2024123106' 'pgbf2024123112' 'pgbf2024123118' 'pgbf2025010100'\n",
+      " 'pgbf2025010106' 'pgbf2025010112' 'pgbf2025010118' 'pgbf2025010200'\n",
+      " 'pgbf2025010206' 'pgbf2025010212' 'pgbf2025010218' 'pgbf2025010300'\n",
+      " 'pgbf2025010306' 'pgbf2025010312' 'pgbf2025010318' 'pgbf2025010400'\n",
+      " 'pgbf2025010406' 'pgbf2025010412' 'pgbf2025010418' 'pgbf2025010500'\n",
+      " 'pgbf2025010506' 'pgbf2025010512' 'pgbf2025010518' 'pgbf2025010600'\n",
+      " 'pgbf2025010606' 'pgbf2025010612' 'pgbf2025010618' 'pgbf2025010700'\n",
+      " 'pgbf2025010706' 'pgbf2025010712' 'pgbf2025010718' 'pgbf2025010800'\n",
+      " 'pgbf2025010806' 'pgbf2025010812' 'pgbf2025010818' 'pgbf2025010900'\n",
+      " 'pgbf2025010906' 'pgbf2025010912' 'pgbf2025010918' 'pgbf2025011000'\n",
+      " 'pgbf2025011006' 'pgbf2025011012' 'pgbf2025011018' 'pgbf2025011100'\n",
+      " 'pgbf2025011106' 'pgbf2025011112' 'pgbf2025011118' 'pgbf2025011200'\n",
+      " 'pgbf2025011206' 'pgbf2025011212' 'pgbf2025011218' 'pgbf2025011300'\n",
+      " 'pgbf2025011306' 'pgbf2025011312' 'pgbf2025011318' 'pgbf2025011400'\n",
+      " 'pgbf2025011406' 'pgbf2025011412' 'pgbf2025011418' 'pgbf2025011500'\n",
+      " 'pgbf2025011506' 'pgbf2025011512' 'pgbf2025011518' 'pgbf2025011600'\n",
+      " 'pgbf2025011606' 'pgbf2025011612' 'pgbf2025011618' 'pgbf2025011700'\n",
+      " 'pgbf2025011706' 'pgbf2025011712' 'pgbf2025011718' 'pgbf2025011800'\n",
+      " 'pgbf2025011806' 'pgbf2025011812' 'pgbf2025011818' 'pgbf2025011900'\n",
+      " 'pgbf2025011906' 'pgbf2025011912' 'pgbf2025011918' 'pgbf2025012000'\n",
+      " 'pgbf2025012006' 'pgbf2025012012' 'pgbf2025012018' 'pgbf2025012100'\n",
+      " 'pgbf2025012106' 'pgbf2025012112' 'pgbf2025012118' 'pgbf2025012200'\n",
+      " 'pgbf2025012206' 'pgbf2025012212' 'pgbf2025012218' 'pgbf2025012300'\n",
+      " 'pgbf2025012306' 'pgbf2025012312' 'pgbf2025012318' 'pgbf2025012400'\n",
+      " 'pgbf2025012406' 'pgbf2025012412' 'pgbf2025012418' 'pgbf2025012500'\n",
+      " 'pgbf2025012506' 'pgbf2025012512' 'pgbf2025012518' 'pgbf2025012600'\n",
+      " 'pgbf2025012606' 'pgbf2025012612' 'pgbf2025012618' 'pgbf2025012700'\n",
+      " 'pgbf2025012706' 'pgbf2025012712' 'pgbf2025012718' 'pgbf2025012800'\n",
+      " 'pgbf2025012806' 'pgbf2025012812' 'pgbf2025012818' 'pgbf2025012900'\n",
+      " 'pgbf2025012906' 'pgbf2025012912' 'pgbf2025012918' 'pgbf2025013000'\n",
+      " 'pgbf2025013006' 'pgbf2025013012' 'pgbf2025013018' 'pgbf2025013100'\n",
+      " 'pgbf2025013106' 'pgbf2025013112' 'pgbf2025013118' 'pgbf2025020100'\n",
+      " 'pgbf2025020106' 'pgbf2025020112' 'pgbf2025020118' 'pgbf2025020200'\n",
+      " 'pgbf2025020206' 'pgbf2025020212' 'pgbf2025020218' 'pgbf2025020300'\n",
+      " 'pgbf2025020306' 'pgbf2025020312' 'pgbf2025020318' 'pgbf2025020400'\n",
+      " 'pgbf2025020406' 'pgbf2025020412' 'pgbf2025020418' 'pgbf2025020500'\n",
+      " 'pgbf2025020506' 'pgbf2025020512' 'pgbf2025020518' 'pgbf2025020600'\n",
+      " 'pgbf2025020606' 'pgbf2025020612' 'pgbf2025020618' 'pgbf2025020700'\n",
+      " 'pgbf2025020706' 'pgbf2025020712' 'pgbf2025020718' 'pgbf2025020800'\n",
+      " 'pgbf2025020806' 'pgbf2025020812' 'pgbf2025020818' 'pgbf2025020900'\n",
+      " 'pgbf2025020906' 'pgbf2025020912' 'pgbf2025020918' 'pgbf2025021000'\n",
+      " 'pgbf2025021006' 'pgbf2025021012' 'pgbf2025021018' 'pgbf2025021100'\n",
+      " 'pgbf2025021106' 'pgbf2025021112' 'pgbf2025021118' 'pgbf2025021200'\n",
+      " 'pgbf2025021206' 'splanl']\n",
+      "c ['02']\n",
+      "d ['2024122906']\n",
+      "e ['grb2']\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in a.columns:\n",
+    "    print(i, a[i].unique().sort().to_numpy())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# <name>f<forecastdatehour>.<member>.<validdatecycle>.grb2\n",
+    "\n",
+    "2024122906\n",
+    "2024122906"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "herbie-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/__init__.py b/tests/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/test_cfs.py b/tests/test_cfs.py
new file mode 100644
index 00000000..ead4040d
--- /dev/null
+++ b/tests/test_cfs.py
@@ -0,0 +1,58 @@
+## Brian Blaylock
+## February 9, 2022
+
+"""Tests discovery of CFS model data."""
+
+from herbie import Herbie, config
+import pytest
+from itertools import product
+
+save_dir = config["default"]["save_dir"] / "Herbie-Tests-Data/"
+
+
+@pytest.mark.parametrize("variable, member", product(["tmp2m", "wnd10m"], [1, 2, 3, 4]))
+def test_cfs_timeseries(variable, member):
+    H = Herbie(
+        "2025-01-01",
+        model="cfs",
+        product="time_series",
+        member=member,
+        variable=variable,
+    )
+
+    assert H.grib, "CFS grib2 file not found"
+    assert H.idx, "CFS index file not found"
+    assert len(H.inventory()), "CFS inventory has zero length."
+
+
+@pytest.mark.parametrize(
+    "kind, member", product(["flxf", "pgbf", "ocnf", "ipvf"], [1, 4])
+)
+def test_cfs_6hour(kind, member):
+    H = Herbie(
+        "2024-12-25", model="cfs", product="6_hourly", kind=kind, member=member, fxx=12
+    )
+
+    assert H.grib, "CFS grib2 file not found"
+    assert H.idx, "CFS index file not found"
+    assert len(H.inventory()), "CFS inventory has zero length."
+
+
+@pytest.mark.parametrize(
+    "kind, member, hour",
+    product(["flxf", "pgbf", "ocnh", "ocnf", "ipvf"], [1, 4], [0, None]),
+)
+def test_cfs_monthly(kind, member, hour):
+    H = Herbie(
+        "2024-05-25",
+        model="cfs",
+        product="monthly_means",
+        kind=kind,
+        member=member,
+        month=1,
+        hour=hour,
+    )
+
+    assert H.grib, "CFS grib2 file not found"
+    assert H.idx, "CFS index file not found"
+    assert len(H.inventory()), "CFS inventory has zero length."
diff --git a/tests/test_hrrr.py b/tests/test_hrrr.py
index ec717955..7ac61a5e 100644
--- a/tests/test_hrrr.py
+++ b/tests/test_hrrr.py
@@ -14,6 +14,7 @@
 import requests
 import pandas as pd
 
+
 now = datetime.now()
 today = datetime(now.year, now.month, now.day, now.hour) - timedelta(hours=6)
 yesterday = today - timedelta(days=1)
@@ -108,7 +109,7 @@ def test_do_not_remove_file():
 
 
 @pytest.mark.skipif(wgrib2 is None, reason="wgrib2 not installed")
-def test_make_idx_with_wgrib():
+def test_make_idx_with_wgrib2():
     H = Herbie(
         "2022-12-13 6:00",
         model="hrrr",
@@ -128,20 +129,6 @@ def test_make_idx_with_wgrib():
     assert H.idx_source == "generated", "Doesn't look like a generated idx file."
 
 
-@pytest.mark.skipif(wgrib2 is None, reason="wgrib2 not installed")
-def test_create_idx_with_wgrib2():
-    """Test that Herbie can make an index file with wgrib2 when an index file is not found"""
-    H = Herbie(
-        today_str,
-        model="hrrr",
-        product="sfc",
-        save_dir=save_dir,
-    )
-    H.download()
-    H.idx = None
-    assert len(H.index_as_dataframe) > 0
-
-
 # ===========================
 # Check Downloaded File Sizes
 # ===========================
diff --git a/tests/test_pick_points.py b/tests/test_pick_points.py
index 860b6bd8..764854c2 100644
--- a/tests/test_pick_points.py
+++ b/tests/test_pick_points.py
@@ -76,8 +76,3 @@ def test_pick_points_self_points():
 
     r1 = ds.herbie.pick_points(points_self)
     assert all(r1.point_grid_distance == 0)
-
-
-def test_caching_tree():
-    """Test the BallTree caching works as expected."""
-    pass
diff --git a/tests/test_rap.py b/tests/test_rap.py
index 6c106515..36cbe0c3 100644
--- a/tests/test_rap.py
+++ b/tests/test_rap.py
@@ -7,7 +7,7 @@
 import pytest
 
 from herbie import Herbie, config
-from tests.util import is_time_between
+from util import is_time_between
 
 today = pd.to_datetime("today").floor("1D") - pd.to_timedelta("1D")
 save_dir = config["default"]["save_dir"] / "Herbie-Tests-Data/"
@@ -24,7 +24,7 @@
     "because upstream data not available or incomplete at night.",
 )
 def test_rap_aws():
-    # Test
+    """Test downloading the RAP model from AWS."""
     H = Herbie(today, model="rap", save_dir=save_dir, overwrite=True)
     assert H.grib is not None
 
@@ -43,7 +43,6 @@ def test_rap_aws():
 
 def test_rap_historical():
     """Search for RAP urls on NCEI that I know exist."""
-
     H = Herbie(
         "2019-11-23",
         model="rap_historical",
@@ -62,6 +61,7 @@ def test_rap_historical():
 
 
 def test_rap_ncei():
+    """Search for RAP urls on NCEI that I know exist."""
     H = Herbie(
         "2020-03-15",
         model="rap_ncei",
diff --git a/tests/test_first.py b/tests/test_simple.py
similarity index 65%
rename from tests/test_first.py
rename to tests/test_simple.py
index 78bb0688..7b896ea8 100644
--- a/tests/test_first.py
+++ b/tests/test_simple.py
@@ -14,3 +14,7 @@ def test_str():
     a = "Herbie"
     b = "Herbie"
     assert a == b
+
+def test_charity():
+    """Charity never faileth."""
+    assert "charity" == "charity"