Merge pull request #280 from NASA-IMPACT/develop

Production deploy v0.9.0

datasets/CMIP-winter-median-ta.data.mdx

@@ -31,6 +31,8 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;        
         }
     legend:
+      unit:
+        label: °C
       type: gradient
       label: Air temperature difference [C]
       min: "-5.5"
@@ -65,6 +67,8 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: °C
       type: gradient
       label: Air temperature difference [C]
       min: "-5.5"

datasets/co2.data.mdx

@@ -60,9 +60,11 @@ layers:
         - -0.0000015
         - 0.0000015
     legend:
+      unit:
+        label: ppm
       type: gradient
-      min: "-1.5 ppm"
-      max: "1.5 ppm"
+      min: "-1.5"
+      max: "1.5"
       stops:
         - "#384cc2"
         - "#6384eb"

datasets/fire.data.mdx

@@ -0,0 +1,52 @@
+---
+id: fire
+name: Fire Perimeters
+description: Fire perimeters generated from VIIRs sensor observations.
+usage:
+  - url: https://nasa-impact.github.io/veda-docs/notebooks/tutorials/mapping-fires.html
+    label: View example notebook
+    title: VEDA documentation for visualization and download
+media:
+  src: ::file ./fire--dataset-cover.jpg
+  alt: Forest burning at night
+  author:
+    name: Matt Howard
+    url: https://unsplash.com/photos/eAKDzK4lo4o
+thematics:
+  - eis
+
+layers:
+  - id: eis_fire_perimeter
+    stacCol: eis_fire_perimeter
+    name: Fire Perimeter
+    type: vector
+    description: eis_fire_perimeter
+    zoomExtent:
+      - 5
+      - 20
+---
+
+<Block>
+  <Prose>
+    ## FEDs Fire Perimeters
+
+    Fire perimeter data is generated by the FEDs algorithm. The FEDs algorithm tracks fire movement and severity by ingesting observations from the VIIRS thermal sensors on the Suomi NPP and NOAA-20 satellites. This algorithm uses raw VIIRS observations to generate a polygon of the fire, locations of the active fire line, and estimates of fire mean Fire Radiative Power (FRP). The VIIRS sensors overpass at ~1:30 AM and PM local time, and provide estimates of fire evolution ~ every 12 hours. The data produced by this algorithm describe where fires are in space and how fires evolve through time. This CONUS-wide implementation of the FEDs algorithm is based on [Chen et al 2020’s algorithm for California.](https://www.nature.com/articles/s41597-022-01343-0)
+
+  </Prose>
+</Block>
+<Block>
+  <Prose>
+    ## Scientific Application Using FEDS Fire Perimeters
+    FEDS Fire Perimeters offer insight into pre-fire risk, fire behaviour, and post-fire effects. The Earth Information System - Fire team is using FEDs perimeters to <Link to='/discoveries/fire-life-cycle'>understand the full lifecycle of a fire</Link>
+  </Prose>
+</Block>
+<Block>
+  <Prose>
+    ## Datasets generated using FEDs Fire perimeters
+
+    * <Link to='/data-catalog/caldor-fire-behavior-burn-severity'>Caldor Fire Behavior and Burn Severity</Link>
+    * <Link to='/data-catalog/barc-thomasfire'>Burn Area Reflectance Classification for Thomas Fire</Link>
+    * <Link to='/data-catalog/frp-max-thomasfire'>Maximum Fire Radiative Power for Thomas Fire</Link>
+
+  </Prose>
+</Block>

datasets/geoglam.data.mdx

@@ -6,16 +6,18 @@ media:
   src: ::file ./geoglam--dataset-cover.jpg
   alt: Bird's eye view of fields
   author:
-    name: jean wimmerlin
+    name: Jean Wimmerlin
     url: https://unsplash.com/photos/RUj5b4YXaHE
 thematics:
   - agriculture
+sources:
+  - geoglam
 layers:
   - id: geoglam
     stacCol: geoglam
     name: GEOGLAM Crop Conditions
     type: raster
-    description: "Add dataset description here"
+    description: Combined crop conditions across both the Crop Monitor for AMIS and Crop Monitor for Early Warning
     zoomExtent:
       - 0
       - 16

datasets/global-reanalysis-da.data.mdx

@@ -35,10 +35,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: kg m-2 s-1
       type: gradient
       label: Evapotranspiration [kg m-2 s-1]
       min: "0"
-      max: "0.0001 kg m-2 s-1"
+      max: "0.0001"
       stops:
         - '#440154'
         - '#3b528b'
@@ -69,10 +71,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: g m-2 s-1
       type: gradient
       label: Gross primary productivity [g m-2 s-1]
       min: "0"
-      max: "0.0001 g m-2 s-1"
+      max: "0.0001"
       stops:
         - '#440154'
         - '#3b528b'
@@ -103,10 +107,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: mm
       type: gradient
       label: Groundwater storage [mm]
       min: "4500"
-      max: "5000 mm"
+      max: "5000"
       stops:
         - '#440154'
         - '#3b528b'
@@ -137,10 +143,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: mm
       type: gradient
       label: Snow Water Equivalent [mm]
       min: "0"
-      max: "500 mm"
+      max: "500"
       stops:
         - "#F7FBFF"
         - "#D0E1F2"
@@ -172,10 +180,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: m3 s-1
       type: gradient
       label: Streamflow [m3 s-1]
       min: "0"
-      max: "2500 m3 s-1"
+      max: "2500"
       stops:
         - '#440154'
         - '#3b528b'
@@ -206,10 +216,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: kg m-2 s-1
       type: gradient
       label: Surface runoff [kg m-2 s-1]
       min: "0"
-      max: "0.00001 kg m-2 s-1"
+      max: "0.00001"
       stops:
         - '#440154'
         - '#3b528b'
@@ -240,10 +252,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: kg m-2 s-1
       type: gradient
       label: Subsurface runoff [kg m-2 s-1]
       min: "0"
-      max: "0.0001 kg m-2 s-1"
+      max: "0.0001"
       stops:
         - '#440154'
         - '#3b528b'
@@ -274,10 +288,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: mm
       type: gradient
       label: Terrestrial Water Storage [mm]
       min: "5000"
-      max: "5800 mm"
+      max: "5800"
       stops:
         - '#440154'
         - '#3b528b'
@@ -308,10 +324,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
         }
     legend:
+      unit:
+        label: kg m-2 s-1
       type: gradient
       label: Total precipitation [kg m-2 s-1]
       min: "0"
-      max: "0.00001 kg m-2 s-1"
+      max: "0.00001"
       stops:
         - "#F7FBFF"
         - "#D0E1F2"

datasets/lis.da.trend.data.mdx

@@ -34,10 +34,12 @@ layers:
            return `TWS trend (2003-2021) VS GPP trend (2003-2021)`;
          }
     legend:
+      unit:
+        label: (mm/yr)
       type: gradient
       label: TWS Trend (mm/yr)
       min: "-20"
-      max: "20 (mm/yr)"
+      max: "20"
       stops:
       - "#a50026"
       - "#f46d43"
@@ -61,10 +63,12 @@ layers:
         - 40
       nodata: -9999.
     legend:
+      unit:
+        label: (gC/m2/yr)
       type: gradient
       label: GPP Trend (gC/m2/yr)
       min: "-40"
-      max: "40 (gC/m2/yr)"
+      max: "40"
       stops:
       - "#a50026"
       - "#f46d43"

datasets/no2.data.mdx

@@ -4,10 +4,10 @@ name: 'Nitrogen Dioxide'
 featured: true
 description: "Since the outbreak of the novel coronavirus, atmospheric concentrations of nitrogen dioxide have changed by as much as 60% in some regions."
 usage:
-  - url: 'https://github.com/NASA-IMPACT/veda-docs/blob/no2-single-item-viz/example-notebooks/no2-single-item-vizualization.ipynb'
+  - url: 'https://github.com/NASA-IMPACT/veda-docs/blob/main/notebooks/quickstarts/no2-map-plot.ipynb'
     label: View example notebook
     title: 'Static view in VEDA documentation'
-  - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FNASA-IMPACT%2Fveda-docs&urlpath=lab%2Ftree%2Fveda-docs%2Fexample-notebooks%2Fno2-single-item-vizualization.ipynb&branch=no2-single-item-viz"
+  - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FNASA-IMPACT%2Fveda-docs&urlpath=lab%2Ftree%2Fveda-docs%2Fnotebooks%2Fquickstarts%2Fno2-map-plot.ipynb&branch=main"
     label: Run example notebook
     title: 'Interactive session in VEDA 2i2c JupyterHub (requires account)'
 media:
@@ -99,9 +99,11 @@ layers:
         - 0
         - 30E14
     legend:
+      unit:
+        label: mol/cm2
       type: gradient
       min: 0
-      max: 30e14 mol/cm2
+      max: 30e14
       stops:
         - '#ffffff'
         - '#fdd1bf'

datasets/snow-projections-median.data.mdx

@@ -31,10 +31,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;        
         }
     legend:
+      unit:
+        label: mm
       type: gradient
       label: Snow Water Equivalent [mm]
       min: "0"
-      max: "1000 mm"
+      max: "1000"
       stops:
       - "#F7FBFF"
       - "#D0E1F2"
@@ -62,10 +64,12 @@ layers:
           return `${dateFns.format(datetime, 'DD LLL yyyy')}`;        
         }
     legend:
+      unit:
+        label: mm
       type: gradient
       label: Snow Water Equivalent [mm]
       min: "0"
-      max: "1000 mm"
+      max: "1000"
       stops:
       - "#F7FBFF"
       - "#D0E1F2"

discoveries/air-quality-and-covid-19.discoveries.mdx

@@ -191,8 +191,6 @@ thematics:
   <Prose>
     ## Measuring Air Pollution on the Ground at Airports
     New research during the pandemic is also looking at how COVID-related travel bans are impacting air quality around airports. Current conditions create a unique opportunity to study airport-related pollutants, especially nitrogen dioxide and formaldehyde. While travel bans and strict regulations around air travel have been in place, air traffic has yet to return to previous levels, and many planes remain grounded.
-
-    They are comparing the on-the-ground sensor information from NASA's [Pandora Project](https://pandora.gsfc.nasa.gov/ "Explore the Pandora Project") with satellite information from TROPOMI. So far, they have found that nitrogen dioxide hotspots in Atlanta shifted from the airport, shown here, to the city center from April-June 2020. By September, however, satellites revealed the airport had reemerged as a dominant nitrogen dioxide emission source.
   </Prose>
   <Figure>
     <Map
@@ -282,6 +280,6 @@ thematics:
 
     NASA scientists will continue to monitor nitrogen dioxide levels and long-term trends around the world. NASA is expected to launch its [Tropospheric Emissions: Monitoring of Pollution (TEMPO)](http://tempo.si.edu/overview.html "Explore the TEMPO instrument") instrument in 2022, which will provide hourly, high-resolution measurements of nitrogen dioxide, ozone, and other air pollutants across North America, improving future air quality forecasts.
 
-    [Explore How COVID-19 Is Affecting Earth's Climate](/covid19/discoveries/climate/climate-change-and-covid "Explore How COVID-19 Is Affecting Earth's Climate")
+    <Link to='/discoveries/climate/climate-and-covid'>Explore How COVID-19 Is Affecting Earth's Climate</Link>
   </Prose>
 </Block>

discoveries/coastal-flooding-and-slr.discoveries.mdx

@@ -185,7 +185,7 @@ thematics:
 
 <Block>
    <Prose>
-    The ECCO model shows that there are seasonal variations in sea-level change and a background increase of **3.6 mm/yr** over time. Sea-level rise compounds the risk of coastal flooding by raising the water level at river mouths and increasing flood magnitudes.separates total sea-level change into two components: extra water mass added to the ocean (barystatic) and ocean temperature and salinity changes (steric). At this location, the steric component remains steady over time, while the barystatic component is increasing, meaning that **sea-level rise off the coast of Bangladesh is driven by extra water mass added to the ocean from the ice sheets, glaciers, and changes to land-water storage**. Understanding the sources of past sea-level rise helps researchers create better projections of future sea-level rise.
+    The ECCO model shows that there are seasonal variations in sea-level change and a background increase of **3.6 mm/yr** over time. Sea-level rise compounds the risk of coastal flooding by raising the water level at river mouths and increasing flood magnitudes. ECCO separates total sea-level change into two components: extra water mass added to the ocean (barystatic) and ocean temperature and salinity changes (steric). At this location, the steric component remains steady over time, while the barystatic component is increasing, meaning that **sea-level rise off the coast of Bangladesh is driven by extra water mass added to the ocean from the ice sheets, glaciers, and changes to land-water storage**. Understanding the sources of past sea-level rise helps researchers create better projections of future sea-level rise.
 
     Analyze the ECCO data further:
       - <a href="https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FEarth-Information-System%2Fveda-data-processing&urlpath=lab%2Ftree%2Fveda-data-processing%2FECCO_NetCDF_to_COG%2Fcreate_ECCO_SSH_timeseries.ipynb&branch=main" target="_blank">interactive notebook</a>

package.json

@@ -1,7 +1,7 @@
 {
   "name": "veda-config",
   "description": "Configuration for Veda",
-  "version": "0.8.1",
+  "version": "0.9.0",
   "source": "./.veda/ui/app/index.html",
   "license": "Apache-2.0",
   "scripts": {

taxonomies.yml

@@ -22,4 +22,6 @@ sources:
   - id: nasa-ciesin-grdi
     name: NASA CIESIN
   - id: atsdr-svi
-    name: ATSDR
+    name: ATSDR
+  - id: geoglam
+    name: GEOGLAM