From 68e2bec20d85def71e410d4a4cb00d06660b164b Mon Sep 17 00:00:00 2001 From: Mattia Almansi Date: Thu, 20 Jun 2024 18:22:47 +0200 Subject: [PATCH] test nb --- .github/workflows/on-pull-request.yml | 2 +- ...hly-single-levels_forecast-skill_q12.ipynb | 1089 +++++++++++++++++ ...hly-single-levels_forecast-skill_q13.ipynb | 1089 +++++++++++++++++ 3 files changed, 2179 insertions(+), 1 deletion(-) create mode 100644 Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q12.ipynb create mode 100644 Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q13.ipynb diff --git a/.github/workflows/on-pull-request.yml b/.github/workflows/on-pull-request.yml index 0c9c06cb..6c41f243 100644 --- a/.github/workflows/on-pull-request.yml +++ b/.github/workflows/on-pull-request.yml @@ -82,5 +82,5 @@ jobs: uses: thollander/actions-comment-pull-request@v2 with: message: | - **Book preview:** ${{ steps.upload-artifact.outputs.artifact-url }} + **Jupyter Book Preview:** ${{ steps.upload-artifact.outputs.artifact-url }} comment_tag: artifact-url diff --git a/Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q12.ipynb b/Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q12.ipynb new file mode 100644 index 00000000..faa654dd --- /dev/null +++ b/Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q12.ipynb @@ -0,0 +1,1089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5bda21ce-fe22-486d-bdb0-d7450216d405", + "metadata": { + "papermill": { + "duration": 0.008751, + "end_time": "2024-03-08T17:23:56.435664", + "exception": false, + "start_time": "2024-03-08T17:23:56.426913", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Use case: Risk assessment in agriculture using seasonal forecasts as an input for climate impact models.\n", + "\n", + "## Quality assessment questions\n", + "* Do I have to correct systematic errors in temperature and precipitation before using seasonal forecasts as an input for my crop model?\n", + "* Can I assume that climate models always produce the same systematic error for seasonal forecasts over a certain area? " + ] + }, + { + "attachments": { + "65ac1e47-560f-45d9-b5eb-eaf0388326cb.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "2590e7f4-d6b3-4eed-a91f-c94a78df2db5", + "metadata": {}, + "source": [ + "Seasonal forecasts can be used as an input to crop models for agricultural risk assessment [[1]](https://www.nature.com/articles/s41598-018-20628-2) [[2]](https://www.sciencedirect.com/science/article/pii/S0168192318303836) [[3]](https://www.sciencedirect.com/science/article/pii/S2405880722000425). However, the presence of systematic errors can undermine the performance of crop models in a number of ways, particularly when critical thresholds in the plant growth process are considered. Similar problems can arise in other sectoral applications, such as the management of renewable energy facilities [[4]](https://www.sciencedirect.com/science/article/pii/S0960148119306196), or the alerting of national health services for the occurrence of summer heat waves [[5]](https://link.springer.com/article/10.1007/s00382-021-05828-3). In addition to temperature and precipitation, the most commonly used climate variables for risk assessment in all sectoral applications, the notebook also considers wind speed and dew point temperature. While these variables are commonly used in agricultural applications to calculate potential evapotranspiration, the results of the assessment are also valuable for several other sectors.\n", + "\n", + "## Quality assessment statement\n", + "\n", + "* Seasonal forecasts should be bias-corrected before being used as input to climate impact models. Depending on the impact model, it might be advisable to bias-correct the resulting impact indicator [[6]](https://doi.org/10.1175/JHM-D-19-0042.1)\n", + "* Model drifts and bias tend to be a complex function of both the start time of the forecast and of its valid time, with a slightly stronger dependence on the valid time for the case of temperature and precipitation. Therefore, the most appropriate approach to produce unbiased forecasts for use in impact models must be selected on a case-by-case basis.[[7]](https://link.springer.com/article/10.1007/s00382-019-04640-4) [[8]](https://link.springer.com/article/10.1007/s00382-017-3962-9)\n", + "* For the implementation of bias correction procedures, it is recommended that reference climatologies are computed for time scales no longer than one month, since the typical model bias changes on a monthly basis. Instead, the correction of model biases on a seasonal (i.e. three months) timescale may lead to a loss of quality.\n", + "* Similarly, the calculation of multi-model statistics requires a recalibration of the model output in order to align all models around the same climatological distribution. [[9]](https://link.springer.com/article/10.1007/s00382-020-05314-2)\n", + "\n", + "![image.png](attachment:65ac1e47-560f-45d9-b5eb-eaf0388326cb.png)\n", + "\n", + "The figure shows a sample of the ECMWF monthly temperature bias for Western North America as a function of the starting month and of the valid month of the forecast. More details on how to interpret the results are available below in the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "c10778f3-052d-4b4b-ba3c-f8c8dcec2232", + "metadata": {}, + "source": [ + "## Methodology\n", + "\n", + "This notebook provides a comprehensive assessment of the systematic errors of the forecast systems by comparing the model predictions with the ERA5 reanalysis. The analysis is carried out for eight regions selected from those used in the IPCC-AR6 (see figure below), which capture the spatial scale of the main systematic errors already analysed in previous studies [[10]](https://link.springer.com/article/10.1007/s00382-019-04640-4)\n", + "\n", + "The analysis and results are organised in the following steps, which are detailed in the sections below:\n", + "\n", + "**1. Choose the data to use and setup code:**\n", + " * Import required packages\n", + " * Define parameters describing regions, variables, and data\n", + " * Plot the chosen regions\n", + " * Define required functions\n", + " * Show a sample global map of temperature biases\n", + "\n", + "**2. ERA5 data retrieval and area average:**\n", + " * Retrieve monthly ERA5 data and compute the regional mean climatology, i.e. 12 values for each area\n", + " * Compute area averages\n", + "\n", + "**3. Seasonal hindcast retrieval and area average:**\n", + " * Retrieve monthly seasonal forecast data from all available centres and for all starting dates, compute the monthly mean climatology (i.e. 6 values, since each forecast is valid for 6 months) and subtract the corresponding ERA5 climatology.\n", + " * Compute area averages\n", + " * Plot a sample map\n", + "\n", + "**4. Compute the hindcast bias**\n", + "\n", + "**5. Plot and describe results:** \n", + " * Plots are shown in a comprehensive set of panels that provide an overview of the multimodel performance \n", + " * The results are summarised and discussed\n", + "\n", + "Note that crop models and other sectoral applications tend to use daily values rather than the monthly statistics considered in this notebook, and they focus on smaller areas compared to the regions considered below. However, the analysis of monthly data over the IPCC-AR6 regions provides a useful overview of the overall characteristics of the dataset.\n", + "\n", + "Note that for ECCC, only one of the two systems has been analysed (system 3). This is different from the approach taken in the C3S charts, and CanSIPSv2.1, where both systems are combined (system 2 and system 3)." + ] + }, + { + "cell_type": "markdown", + "id": "93a36ba7-11f3-43f2-9cea-2eff221f68ec", + "metadata": {}, + "source": [ + "## Analysis and results" + ] + }, + { + "cell_type": "markdown", + "id": "368e01c6", + "metadata": { + "papermill": { + "duration": 0.007051, + "end_time": "2024-03-08T17:23:56.492658", + "exception": false, + "start_time": "2024-03-08T17:23:56.485607", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 1. Choose the data to use and setup the code." + ] + }, + { + "cell_type": "markdown", + "id": "e798c526-d227-4011-8410-dd8931790be6", + "metadata": {}, + "source": [ + "#### Import required packages\n", + "We use the `regionmask package` to extract climatologies for a few selected regiones that have already e been adopted in several climate studies, including the analysis presented in the latest IPCC report.\n", + "\n", + "A description of the regions adopted for climate assesements in the scientific literature is available [here](https://regionmask.readthedocs.io/en/stable/defined_scientific.htm)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "48246700", + "metadata": { + "papermill": { + "duration": 3.749769, + "end_time": "2024-03-08T17:24:00.248720", + "exception": false, + "start_time": "2024-03-08T17:23:56.498951", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "import regionmask\n", + "import xarray as xr\n", + "import numpy as np\n", + "from c3s_eqc_automatic_quality_control import diagnostics, download, plot\n", + "\n", + "plt.style.use(\"seaborn-v0_8-notebook\")" + ] + }, + { + "cell_type": "markdown", + "id": "49f2816d", + "metadata": { + "papermill": { + "duration": 0.006416, + "end_time": "2024-03-08T17:24:00.262100", + "exception": false, + "start_time": "2024-03-08T17:24:00.255684", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "#### Define parameters describing regions, variables, and data\n", + "\n", + "In this notebook the analysis of the model bias is done for 5 variables from all 8 originating centres. The SREX regions selected for the analysis are:\n", + "\n", + "- NEAF: Northern-East Africa\n", + "- ENA: Eastern North America\n", + "- MED: Southern Europe/Mediterranean\n", + "- NEB: North-eastern Brazil\n", + "- SAS: Southern Asia\n", + "- SEA: South-Eastern Asia\n", + "- WNA: Western North America\n", + "- NWS: Western Coast of South America\n", + "\n", + "A `common_request` is defined in order to cover the entire global domain and the entire hindcast period from 1993 to 2016. Specific data requests are then defined for the reanalysis and for the seasonal forecasts, so that all available months and lead times are retrieved for the analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "348d6273-3e42-4dce-90bb-5b0f5628d356", + "metadata": { + "papermill": { + "duration": 0.016612, + "end_time": "2024-03-08T17:24:00.285147", + "exception": false, + "start_time": "2024-03-08T17:24:00.268535", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Time\n", + "year_start_hindcast = 1993\n", + "year_stop_hindcast = 2016\n", + "\n", + "# Variable\n", + "variables = [\n", + " \"2m_temperature\",\n", + " \"total_precipitation\",\n", + " \"10m_wind_speed\",\n", + " \"2m_dewpoint_temperature\",\n", + "]\n", + "\n", + "# Define centres with missing variables\n", + "missing_variables = {\"eccc\": [\"2m_dewpoint_temperature\"]}\n", + "\n", + "# Centres\n", + "centres = {\n", + " \"cmcc\": {\"system\": \"35\"},\n", + " \"dwd\": {\"system\": \"21\"},\n", + " \"eccc\": {\"system\": \"3\"},\n", + " \"ecmwf\": {\"system\": \"51\"},\n", + " \"jma\": {\"system\": \"3\"},\n", + " \"meteo_france\": {\"system\": \"8\"},\n", + " \"ncep\": {\"system\": \"2\"},\n", + " \"ukmo\": {\"system\": \"601\"},\n", + "}\n", + "assert all(\"system\" in v for v in centres.values())\n", + "assert set(centres) <= {\n", + " \"cmcc\",\n", + " \"dwd\",\n", + " \"eccc\",\n", + " \"ecmwf\",\n", + " \"jma\",\n", + " \"meteo_france\",\n", + " \"ncep\",\n", + " \"ukmo\",\n", + "}\n", + "\n", + "# Regions\n", + "defined_regions_module_name = \"ar6.land\"\n", + "regions = [\n", + " \"NWS\",\n", + " \"ENA\",\n", + " \"MED\",\n", + " \"NEAF\",\n", + " \"NES\",\n", + " \"SAS\",\n", + " \"SEA\",\n", + " \"WNA\",\n", + "]\n", + "\n", + "# Ploting settings\n", + "plot_kwargs = {\n", + " \"total_precipitation\": {\"cmap\": \"BrBG\"},\n", + "}\n", + "\n", + "\n", + "# Define data requests\n", + "chunks = {\"year\": 1, \"variable\": 1}\n", + "common_request = {\n", + " \"format\": \"grib\",\n", + " \"area\": [89.5, -179.5, -89.5, 179.5],\n", + " \"year\": [str(year) for year in range(year_start_hindcast, year_stop_hindcast + 1)],\n", + "}\n", + "\n", + "collection_id_reanalysis = \"reanalysis-era5-single-levels-monthly-means\"\n", + "request_reanalysis = common_request | {\n", + " \"product_type\": \"monthly_averaged_reanalysis\",\n", + " \"month\": [f\"{month:02d}\" for month in range(1, 13)],\n", + " \"time\": \"00:00\",\n", + "}\n", + "\n", + "collection_id_seasonal = \"seasonal-monthly-single-levels\"\n", + "request_seasonal = common_request | {\n", + " \"product_type\": \"monthly_mean\",\n", + " \"leadtime_month\": list(map(str, range(1, 7))),\n", + " \"month\": [f\"{month:02d}\" for month in range(1, 13)],\n", + " \"grid\": \"1/1\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c0e897d5-0572-4653-8515-b3100d03348d", + "metadata": {}, + "source": [ + "#### Plot the chosen regions\n", + "For the bias analysis we use 8 out of the 45 regions defined in the `regionmask` package in order to assess the systematic errors of the modelling systems under different climatic regimes (tropical, extratropical, continental, maritime) with a number of cases maneageable within the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2686a074-0698-4dee-82bf-8cf0f161e0af", + "metadata": { + "papermill": { + "duration": 1.932335, + "end_time": "2024-03-08T17:24:02.237426", + "exception": false, + "start_time": "2024-03-08T17:24:00.305091", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_defined_regions(defined_regions_module_name):\n", + " defined_regions = regionmask.defined_regions\n", + " for module in defined_regions_module_name.split(\".\"):\n", + " defined_regions = getattr(defined_regions, module)\n", + " return defined_regions\n", + "\n", + "\n", + "defined_regions = get_defined_regions(defined_regions_module_name)\n", + "assert set(regions) <= set(defined_regions.abbrevs)\n", + "\n", + "ax = defined_regions[regions].plot(\n", + " projection=ccrs.Robinson(),\n", + " label=\"abbrev\",\n", + " add_ocean=True,\n", + ")\n", + "ax.set_global()" + ] + }, + { + "cell_type": "markdown", + "id": "e5ee547d-b9a3-4972-913a-a9563ab729b6", + "metadata": {}, + "source": [ + "#### Define the required functions\n", + "\n", + "- The `regionalised_spatial_weighted_mean` function extracts the regional means over the selected domains. It uses spatial weighting to account for the latitudinal dependence of the grid size in the lon-lat grids used for the reanalysis and for the forecast models. The bias is then calculated later in the notebook by simply subtracting the regional means.\n", + "\n", + "- The `postprocess_dataarray` function performs two important postprocessings operations required to align the arrays containing the reanalsysis and the forecast data. \n", + "\n", + " - The time dimensions are renamed to allow the broadcasting of the dimension `valid_time`, which is used to compute the bias, independently of the corresponding `lead_times` in the forecast. \n", + "\n", + " - Furthermore, the cumulative variables have to be rescaled in order to make ERA5 data and seasonal forecast data comparable on a monthly timescale. A [conversion table](https://confluence.ecmwf.int/pages/viewpage.action?pageId=197702790) for accumulated variables is available in the [Copernicus Knowledge Base](https://confluence.ecmwf.int/pages/viewpage.action?pageId=55116796) repository.\n", + "\n", + "- The function `get_seasonal_maps` is used to derive a sample bias map to illustrate how the analysis is performed.\n", + "\n", + "- The `monthly_mean` function will be used to derive the monthly values at the appropriate `monthly_mean` before calculating the bias." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a3aa1b96-f0fc-4cd2-810c-6a32a4eae0ea", + "metadata": { + "papermill": { + "duration": 0.022708, + "end_time": "2024-03-08T17:24:02.323620", + "exception": false, + "start_time": "2024-03-08T17:24:02.300912", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def regionalised_spatial_weighted_mean(\n", + " ds, region, defined_regions_module_name, weights, mean_dims=None\n", + "):\n", + " defined_regions = get_defined_regions(defined_regions_module_name)\n", + " mask = defined_regions.mask(ds)\n", + " index = defined_regions.map_keys(region)\n", + " ds = ds.where((mask == index).compute(), drop=True)\n", + " ds = diagnostics.spatial_weighted_mean(ds, weights=weights)\n", + " if mean_dims:\n", + " return ds.mean(mean_dims, keep_attrs=True)\n", + " return ds\n", + "\n", + "\n", + "def postprocess_dataarray(da):\n", + " time_dims = {dim for dim in da.dims if \"time\" in dim}\n", + " if time_dims:\n", + " if len(time_dims) == 1:\n", + " (valid_time,) = time_dims\n", + " else:\n", + " starting_time = \"forecast_reference_time\"\n", + " (valid_time,) = time_dims - {starting_time}\n", + " da = da.rename({starting_time: \"starting_time\"})\n", + " da = da.rename({valid_time: \"valid_time\"})\n", + "\n", + " if \"leadtime\" in da.coords:\n", + " da = da.drop_vars(\"leadtime\")\n", + "\n", + " # Convert units\n", + " name = da.name\n", + " units = da.attrs[\"units\"]\n", + " m_to_mm = 1.0e3\n", + " day_to_s = 60 * 60 * 24\n", + " if name == \"total_precipitation\" and units != \"mm/month\":\n", + " match units:\n", + " case \"m\":\n", + " factor = m_to_mm\n", + " case \"m s**-1\":\n", + " factor = m_to_mm * day_to_s\n", + " case _:\n", + " raise ValueError(f\"{name=} {units=}\")\n", + " if \"valid_time\" in da.coords:\n", + " factor *= da[\"valid_time\"].dt.days_in_month\n", + " units = \"mm/month\"\n", + " else:\n", + " units = \"mm/day\"\n", + " elif name == \"surface_solar_radiation_downwards\" and units != \"W m**-2\":\n", + " match units:\n", + " case \"J m**-2\":\n", + " factor = 1 / day_to_s\n", + " units = \"W m**-2\"\n", + " case _:\n", + " raise ValueError(f\"{name=} {units=}\")\n", + " else:\n", + " return da\n", + "\n", + " with xr.set_options(keep_attrs=True):\n", + " da *= factor\n", + " da.attrs[\"units\"] = units\n", + " return da\n", + "\n", + "\n", + "def get_seasonal_map(ds, starting_month, valid_month, grid_out, **kwargs):\n", + " starting_time = \"forecast_reference_time\"\n", + " (valid_dim,) = {dim for dim in ds.dims if \"time\" in dim} - {starting_time}\n", + " (year,) = set(ds[starting_time].dt.year.values)\n", + "\n", + " for dim, prefix in {starting_time: \"starting\", valid_dim: \"valid\"}.items():\n", + " ds = (\n", + " ds.groupby(f\"{dim}.month\")\n", + " .mean(keep_attrs=True)\n", + " .rename(month=f\"{prefix}_month\")\n", + " )\n", + " ds = ds.sel(starting_month=starting_month, valid_month=valid_month)\n", + " ds = ds.mean(\"realization\", keep_attrs=True)\n", + "\n", + " ds = diagnostics.regrid(ds, grid_out, **kwargs)\n", + " return ds.expand_dims(year=[year])\n", + "\n", + "def monthly_mean(ds):\n", + " for dim in ds.dims:\n", + " if \"time\" in dim:\n", + " ds = (\n", + " ds.groupby(f\"{dim}.month\")\n", + " .mean(keep_attrs=True)\n", + " .rename(month=dim.replace(\"time\", \"month\"))\n", + " )\n", + " return ds" + ] + }, + { + "cell_type": "markdown", + "id": "28cd53db-4ef8-4206-969a-91ff99fc854d", + "metadata": { + "papermill": { + "duration": 0.013468, + "end_time": "2024-03-08T17:24:12.206724", + "exception": false, + "start_time": "2024-03-08T17:24:12.193256", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 2. ERA5 data retrieval and area average`." + ] + }, + { + "cell_type": "markdown", + "id": "fc47c3cd-add8-478f-b9e0-d8f3c7b7a9a8", + "metadata": {}, + "source": [ + "The `regionalised_spatial_weighted_mean` is applied for all regions and all variables selected for download. All time series of the regionalised means are collected in the same xarray `ds_reanalysis`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cce21bc8-86fa-4384-9e70-7445c3d7c833", + "metadata": { + "papermill": { + "duration": 97.359229, + "end_time": "2024-03-08T17:25:49.579570", + "exception": false, + "start_time": "2024-03-08T17:24:12.220341", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Get the reanalysis data\n", + "datasets = []\n", + "for region in regions:\n", + " dataarrays = []\n", + " for variable in variables:\n", + " print(f\"{region=} {variable=}\")\n", + " ds = download.download_and_transform(\n", + " collection_id_reanalysis,\n", + " request_reanalysis | {\"variable\": variable},\n", + " chunks=chunks,\n", + " transform_func=regionalised_spatial_weighted_mean,\n", + " transform_func_kwargs={\n", + " \"region\": region,\n", + " \"defined_regions_module_name\": defined_regions_module_name,\n", + " \"weights\": True,\n", + " },\n", + " )\n", + " (da,) = ds.data_vars.values()\n", + " dataarrays.append(postprocess_dataarray(da.rename(variable)))\n", + " ds = xr.merge(dataarrays)\n", + " datasets.append(ds.expand_dims(region=[region]).compute())\n", + "ds_reanalysis = xr.concat(datasets, \"region\")\n", + "del datasets" + ] + }, + { + "cell_type": "markdown", + "id": "be4203dc-2f41-4d2f-a9bf-6dbbe9379e3a", + "metadata": { + "papermill": { + "duration": 0.041731, + "end_time": "2024-03-08T17:25:49.663137", + "exception": false, + "start_time": "2024-03-08T17:25:49.621406", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 3. Seasonal hindcast data retrieval and area average`." + ] + }, + { + "cell_type": "markdown", + "id": "c3018ee3-e0ca-4ea7-97dd-e7ec6344ea92", + "metadata": {}, + "source": [ + "The `regionalised_spatial_weighted_mean` is applied for all centres, regions and all variables selected for download. Missing variables are handled, so the download and transform process is not interrupted. All time series of the regionalised means are collected in the same xarray `ds_seasonal`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b3312e2-3f89-4b22-b5c7-a123cdb0ae8f", + "metadata": { + "papermill": { + "duration": 809.769413, + "end_time": "2024-03-08T17:39:19.472888", + "exception": false, + "start_time": "2024-03-08T17:25:49.703475", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 67%|██████▋ | 16/24 [00:01<00:00, 11.81it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 75%|███████▌ | 18/24 [00:01<00:00, 11.82it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 83%|████████▎ | 20/24 [00:01<00:00, 12.40it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 22/24 [00:01<00:00, 12.61it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 24/24 [00:01<00:00, 13.00it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 24/24 [00:01<00:00, 12.37it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Get the seasonal forecast data\n", + "datasets = []\n", + "for centre, request_kwargs in centres.items():\n", + " for region in regions:\n", + " dataarrays = []\n", + " for variable in variables:\n", + " print(f\"{centre=} {region=} {variable=}\")\n", + " if variable in missing_variables.get(centre, []):\n", + " print(\"SKIP\")\n", + " continue\n", + "\n", + " ds = download.download_and_transform(\n", + " collection_id_seasonal,\n", + " request_seasonal\n", + " | {\"originating_centre\": centre, \"variable\": variable}\n", + " | request_kwargs,\n", + " chunks=chunks,\n", + " transform_func=regionalised_spatial_weighted_mean,\n", + " transform_func_kwargs={\n", + " \"region\": region,\n", + " \"defined_regions_module_name\": defined_regions_module_name,\n", + " \"weights\": True,\n", + " \"mean_dims\": (\"realization\",),\n", + " },\n", + " backend_kwargs={\n", + " \"time_dims\": (\n", + " \"verifying_time\",\n", + " (\n", + " \"indexing_time\"\n", + " if centre in [\"ukmo\", \"jma\", \"ncep\"]\n", + " else \"time\"\n", + " ),\n", + " )\n", + " },\n", + " )\n", + " (da,) = ds.data_vars.values()\n", + " dataarrays.append(postprocess_dataarray(da.rename(variable)))\n", + " ds = xr.merge(dataarrays)\n", + " datasets.append(ds.expand_dims(centre=[centre], region=[region]).compute())\n", + "ds_seasonal = xr.merge(datasets)\n", + "del datasets" + ] + }, + { + "cell_type": "markdown", + "id": "6872e21c-b3dd-4f5a-af78-6febc003413a", + "metadata": {}, + "source": [ + "### Show a sample global map of temperature biases\n", + "\n", + "To reduce computation, this notebook simplifies the bias analysis by focusing on mean values of surface variables across the chosen regions. The goal is to offer a broad overview of the bias evolution over lead times. Thus, it is crucial that the selected regions exhibit sufficient homogeneity to minimize the compensation effects from systematic errors of varying sign.\n", + "\n", + "This section of the notebook offers a simple tool for exploring sample bias maps and confirming that bias sign remain relatively constant within selected regions. Additionally, it serves as an insight on the behavior of individual models summarized in the comprehensive tables provided later in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9af3e87-991b-4083-9ec8-e0d81249a896", + "metadata": { + "papermill": { + "duration": 9.833341, + "end_time": "2024-03-08T17:24:12.180097", + "exception": false, + "start_time": "2024-03-08T17:24:02.346756", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Set parameters\n", + "starting_month = 1\n", + "valid_month = 1\n", + "variable = \"2m_temperature\"\n", + "centre = \"cmcc\"\n", + "assert all(month in range(1, 13) for month in (starting_month, valid_month))\n", + "assert variable in variables\n", + "assert centre in centres\n", + "\n", + "# Get reanalysis map\n", + "ds = download.download_and_transform(\n", + " collection_id_reanalysis,\n", + " request_reanalysis | {\"variable\": variable},\n", + " chunks=chunks,\n", + " transform_chunks=False,\n", + " transform_func=diagnostics.monthly_weighted_mean,\n", + ")\n", + "ds = ds.rename(month=\"valid_month\").sel(valid_month=valid_month)\n", + "grid_out = ds[[\"latitude\", \"longitude\"]]\n", + "(da,) = ds.data_vars.values()\n", + "da_reanalysis = postprocess_dataarray(da.rename(variable))\n", + "\n", + "# Get seasonal map\n", + "ds = download.download_and_transform(\n", + " collection_id_seasonal,\n", + " request_seasonal\n", + " | {\"originating_centre\": centre, \"variable\": variable}\n", + " | centres[centre],\n", + " chunks=chunks,\n", + " transform_func=get_seasonal_map,\n", + " transform_func_kwargs={\n", + " \"starting_month\": starting_month,\n", + " \"valid_month\": valid_month,\n", + " \"grid_out\": grid_out,\n", + " \"method\": \"bilinear\",\n", + " },\n", + " backend_kwargs={\n", + " \"time_dims\": (\n", + " \"verifying_time\",\n", + " (\"indexing_time\" if centre in [\"ukmo\", \"jma\", \"ncep\"] else \"time\"),\n", + " )\n", + " },\n", + ")\n", + "ds = ds.mean(\"year\", keep_attrs=True)\n", + "(da,) = ds.data_vars.values()\n", + "da_seasonal = postprocess_dataarray(da.rename(variable))\n", + "\n", + "# Compute bias\n", + "with xr.set_options(keep_attrs=True):\n", + " bias = da_seasonal - da_reanalysis\n", + "bias.attrs[\"long_name\"] = \"Bias of \" + bias.attrs[\"long_name\"]\n", + "\n", + "# Plot\n", + "plot.projected_map(\n", + " bias, projection=ccrs.Robinson(), **plot_kwargs.get(variable, {}) | {\"robust\": True}\n", + ")\n", + "defined_regions[regions].plot(add_label=False)\n", + "_ = plt.title(f\"{centre=}\\n{starting_month=} {valid_month=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b75ca9b4-a55b-4cff-9d3d-ff30e3d10c9e", + "metadata": {}, + "source": [ + "### 4. Compute bias" + ] + }, + { + "cell_type": "markdown", + "id": "e64b5bc6-bd04-4116-ad51-f787a0c5b2df", + "metadata": {}, + "source": [ + "The the seasonal hindcast data are arranged in a four-dimensional array `ds_seasonal[i,j,n,k]`, where:\n", + "> * `i` is the reference start month of the forecast (from January to December)\n", + "> * `j` is the valid time of the forecast (from January to December)\n", + "> * `n` is the originating centre of the forecast (currently a maximum of 8)\n", + "> * `k` is the region (currently 8)\n", + "\n", + "Correspondingly, ERA5 data are arranged in a three-dimensional array `ds_seasonal[j,n,k]`, where:\n", + "> * `j` is the valid time of the forecast (from January to December)\n", + "> * `n` is the originating centre of the forecast (currently a maximum of 8)\n", + "> * `k` is the region (currently 8)\n", + "\n", + "With such a shaping and setup of the datasets `ds_seasonal` and `ds_reanalysis` the computation of the bias is straightforward." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4838e944-c810-4f11-9490-f6bb79b01948", + "metadata": { + "papermill": { + "duration": 0.538857, + "end_time": "2024-03-08T17:39:20.753777", + "exception": false, + "start_time": "2024-03-08T17:39:20.214920", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "ds_reanalysis = monthly_mean(ds_reanalysis)\n", + "ds_seasonal = monthly_mean(ds_seasonal)\n", + "\n", + "with xr.set_options(keep_attrs=True):\n", + " bias = ds_seasonal - ds_reanalysis\n", + "for da in bias.data_vars.values():\n", + " da.attrs[\"long_name\"] = \"Bias of \\n \" + da.attrs[\"long_name\"]\n", + "# Avoid ticks interpolation\n", + "bias = bias.assign_coords(\n", + " {name: coord.astype(str) for name, coord in bias.coords.items()}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b0e8053e-c581-4178-924f-2f219717d53f", + "metadata": { + "papermill": { + "duration": 0.245188, + "end_time": "2024-03-08T17:39:21.277354", + "exception": false, + "start_time": "2024-03-08T17:39:21.032166", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 5. Plot and describe results" + ] + }, + { + "cell_type": "markdown", + "id": "be002356-1692-4e24-bbfc-a152d249cd15", + "metadata": { + "papermill": { + "duration": 0.245188, + "end_time": "2024-03-08T17:39:21.277354", + "exception": false, + "start_time": "2024-03-08T17:39:21.032166", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "#### How to read the results\n", + "The following tables summarise the monthly bias for chosen C3S seasonal systems (labelled by producing centre) and selected areas. \n", + "\n", + "In each panel, the horizontal axis represents the validity period of the forecast, while the vertical axis shows the corresponding start dates. Thus, each horizontal strip of coloured tiles shows the evolution of the mean temperature bias over the six-month period of validity of each forecast.\n", + "\n", + "Reading each panel from bottom to top, the strips of coloured tiles are progressively shifted by one month, corresponding to the same shift in the starting date of the forecast. The horizontal axis is periodic: forecasts starting after August have their last months on the left side of the panel." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "16dd5dc1-6fce-481b-a514-c600fd535c59", + "metadata": { + "papermill": { + "duration": 0.547082, + "end_time": "2024-03-08T17:39:22.068303", + "exception": false, + "start_time": "2024-03-08T17:39:21.521221", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "centre = \"ecmwf\"\n", + "region = \"WNA\"\n", + "ax = bias[\"2m_temperature\"].sel(centre=centre,region=region).plot(x=\"valid_month\",cmap=\"RdBu_r\", levels = np.arange(-4, 4.5, .5))\n", + "_ = plt.title(f\"centre: {centre} - region: {region}\", y=1.01)" + ] + }, + { + "cell_type": "markdown", + "id": "6eed6e75-1a53-4aca-84c6-35d03e136775", + "metadata": { + "papermill": { + "duration": 0.240481, + "end_time": "2024-03-08T17:39:22.596548", + "exception": false, + "start_time": "2024-03-08T17:39:22.356067", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "#### Discussion\n", + "\n", + "The tables below summarise the monthly bias for all originating centres (columns) and for all selected areas (rows). Note that only one system (system 3) is shown from the ECCC contributions.\n", + "\n", + "**Temperature** - All seasonal forecast models produce a significant temperature bias over most of the regions selected for analysis. Two centres, DWD and METEOFRANCE, produce a forecast with a predominant warm bias over most regions and in all seasons. The temperature bias is a function of both the start time and the valid time of the forecast, with a slightly stronger dependence on the valid time. Therefore, the bias does not increase with the lead time of the forecast. Instead, it tends to be a characteristic aspect of each model over the specific region and time of year. The seasonal cycle of the bias is particularly evident over western North America.\n", + "\n", + "**Precipitation** - As in the case of temperature, the bias in precipitation tends to be more dependent on the validity period of the forecasts. The regions with the largest systematic bias are northeastern Brazil and southern Asia, where all models tend to overestimate the amplitude of the seasonal cycle.\n", + "\n", + "**Wind speed** - Unlike temperature and precipitation, the bias of wind speed does not show a significant seasonal cycle over most of the selected regions. The only exceptions are northeastern Brazil and southern Asia, where some models (e.g. ECCC and NCEP) show a positive bias during the months of June to September.\n", + "\n", + "**Dew point temperature** - As in the case of wind speed, the dew point temperature bias tends to have a weaker seasonal cycle than temperature and precipitation. However, some regions (southern Asia and western North America) show a slightly stronger seasonal dependence of the bias. NOTE: Dew point temperature is not available for ECCC." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cb4e0a7c-29d2-44ea-8c64-ff2ad1b02cd8", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_kwargs = {\n", + " \"2m_temperature\": {\"cmap\": \"RdBu_r\", \"levels\": np.arange(-4, 4.5, .5), \"extend\": \"both\"},\n", + " \"total_precipitation\": {\"cmap\": \"BrBG\", \"levels\": np.arange(-140, 160, 20), \"extend\": \"both\"},\n", + " \"10m_wind_speed\": {\"cmap\": \"PuOr\", \"levels\": np.arange(-1.4, 1.5, .2), \"extend\": \"both\"},\n", + " \"2m_dewpoint_temperature\": {\"cmap\": \"BrBG\", \"levels\": np.arange(-6, 6.5, .5), \"extend\": \"both\"},\n", + "}\n", + "\n", + "cbar_width = cbar_pad = 0.02\n", + "for varname, da in bias.data_vars.items():\n", + " facet = da.plot(\n", + " col=\"centre\", row=\"region\", x=\"valid_month\", add_colorbar=False, **plot_kwargs.get(varname, {})\n", + " )\n", + " facet.fig.suptitle(f\"Seasonal forecast: {da.attrs['long_name']}\", y=1.02, fontsize=24, fontweight='bold')\n", + " facet.set_titles(fontsize=16)\n", + " \n", + " # Create a colorbar for each row\n", + " for ax in facet.axs[:, -1]:\n", + " pos = ax.get_position()\n", + " cax = facet.fig.add_axes([pos.x1 + cbar_pad, pos.y0, cbar_width, pos.height])\n", + " facet.fig.colorbar(\n", + " ax.collections[0],\n", + " cax=cax,\n", + " label=f\"[{da.attrs['units']}]\"\n", + " )\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f3ac7f64-d375-4e4a-8a2e-9833a1305772", + "metadata": { + "papermill": { + "duration": 0.258521, + "end_time": "2024-03-08T17:40:01.124130", + "exception": false, + "start_time": "2024-03-08T17:40:00.865609", + "status": "completed" + }, + "tags": [] + }, + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cc9c7839-229e-4b13-8b8a-418b93d5ebb8", + "metadata": { + "papermill": { + "duration": 0.271597, + "end_time": "2024-03-08T17:40:01.664067", + "exception": false, + "start_time": "2024-03-08T17:40:01.392470", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## References\n", + "\n", + "[[1]](https://www.nature.com/articles/s41598-018-20628-2) Rodriguez, D., De Voil, P., Hudson, D., Brown, J. N., Hayman, P., Marrou, H., & Meinke, H. (2018). Predicting optimum crop designs using crop models and seasonal climate forecasts. Scientific reports, 8(1), 2231.\n", + "\n", + "[[2]](https://www.sciencedirect.com/science/article/pii/S0168192318303836) Jha, P. K., Athanasiadis, P., Gualdi, S., Trabucco, A., Mereu, V., Shelia, V., & Hoogenboom, G. (2019). Using daily data from seasonal forecasts in dynamic crop models for yield prediction: A case study for rice in Nepal’s Terai. Agricultural and forest meteorology, 265, 349-358.\n", + "\n", + "[[3]](https://www.sciencedirect.com/science/article/pii/S2405880722000425) Dainelli, R., Calmanti, S., Pasqui, M., Rocchi, L., Di Giuseppe, E., Monotti, C., ... & Toscano, P. (2022). Moving climate seasonal forecasts information from useful to usable for early within-season predictions of durum wheat yield. Climate Services, 28, 100324.\n", + "\n", + "[[4]]() Lledó, L., Torralba, V., Soret, A., Ramon, J., & Doblas-Reyes, F. J. (2019). Seasonal forecasts of wind power generation. Renewable Energy, 143, 91-100.\n", + "\n", + "[[5]](https://link.springer.com/article/10.1007/s00382-021-05828-3) Prodhomme, C., Materia, S., Ardilouze, C., White, R. H., Batté, L., Guemas, V., ... & García-Serrano, J. (2021). Seasonal prediction of European summer heatwaves. Climate Dynamics, 1-18.\n", + "\n", + "[[6]](https://doi.org/10.1175/JHM-D-19-0042.1) Li, W., Chen, J. I. E., Li, L. U., Chen, H. U. A., Liu, B., Xu, C. Y., & Li, X. (2019). Evaluation and bias correction of S2S precipitation for hydrological extremes. Journal of Hydrometeorology, 20(9), 1887-1906.\n", + "\n", + "[[7]](https://link.springer.com/article/10.1007/s00382-019-04640-4) Manzanas, R. (2020). Assessment of model drifts in seasonal forecasting: Sensitivity to ensemble size and implications for bias correction. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001751.\n", + "\n", + "[[8]](https://link.springer.com/article/10.1007/s00382-017-3962-9) Hermanson, L., Ren, H. L., Vellinga, M., Dunstone, N. D., Hyder, P., Ineson, S., ... & Williams, K. D. (2018). Different types of drifts in two seasonal forecast systems and their dependence on ENSO. Climate dynamics, 51, 1411-1426.\n", + "\n", + "[[9]](https://link.springer.com/article/10.1007/s00382-020-05314-2) Hemri, S., Bhend, J., Liniger, M. A., Manzanas, R., Siegert, S., Stephenson, D. B., ... & Doblas-Reyes, F. J. (2020). How to create an operational multi-model of seasonal forecasts?. Climate Dynamics, 55, 1141-1157.\n", + "\n", + "[[10]](https://link.springer.com/article/10.1007/s00382-019-04640-4) Manzanas, R., Gutiérrez, J. M., Bhend, J., Hemri, S., Doblas-Reyes, F. J., Torralba, V., ... & Brookshaw, A. (2019). Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset. Climate Dynamics, 53, 1287-1305.\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.8" + }, + "papermill": { + "default_parameters": {}, + "duration": 968.520731, + "end_time": "2024-03-08T17:40:03.783430", + "environment_variables": {}, + "exception": null, + "input_path": "D520.3.2.3b.SEASONAL_multimodel-bias_v5.ipynb", + "output_path": "output.ipynb", + "parameters": {}, + "start_time": "2024-03-08T17:23:55.262699", + "version": "2.4.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q13.ipynb b/Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q13.ipynb new file mode 100644 index 00000000..faa654dd --- /dev/null +++ b/Seasonal_Forecasts/seasonal_seasonal-monthly-single-levels_forecast-skill_q13.ipynb @@ -0,0 +1,1089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5bda21ce-fe22-486d-bdb0-d7450216d405", + "metadata": { + "papermill": { + "duration": 0.008751, + "end_time": "2024-03-08T17:23:56.435664", + "exception": false, + "start_time": "2024-03-08T17:23:56.426913", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Use case: Risk assessment in agriculture using seasonal forecasts as an input for climate impact models.\n", + "\n", + "## Quality assessment questions\n", + "* Do I have to correct systematic errors in temperature and precipitation before using seasonal forecasts as an input for my crop model?\n", + "* Can I assume that climate models always produce the same systematic error for seasonal forecasts over a certain area? " + ] + }, + { + "attachments": { + "65ac1e47-560f-45d9-b5eb-eaf0388326cb.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "2590e7f4-d6b3-4eed-a91f-c94a78df2db5", + "metadata": {}, + "source": [ + "Seasonal forecasts can be used as an input to crop models for agricultural risk assessment [[1]](https://www.nature.com/articles/s41598-018-20628-2) [[2]](https://www.sciencedirect.com/science/article/pii/S0168192318303836) [[3]](https://www.sciencedirect.com/science/article/pii/S2405880722000425). However, the presence of systematic errors can undermine the performance of crop models in a number of ways, particularly when critical thresholds in the plant growth process are considered. Similar problems can arise in other sectoral applications, such as the management of renewable energy facilities [[4]](https://www.sciencedirect.com/science/article/pii/S0960148119306196), or the alerting of national health services for the occurrence of summer heat waves [[5]](https://link.springer.com/article/10.1007/s00382-021-05828-3). In addition to temperature and precipitation, the most commonly used climate variables for risk assessment in all sectoral applications, the notebook also considers wind speed and dew point temperature. While these variables are commonly used in agricultural applications to calculate potential evapotranspiration, the results of the assessment are also valuable for several other sectors.\n", + "\n", + "## Quality assessment statement\n", + "\n", + "* Seasonal forecasts should be bias-corrected before being used as input to climate impact models. Depending on the impact model, it might be advisable to bias-correct the resulting impact indicator [[6]](https://doi.org/10.1175/JHM-D-19-0042.1)\n", + "* Model drifts and bias tend to be a complex function of both the start time of the forecast and of its valid time, with a slightly stronger dependence on the valid time for the case of temperature and precipitation. Therefore, the most appropriate approach to produce unbiased forecasts for use in impact models must be selected on a case-by-case basis.[[7]](https://link.springer.com/article/10.1007/s00382-019-04640-4) [[8]](https://link.springer.com/article/10.1007/s00382-017-3962-9)\n", + "* For the implementation of bias correction procedures, it is recommended that reference climatologies are computed for time scales no longer than one month, since the typical model bias changes on a monthly basis. Instead, the correction of model biases on a seasonal (i.e. three months) timescale may lead to a loss of quality.\n", + "* Similarly, the calculation of multi-model statistics requires a recalibration of the model output in order to align all models around the same climatological distribution. [[9]](https://link.springer.com/article/10.1007/s00382-020-05314-2)\n", + "\n", + "![image.png](attachment:65ac1e47-560f-45d9-b5eb-eaf0388326cb.png)\n", + "\n", + "The figure shows a sample of the ECMWF monthly temperature bias for Western North America as a function of the starting month and of the valid month of the forecast. More details on how to interpret the results are available below in the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "c10778f3-052d-4b4b-ba3c-f8c8dcec2232", + "metadata": {}, + "source": [ + "## Methodology\n", + "\n", + "This notebook provides a comprehensive assessment of the systematic errors of the forecast systems by comparing the model predictions with the ERA5 reanalysis. The analysis is carried out for eight regions selected from those used in the IPCC-AR6 (see figure below), which capture the spatial scale of the main systematic errors already analysed in previous studies [[10]](https://link.springer.com/article/10.1007/s00382-019-04640-4)\n", + "\n", + "The analysis and results are organised in the following steps, which are detailed in the sections below:\n", + "\n", + "**1. Choose the data to use and setup code:**\n", + " * Import required packages\n", + " * Define parameters describing regions, variables, and data\n", + " * Plot the chosen regions\n", + " * Define required functions\n", + " * Show a sample global map of temperature biases\n", + "\n", + "**2. ERA5 data retrieval and area average:**\n", + " * Retrieve monthly ERA5 data and compute the regional mean climatology, i.e. 12 values for each area\n", + " * Compute area averages\n", + "\n", + "**3. Seasonal hindcast retrieval and area average:**\n", + " * Retrieve monthly seasonal forecast data from all available centres and for all starting dates, compute the monthly mean climatology (i.e. 6 values, since each forecast is valid for 6 months) and subtract the corresponding ERA5 climatology.\n", + " * Compute area averages\n", + " * Plot a sample map\n", + "\n", + "**4. Compute the hindcast bias**\n", + "\n", + "**5. Plot and describe results:** \n", + " * Plots are shown in a comprehensive set of panels that provide an overview of the multimodel performance \n", + " * The results are summarised and discussed\n", + "\n", + "Note that crop models and other sectoral applications tend to use daily values rather than the monthly statistics considered in this notebook, and they focus on smaller areas compared to the regions considered below. However, the analysis of monthly data over the IPCC-AR6 regions provides a useful overview of the overall characteristics of the dataset.\n", + "\n", + "Note that for ECCC, only one of the two systems has been analysed (system 3). This is different from the approach taken in the C3S charts, and CanSIPSv2.1, where both systems are combined (system 2 and system 3)." + ] + }, + { + "cell_type": "markdown", + "id": "93a36ba7-11f3-43f2-9cea-2eff221f68ec", + "metadata": {}, + "source": [ + "## Analysis and results" + ] + }, + { + "cell_type": "markdown", + "id": "368e01c6", + "metadata": { + "papermill": { + "duration": 0.007051, + "end_time": "2024-03-08T17:23:56.492658", + "exception": false, + "start_time": "2024-03-08T17:23:56.485607", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 1. Choose the data to use and setup the code." + ] + }, + { + "cell_type": "markdown", + "id": "e798c526-d227-4011-8410-dd8931790be6", + "metadata": {}, + "source": [ + "#### Import required packages\n", + "We use the `regionmask package` to extract climatologies for a few selected regiones that have already e been adopted in several climate studies, including the analysis presented in the latest IPCC report.\n", + "\n", + "A description of the regions adopted for climate assesements in the scientific literature is available [here](https://regionmask.readthedocs.io/en/stable/defined_scientific.htm)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "48246700", + "metadata": { + "papermill": { + "duration": 3.749769, + "end_time": "2024-03-08T17:24:00.248720", + "exception": false, + "start_time": "2024-03-08T17:23:56.498951", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "import regionmask\n", + "import xarray as xr\n", + "import numpy as np\n", + "from c3s_eqc_automatic_quality_control import diagnostics, download, plot\n", + "\n", + "plt.style.use(\"seaborn-v0_8-notebook\")" + ] + }, + { + "cell_type": "markdown", + "id": "49f2816d", + "metadata": { + "papermill": { + "duration": 0.006416, + "end_time": "2024-03-08T17:24:00.262100", + "exception": false, + "start_time": "2024-03-08T17:24:00.255684", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "#### Define parameters describing regions, variables, and data\n", + "\n", + "In this notebook the analysis of the model bias is done for 5 variables from all 8 originating centres. The SREX regions selected for the analysis are:\n", + "\n", + "- NEAF: Northern-East Africa\n", + "- ENA: Eastern North America\n", + "- MED: Southern Europe/Mediterranean\n", + "- NEB: North-eastern Brazil\n", + "- SAS: Southern Asia\n", + "- SEA: South-Eastern Asia\n", + "- WNA: Western North America\n", + "- NWS: Western Coast of South America\n", + "\n", + "A `common_request` is defined in order to cover the entire global domain and the entire hindcast period from 1993 to 2016. Specific data requests are then defined for the reanalysis and for the seasonal forecasts, so that all available months and lead times are retrieved for the analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "348d6273-3e42-4dce-90bb-5b0f5628d356", + "metadata": { + "papermill": { + "duration": 0.016612, + "end_time": "2024-03-08T17:24:00.285147", + "exception": false, + "start_time": "2024-03-08T17:24:00.268535", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Time\n", + "year_start_hindcast = 1993\n", + "year_stop_hindcast = 2016\n", + "\n", + "# Variable\n", + "variables = [\n", + " \"2m_temperature\",\n", + " \"total_precipitation\",\n", + " \"10m_wind_speed\",\n", + " \"2m_dewpoint_temperature\",\n", + "]\n", + "\n", + "# Define centres with missing variables\n", + "missing_variables = {\"eccc\": [\"2m_dewpoint_temperature\"]}\n", + "\n", + "# Centres\n", + "centres = {\n", + " \"cmcc\": {\"system\": \"35\"},\n", + " \"dwd\": {\"system\": \"21\"},\n", + " \"eccc\": {\"system\": \"3\"},\n", + " \"ecmwf\": {\"system\": \"51\"},\n", + " \"jma\": {\"system\": \"3\"},\n", + " \"meteo_france\": {\"system\": \"8\"},\n", + " \"ncep\": {\"system\": \"2\"},\n", + " \"ukmo\": {\"system\": \"601\"},\n", + "}\n", + "assert all(\"system\" in v for v in centres.values())\n", + "assert set(centres) <= {\n", + " \"cmcc\",\n", + " \"dwd\",\n", + " \"eccc\",\n", + " \"ecmwf\",\n", + " \"jma\",\n", + " \"meteo_france\",\n", + " \"ncep\",\n", + " \"ukmo\",\n", + "}\n", + "\n", + "# Regions\n", + "defined_regions_module_name = \"ar6.land\"\n", + "regions = [\n", + " \"NWS\",\n", + " \"ENA\",\n", + " \"MED\",\n", + " \"NEAF\",\n", + " \"NES\",\n", + " \"SAS\",\n", + " \"SEA\",\n", + " \"WNA\",\n", + "]\n", + "\n", + "# Ploting settings\n", + "plot_kwargs = {\n", + " \"total_precipitation\": {\"cmap\": \"BrBG\"},\n", + "}\n", + "\n", + "\n", + "# Define data requests\n", + "chunks = {\"year\": 1, \"variable\": 1}\n", + "common_request = {\n", + " \"format\": \"grib\",\n", + " \"area\": [89.5, -179.5, -89.5, 179.5],\n", + " \"year\": [str(year) for year in range(year_start_hindcast, year_stop_hindcast + 1)],\n", + "}\n", + "\n", + "collection_id_reanalysis = \"reanalysis-era5-single-levels-monthly-means\"\n", + "request_reanalysis = common_request | {\n", + " \"product_type\": \"monthly_averaged_reanalysis\",\n", + " \"month\": [f\"{month:02d}\" for month in range(1, 13)],\n", + " \"time\": \"00:00\",\n", + "}\n", + "\n", + "collection_id_seasonal = \"seasonal-monthly-single-levels\"\n", + "request_seasonal = common_request | {\n", + " \"product_type\": \"monthly_mean\",\n", + " \"leadtime_month\": list(map(str, range(1, 7))),\n", + " \"month\": [f\"{month:02d}\" for month in range(1, 13)],\n", + " \"grid\": \"1/1\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c0e897d5-0572-4653-8515-b3100d03348d", + "metadata": {}, + "source": [ + "#### Plot the chosen regions\n", + "For the bias analysis we use 8 out of the 45 regions defined in the `regionmask` package in order to assess the systematic errors of the modelling systems under different climatic regimes (tropical, extratropical, continental, maritime) with a number of cases maneageable within the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2686a074-0698-4dee-82bf-8cf0f161e0af", + "metadata": { + "papermill": { + "duration": 1.932335, + "end_time": "2024-03-08T17:24:02.237426", + "exception": false, + "start_time": "2024-03-08T17:24:00.305091", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_defined_regions(defined_regions_module_name):\n", + " defined_regions = regionmask.defined_regions\n", + " for module in defined_regions_module_name.split(\".\"):\n", + " defined_regions = getattr(defined_regions, module)\n", + " return defined_regions\n", + "\n", + "\n", + "defined_regions = get_defined_regions(defined_regions_module_name)\n", + "assert set(regions) <= set(defined_regions.abbrevs)\n", + "\n", + "ax = defined_regions[regions].plot(\n", + " projection=ccrs.Robinson(),\n", + " label=\"abbrev\",\n", + " add_ocean=True,\n", + ")\n", + "ax.set_global()" + ] + }, + { + "cell_type": "markdown", + "id": "e5ee547d-b9a3-4972-913a-a9563ab729b6", + "metadata": {}, + "source": [ + "#### Define the required functions\n", + "\n", + "- The `regionalised_spatial_weighted_mean` function extracts the regional means over the selected domains. It uses spatial weighting to account for the latitudinal dependence of the grid size in the lon-lat grids used for the reanalysis and for the forecast models. The bias is then calculated later in the notebook by simply subtracting the regional means.\n", + "\n", + "- The `postprocess_dataarray` function performs two important postprocessings operations required to align the arrays containing the reanalsysis and the forecast data. \n", + "\n", + " - The time dimensions are renamed to allow the broadcasting of the dimension `valid_time`, which is used to compute the bias, independently of the corresponding `lead_times` in the forecast. \n", + "\n", + " - Furthermore, the cumulative variables have to be rescaled in order to make ERA5 data and seasonal forecast data comparable on a monthly timescale. A [conversion table](https://confluence.ecmwf.int/pages/viewpage.action?pageId=197702790) for accumulated variables is available in the [Copernicus Knowledge Base](https://confluence.ecmwf.int/pages/viewpage.action?pageId=55116796) repository.\n", + "\n", + "- The function `get_seasonal_maps` is used to derive a sample bias map to illustrate how the analysis is performed.\n", + "\n", + "- The `monthly_mean` function will be used to derive the monthly values at the appropriate `monthly_mean` before calculating the bias." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a3aa1b96-f0fc-4cd2-810c-6a32a4eae0ea", + "metadata": { + "papermill": { + "duration": 0.022708, + "end_time": "2024-03-08T17:24:02.323620", + "exception": false, + "start_time": "2024-03-08T17:24:02.300912", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def regionalised_spatial_weighted_mean(\n", + " ds, region, defined_regions_module_name, weights, mean_dims=None\n", + "):\n", + " defined_regions = get_defined_regions(defined_regions_module_name)\n", + " mask = defined_regions.mask(ds)\n", + " index = defined_regions.map_keys(region)\n", + " ds = ds.where((mask == index).compute(), drop=True)\n", + " ds = diagnostics.spatial_weighted_mean(ds, weights=weights)\n", + " if mean_dims:\n", + " return ds.mean(mean_dims, keep_attrs=True)\n", + " return ds\n", + "\n", + "\n", + "def postprocess_dataarray(da):\n", + " time_dims = {dim for dim in da.dims if \"time\" in dim}\n", + " if time_dims:\n", + " if len(time_dims) == 1:\n", + " (valid_time,) = time_dims\n", + " else:\n", + " starting_time = \"forecast_reference_time\"\n", + " (valid_time,) = time_dims - {starting_time}\n", + " da = da.rename({starting_time: \"starting_time\"})\n", + " da = da.rename({valid_time: \"valid_time\"})\n", + "\n", + " if \"leadtime\" in da.coords:\n", + " da = da.drop_vars(\"leadtime\")\n", + "\n", + " # Convert units\n", + " name = da.name\n", + " units = da.attrs[\"units\"]\n", + " m_to_mm = 1.0e3\n", + " day_to_s = 60 * 60 * 24\n", + " if name == \"total_precipitation\" and units != \"mm/month\":\n", + " match units:\n", + " case \"m\":\n", + " factor = m_to_mm\n", + " case \"m s**-1\":\n", + " factor = m_to_mm * day_to_s\n", + " case _:\n", + " raise ValueError(f\"{name=} {units=}\")\n", + " if \"valid_time\" in da.coords:\n", + " factor *= da[\"valid_time\"].dt.days_in_month\n", + " units = \"mm/month\"\n", + " else:\n", + " units = \"mm/day\"\n", + " elif name == \"surface_solar_radiation_downwards\" and units != \"W m**-2\":\n", + " match units:\n", + " case \"J m**-2\":\n", + " factor = 1 / day_to_s\n", + " units = \"W m**-2\"\n", + " case _:\n", + " raise ValueError(f\"{name=} {units=}\")\n", + " else:\n", + " return da\n", + "\n", + " with xr.set_options(keep_attrs=True):\n", + " da *= factor\n", + " da.attrs[\"units\"] = units\n", + " return da\n", + "\n", + "\n", + "def get_seasonal_map(ds, starting_month, valid_month, grid_out, **kwargs):\n", + " starting_time = \"forecast_reference_time\"\n", + " (valid_dim,) = {dim for dim in ds.dims if \"time\" in dim} - {starting_time}\n", + " (year,) = set(ds[starting_time].dt.year.values)\n", + "\n", + " for dim, prefix in {starting_time: \"starting\", valid_dim: \"valid\"}.items():\n", + " ds = (\n", + " ds.groupby(f\"{dim}.month\")\n", + " .mean(keep_attrs=True)\n", + " .rename(month=f\"{prefix}_month\")\n", + " )\n", + " ds = ds.sel(starting_month=starting_month, valid_month=valid_month)\n", + " ds = ds.mean(\"realization\", keep_attrs=True)\n", + "\n", + " ds = diagnostics.regrid(ds, grid_out, **kwargs)\n", + " return ds.expand_dims(year=[year])\n", + "\n", + "def monthly_mean(ds):\n", + " for dim in ds.dims:\n", + " if \"time\" in dim:\n", + " ds = (\n", + " ds.groupby(f\"{dim}.month\")\n", + " .mean(keep_attrs=True)\n", + " .rename(month=dim.replace(\"time\", \"month\"))\n", + " )\n", + " return ds" + ] + }, + { + "cell_type": "markdown", + "id": "28cd53db-4ef8-4206-969a-91ff99fc854d", + "metadata": { + "papermill": { + "duration": 0.013468, + "end_time": "2024-03-08T17:24:12.206724", + "exception": false, + "start_time": "2024-03-08T17:24:12.193256", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 2. ERA5 data retrieval and area average`." + ] + }, + { + "cell_type": "markdown", + "id": "fc47c3cd-add8-478f-b9e0-d8f3c7b7a9a8", + "metadata": {}, + "source": [ + "The `regionalised_spatial_weighted_mean` is applied for all regions and all variables selected for download. All time series of the regionalised means are collected in the same xarray `ds_reanalysis`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cce21bc8-86fa-4384-9e70-7445c3d7c833", + "metadata": { + "papermill": { + "duration": 97.359229, + "end_time": "2024-03-08T17:25:49.579570", + "exception": false, + "start_time": "2024-03-08T17:24:12.220341", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Get the reanalysis data\n", + "datasets = []\n", + "for region in regions:\n", + " dataarrays = []\n", + " for variable in variables:\n", + " print(f\"{region=} {variable=}\")\n", + " ds = download.download_and_transform(\n", + " collection_id_reanalysis,\n", + " request_reanalysis | {\"variable\": variable},\n", + " chunks=chunks,\n", + " transform_func=regionalised_spatial_weighted_mean,\n", + " transform_func_kwargs={\n", + " \"region\": region,\n", + " \"defined_regions_module_name\": defined_regions_module_name,\n", + " \"weights\": True,\n", + " },\n", + " )\n", + " (da,) = ds.data_vars.values()\n", + " dataarrays.append(postprocess_dataarray(da.rename(variable)))\n", + " ds = xr.merge(dataarrays)\n", + " datasets.append(ds.expand_dims(region=[region]).compute())\n", + "ds_reanalysis = xr.concat(datasets, \"region\")\n", + "del datasets" + ] + }, + { + "cell_type": "markdown", + "id": "be4203dc-2f41-4d2f-a9bf-6dbbe9379e3a", + "metadata": { + "papermill": { + "duration": 0.041731, + "end_time": "2024-03-08T17:25:49.663137", + "exception": false, + "start_time": "2024-03-08T17:25:49.621406", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 3. Seasonal hindcast data retrieval and area average`." + ] + }, + { + "cell_type": "markdown", + "id": "c3018ee3-e0ca-4ea7-97dd-e7ec6344ea92", + "metadata": {}, + "source": [ + "The `regionalised_spatial_weighted_mean` is applied for all centres, regions and all variables selected for download. Missing variables are handled, so the download and transform process is not interrupted. All time series of the regionalised means are collected in the same xarray `ds_seasonal`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b3312e2-3f89-4b22-b5c7-a123cdb0ae8f", + "metadata": { + "papermill": { + "duration": 809.769413, + "end_time": "2024-03-08T17:39:19.472888", + "exception": false, + "start_time": "2024-03-08T17:25:49.703475", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 67%|██████▋ | 16/24 [00:01<00:00, 11.81it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 75%|███████▌ | 18/24 [00:01<00:00, 11.82it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 83%|████████▎ | 20/24 [00:01<00:00, 12.40it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 22/24 [00:01<00:00, 12.61it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 24/24 [00:01<00:00, 13.00it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 24/24 [00:01<00:00, 12.37it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Get the seasonal forecast data\n", + "datasets = []\n", + "for centre, request_kwargs in centres.items():\n", + " for region in regions:\n", + " dataarrays = []\n", + " for variable in variables:\n", + " print(f\"{centre=} {region=} {variable=}\")\n", + " if variable in missing_variables.get(centre, []):\n", + " print(\"SKIP\")\n", + " continue\n", + "\n", + " ds = download.download_and_transform(\n", + " collection_id_seasonal,\n", + " request_seasonal\n", + " | {\"originating_centre\": centre, \"variable\": variable}\n", + " | request_kwargs,\n", + " chunks=chunks,\n", + " transform_func=regionalised_spatial_weighted_mean,\n", + " transform_func_kwargs={\n", + " \"region\": region,\n", + " \"defined_regions_module_name\": defined_regions_module_name,\n", + " \"weights\": True,\n", + " \"mean_dims\": (\"realization\",),\n", + " },\n", + " backend_kwargs={\n", + " \"time_dims\": (\n", + " \"verifying_time\",\n", + " (\n", + " \"indexing_time\"\n", + " if centre in [\"ukmo\", \"jma\", \"ncep\"]\n", + " else \"time\"\n", + " ),\n", + " )\n", + " },\n", + " )\n", + " (da,) = ds.data_vars.values()\n", + " dataarrays.append(postprocess_dataarray(da.rename(variable)))\n", + " ds = xr.merge(dataarrays)\n", + " datasets.append(ds.expand_dims(centre=[centre], region=[region]).compute())\n", + "ds_seasonal = xr.merge(datasets)\n", + "del datasets" + ] + }, + { + "cell_type": "markdown", + "id": "6872e21c-b3dd-4f5a-af78-6febc003413a", + "metadata": {}, + "source": [ + "### Show a sample global map of temperature biases\n", + "\n", + "To reduce computation, this notebook simplifies the bias analysis by focusing on mean values of surface variables across the chosen regions. The goal is to offer a broad overview of the bias evolution over lead times. Thus, it is crucial that the selected regions exhibit sufficient homogeneity to minimize the compensation effects from systematic errors of varying sign.\n", + "\n", + "This section of the notebook offers a simple tool for exploring sample bias maps and confirming that bias sign remain relatively constant within selected regions. Additionally, it serves as an insight on the behavior of individual models summarized in the comprehensive tables provided later in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9af3e87-991b-4083-9ec8-e0d81249a896", + "metadata": { + "papermill": { + "duration": 9.833341, + "end_time": "2024-03-08T17:24:12.180097", + "exception": false, + "start_time": "2024-03-08T17:24:02.346756", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Set parameters\n", + "starting_month = 1\n", + "valid_month = 1\n", + "variable = \"2m_temperature\"\n", + "centre = \"cmcc\"\n", + "assert all(month in range(1, 13) for month in (starting_month, valid_month))\n", + "assert variable in variables\n", + "assert centre in centres\n", + "\n", + "# Get reanalysis map\n", + "ds = download.download_and_transform(\n", + " collection_id_reanalysis,\n", + " request_reanalysis | {\"variable\": variable},\n", + " chunks=chunks,\n", + " transform_chunks=False,\n", + " transform_func=diagnostics.monthly_weighted_mean,\n", + ")\n", + "ds = ds.rename(month=\"valid_month\").sel(valid_month=valid_month)\n", + "grid_out = ds[[\"latitude\", \"longitude\"]]\n", + "(da,) = ds.data_vars.values()\n", + "da_reanalysis = postprocess_dataarray(da.rename(variable))\n", + "\n", + "# Get seasonal map\n", + "ds = download.download_and_transform(\n", + " collection_id_seasonal,\n", + " request_seasonal\n", + " | {\"originating_centre\": centre, \"variable\": variable}\n", + " | centres[centre],\n", + " chunks=chunks,\n", + " transform_func=get_seasonal_map,\n", + " transform_func_kwargs={\n", + " \"starting_month\": starting_month,\n", + " \"valid_month\": valid_month,\n", + " \"grid_out\": grid_out,\n", + " \"method\": \"bilinear\",\n", + " },\n", + " backend_kwargs={\n", + " \"time_dims\": (\n", + " \"verifying_time\",\n", + " (\"indexing_time\" if centre in [\"ukmo\", \"jma\", \"ncep\"] else \"time\"),\n", + " )\n", + " },\n", + ")\n", + "ds = ds.mean(\"year\", keep_attrs=True)\n", + "(da,) = ds.data_vars.values()\n", + "da_seasonal = postprocess_dataarray(da.rename(variable))\n", + "\n", + "# Compute bias\n", + "with xr.set_options(keep_attrs=True):\n", + " bias = da_seasonal - da_reanalysis\n", + "bias.attrs[\"long_name\"] = \"Bias of \" + bias.attrs[\"long_name\"]\n", + "\n", + "# Plot\n", + "plot.projected_map(\n", + " bias, projection=ccrs.Robinson(), **plot_kwargs.get(variable, {}) | {\"robust\": True}\n", + ")\n", + "defined_regions[regions].plot(add_label=False)\n", + "_ = plt.title(f\"{centre=}\\n{starting_month=} {valid_month=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b75ca9b4-a55b-4cff-9d3d-ff30e3d10c9e", + "metadata": {}, + "source": [ + "### 4. Compute bias" + ] + }, + { + "cell_type": "markdown", + "id": "e64b5bc6-bd04-4116-ad51-f787a0c5b2df", + "metadata": {}, + "source": [ + "The the seasonal hindcast data are arranged in a four-dimensional array `ds_seasonal[i,j,n,k]`, where:\n", + "> * `i` is the reference start month of the forecast (from January to December)\n", + "> * `j` is the valid time of the forecast (from January to December)\n", + "> * `n` is the originating centre of the forecast (currently a maximum of 8)\n", + "> * `k` is the region (currently 8)\n", + "\n", + "Correspondingly, ERA5 data are arranged in a three-dimensional array `ds_seasonal[j,n,k]`, where:\n", + "> * `j` is the valid time of the forecast (from January to December)\n", + "> * `n` is the originating centre of the forecast (currently a maximum of 8)\n", + "> * `k` is the region (currently 8)\n", + "\n", + "With such a shaping and setup of the datasets `ds_seasonal` and `ds_reanalysis` the computation of the bias is straightforward." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4838e944-c810-4f11-9490-f6bb79b01948", + "metadata": { + "papermill": { + "duration": 0.538857, + "end_time": "2024-03-08T17:39:20.753777", + "exception": false, + "start_time": "2024-03-08T17:39:20.214920", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "ds_reanalysis = monthly_mean(ds_reanalysis)\n", + "ds_seasonal = monthly_mean(ds_seasonal)\n", + "\n", + "with xr.set_options(keep_attrs=True):\n", + " bias = ds_seasonal - ds_reanalysis\n", + "for da in bias.data_vars.values():\n", + " da.attrs[\"long_name\"] = \"Bias of \\n \" + da.attrs[\"long_name\"]\n", + "# Avoid ticks interpolation\n", + "bias = bias.assign_coords(\n", + " {name: coord.astype(str) for name, coord in bias.coords.items()}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b0e8053e-c581-4178-924f-2f219717d53f", + "metadata": { + "papermill": { + "duration": 0.245188, + "end_time": "2024-03-08T17:39:21.277354", + "exception": false, + "start_time": "2024-03-08T17:39:21.032166", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 5. Plot and describe results" + ] + }, + { + "cell_type": "markdown", + "id": "be002356-1692-4e24-bbfc-a152d249cd15", + "metadata": { + "papermill": { + "duration": 0.245188, + "end_time": "2024-03-08T17:39:21.277354", + "exception": false, + "start_time": "2024-03-08T17:39:21.032166", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "#### How to read the results\n", + "The following tables summarise the monthly bias for chosen C3S seasonal systems (labelled by producing centre) and selected areas. \n", + "\n", + "In each panel, the horizontal axis represents the validity period of the forecast, while the vertical axis shows the corresponding start dates. Thus, each horizontal strip of coloured tiles shows the evolution of the mean temperature bias over the six-month period of validity of each forecast.\n", + "\n", + "Reading each panel from bottom to top, the strips of coloured tiles are progressively shifted by one month, corresponding to the same shift in the starting date of the forecast. The horizontal axis is periodic: forecasts starting after August have their last months on the left side of the panel." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "16dd5dc1-6fce-481b-a514-c600fd535c59", + "metadata": { + "papermill": { + "duration": 0.547082, + "end_time": "2024-03-08T17:39:22.068303", + "exception": false, + "start_time": "2024-03-08T17:39:21.521221", + "status": "completed" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "centre = \"ecmwf\"\n", + "region = \"WNA\"\n", + "ax = bias[\"2m_temperature\"].sel(centre=centre,region=region).plot(x=\"valid_month\",cmap=\"RdBu_r\", levels = np.arange(-4, 4.5, .5))\n", + "_ = plt.title(f\"centre: {centre} - region: {region}\", y=1.01)" + ] + }, + { + "cell_type": "markdown", + "id": "6eed6e75-1a53-4aca-84c6-35d03e136775", + "metadata": { + "papermill": { + "duration": 0.240481, + "end_time": "2024-03-08T17:39:22.596548", + "exception": false, + "start_time": "2024-03-08T17:39:22.356067", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "#### Discussion\n", + "\n", + "The tables below summarise the monthly bias for all originating centres (columns) and for all selected areas (rows). Note that only one system (system 3) is shown from the ECCC contributions.\n", + "\n", + "**Temperature** - All seasonal forecast models produce a significant temperature bias over most of the regions selected for analysis. Two centres, DWD and METEOFRANCE, produce a forecast with a predominant warm bias over most regions and in all seasons. The temperature bias is a function of both the start time and the valid time of the forecast, with a slightly stronger dependence on the valid time. Therefore, the bias does not increase with the lead time of the forecast. Instead, it tends to be a characteristic aspect of each model over the specific region and time of year. The seasonal cycle of the bias is particularly evident over western North America.\n", + "\n", + "**Precipitation** - As in the case of temperature, the bias in precipitation tends to be more dependent on the validity period of the forecasts. The regions with the largest systematic bias are northeastern Brazil and southern Asia, where all models tend to overestimate the amplitude of the seasonal cycle.\n", + "\n", + "**Wind speed** - Unlike temperature and precipitation, the bias of wind speed does not show a significant seasonal cycle over most of the selected regions. The only exceptions are northeastern Brazil and southern Asia, where some models (e.g. ECCC and NCEP) show a positive bias during the months of June to September.\n", + "\n", + "**Dew point temperature** - As in the case of wind speed, the dew point temperature bias tends to have a weaker seasonal cycle than temperature and precipitation. However, some regions (southern Asia and western North America) show a slightly stronger seasonal dependence of the bias. NOTE: Dew point temperature is not available for ECCC." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cb4e0a7c-29d2-44ea-8c64-ff2ad1b02cd8", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_kwargs = {\n", + " \"2m_temperature\": {\"cmap\": \"RdBu_r\", \"levels\": np.arange(-4, 4.5, .5), \"extend\": \"both\"},\n", + " \"total_precipitation\": {\"cmap\": \"BrBG\", \"levels\": np.arange(-140, 160, 20), \"extend\": \"both\"},\n", + " \"10m_wind_speed\": {\"cmap\": \"PuOr\", \"levels\": np.arange(-1.4, 1.5, .2), \"extend\": \"both\"},\n", + " \"2m_dewpoint_temperature\": {\"cmap\": \"BrBG\", \"levels\": np.arange(-6, 6.5, .5), \"extend\": \"both\"},\n", + "}\n", + "\n", + "cbar_width = cbar_pad = 0.02\n", + "for varname, da in bias.data_vars.items():\n", + " facet = da.plot(\n", + " col=\"centre\", row=\"region\", x=\"valid_month\", add_colorbar=False, **plot_kwargs.get(varname, {})\n", + " )\n", + " facet.fig.suptitle(f\"Seasonal forecast: {da.attrs['long_name']}\", y=1.02, fontsize=24, fontweight='bold')\n", + " facet.set_titles(fontsize=16)\n", + " \n", + " # Create a colorbar for each row\n", + " for ax in facet.axs[:, -1]:\n", + " pos = ax.get_position()\n", + " cax = facet.fig.add_axes([pos.x1 + cbar_pad, pos.y0, cbar_width, pos.height])\n", + " facet.fig.colorbar(\n", + " ax.collections[0],\n", + " cax=cax,\n", + " label=f\"[{da.attrs['units']}]\"\n", + " )\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f3ac7f64-d375-4e4a-8a2e-9833a1305772", + "metadata": { + "papermill": { + "duration": 0.258521, + "end_time": "2024-03-08T17:40:01.124130", + "exception": false, + "start_time": "2024-03-08T17:40:00.865609", + "status": "completed" + }, + "tags": [] + }, + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cc9c7839-229e-4b13-8b8a-418b93d5ebb8", + "metadata": { + "papermill": { + "duration": 0.271597, + "end_time": "2024-03-08T17:40:01.664067", + "exception": false, + "start_time": "2024-03-08T17:40:01.392470", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## References\n", + "\n", + "[[1]](https://www.nature.com/articles/s41598-018-20628-2) Rodriguez, D., De Voil, P., Hudson, D., Brown, J. N., Hayman, P., Marrou, H., & Meinke, H. (2018). Predicting optimum crop designs using crop models and seasonal climate forecasts. Scientific reports, 8(1), 2231.\n", + "\n", + "[[2]](https://www.sciencedirect.com/science/article/pii/S0168192318303836) Jha, P. K., Athanasiadis, P., Gualdi, S., Trabucco, A., Mereu, V., Shelia, V., & Hoogenboom, G. (2019). Using daily data from seasonal forecasts in dynamic crop models for yield prediction: A case study for rice in Nepal’s Terai. Agricultural and forest meteorology, 265, 349-358.\n", + "\n", + "[[3]](https://www.sciencedirect.com/science/article/pii/S2405880722000425) Dainelli, R., Calmanti, S., Pasqui, M., Rocchi, L., Di Giuseppe, E., Monotti, C., ... & Toscano, P. (2022). Moving climate seasonal forecasts information from useful to usable for early within-season predictions of durum wheat yield. Climate Services, 28, 100324.\n", + "\n", + "[[4]]() Lledó, L., Torralba, V., Soret, A., Ramon, J., & Doblas-Reyes, F. J. (2019). Seasonal forecasts of wind power generation. Renewable Energy, 143, 91-100.\n", + "\n", + "[[5]](https://link.springer.com/article/10.1007/s00382-021-05828-3) Prodhomme, C., Materia, S., Ardilouze, C., White, R. H., Batté, L., Guemas, V., ... & García-Serrano, J. (2021). Seasonal prediction of European summer heatwaves. Climate Dynamics, 1-18.\n", + "\n", + "[[6]](https://doi.org/10.1175/JHM-D-19-0042.1) Li, W., Chen, J. I. E., Li, L. U., Chen, H. U. A., Liu, B., Xu, C. Y., & Li, X. (2019). Evaluation and bias correction of S2S precipitation for hydrological extremes. Journal of Hydrometeorology, 20(9), 1887-1906.\n", + "\n", + "[[7]](https://link.springer.com/article/10.1007/s00382-019-04640-4) Manzanas, R. (2020). Assessment of model drifts in seasonal forecasting: Sensitivity to ensemble size and implications for bias correction. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001751.\n", + "\n", + "[[8]](https://link.springer.com/article/10.1007/s00382-017-3962-9) Hermanson, L., Ren, H. L., Vellinga, M., Dunstone, N. D., Hyder, P., Ineson, S., ... & Williams, K. D. (2018). Different types of drifts in two seasonal forecast systems and their dependence on ENSO. Climate dynamics, 51, 1411-1426.\n", + "\n", + "[[9]](https://link.springer.com/article/10.1007/s00382-020-05314-2) Hemri, S., Bhend, J., Liniger, M. A., Manzanas, R., Siegert, S., Stephenson, D. B., ... & Doblas-Reyes, F. J. (2020). How to create an operational multi-model of seasonal forecasts?. Climate Dynamics, 55, 1141-1157.\n", + "\n", + "[[10]](https://link.springer.com/article/10.1007/s00382-019-04640-4) Manzanas, R., Gutiérrez, J. M., Bhend, J., Hemri, S., Doblas-Reyes, F. J., Torralba, V., ... & Brookshaw, A. (2019). Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset. Climate Dynamics, 53, 1287-1305.\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.8" + }, + "papermill": { + "default_parameters": {}, + "duration": 968.520731, + "end_time": "2024-03-08T17:40:03.783430", + "environment_variables": {}, + "exception": null, + "input_path": "D520.3.2.3b.SEASONAL_multimodel-bias_v5.ipynb", + "output_path": "output.ipynb", + "parameters": {}, + "start_time": "2024-03-08T17:23:55.262699", + "version": "2.4.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}