copernicus-climate

# Copernicus Climate Data Store (CDS) Access ERA5 reanalysis, climate projections, and satellite climate records through the Copernicus CDS API. Covers global gridded climate data from 1940 to present. ## Prerequisites Install the CDS API client and configure credentials: ```bash pip install cdsapi ``` Create `~/.cdsapirc` with your CDS credentials: ``` url: https://cds.climate.copernicus.eu/api key: <your-uid>:<your-api-key> ``` Register at https://cds.climate.copernicus.eu to obtain credentials. ## API Base URL ``` https://cds.climate.copernicus.eu/api ``` ## Basic Python Retrieval Pattern ```python import cdsapi c = cdsapi.Client() c.retrieve( "reanalysis-era5-single-levels", { "product_type": "reanalysis", "variable": "2m_temperature", "year": "2023", "month": "07", "day": "15", "time": "12:00", "area": [60, -10, 35, 30], # N, W, S, E bounding box "format": "netcdf", }, "era5_temperature.nc", ) ``` ## ERA5 Pressure-Level Variables Retrieve upper-air data on pressure levels: ```python c.retrieve( "reanalysis-era5-pressure-levels", { "product_type": "reanalysis", "variable": ["temperature", "geopotential", "relative_humidity"], "pressure_level": ["500", "700", "850", "925"], "year": "2023", "month": "01", "day": "15", "time": "12:00", "format": "netcdf", }, "era5_pressure_levels.nc", ) ``` ## Key Dataset Identifiers | Dataset ID | Description | |-----------------------------------------|------------------------------------------| | `reanalysis-era5-single-levels` | Surface and single-level hourly fields | | `reanalysis-era5-pressure-levels` | Upper-air on 37 pressure levels | | `reanalysis-era5-single-levels-monthly` | Monthly-averaged surface fields | | `reanalysis-era5-land` | ERA5-Land (enhanced land, 9 km) | | `satellite-sea-level-global` | Satellite altimetry sea level | ## Common Variables **Single level**: `2m_temperature`, `total_precipitation`, `10m_u_component_of_wind`, `10m_v_component_of_wind`, `mean_sea_level_pressure`, `surface_solar_radiation_downwards`. **Pressure level**: `temperature`, `geopotential`, `relative_humidity`, `specific_humidity`. ## Processing Downloaded NetCDF ```python import xarray as xr ds = xr.open_dataset("era5_temperature.nc") temp_celsius = ds["t2m"] - 273.15 # Kelvin to Celsius print(f"Mean temperature: {float(temp_celsius.mean()):.1f} C") ``` ## Area Selection (N, W, S, E bounding box) Global: `[90, -180, -90, 180]`, Europe: `[72, -25, 33, 45]`, Continental US: `[50, -125, 25, -65]`, East Asia: `[55, 70, 5, 145]`. ## Best Practices 1. Specify the smallest area and fewest variables needed to reduce download time. 2. Use monthly-averaged datasets when daily resolution is not required. 3. Request data in NetCDF format for analysis; GRIB for operational workflows. 4. CDS queues requests; large jobs may take hours. Check status via the web dashboard. 5. ERA5 data is available from 1940 to present with ~5-day latency. 6. For multi-year bulk downloads, split requests by year to avoid timeouts. 7. Install `xarray` and `netCDF4` for reading downloaded files in Python.