ERA5 reanalysis data in Google Earth Engine
This repository contains a set of functions and example scripts to ingest Copernicus ERA5 reanalysis data into Google Earth Engine based on manifest uploads.
The functions were developed during the process of making a subset of the ERA5 reanalysis data available in Google Earth Engine.
Workflow overview
The workflow consists of six major steps:
- Downloading hourly data as daily files or monthly aggregates in
NetCDF format from the Climate Data Store with cdsapi
- Aggregating hourly files to daily means or sums (total precipitation) with
xarray
- Converting
NetCDF data files to GeoTiff with gdal
- Uploading hourly, daily and monthly
GeoTiff files to Google Cloud Platform (GCP) with google-cloud-storage Python API
- Creating image manifests (JSON-based files) describing the metadata and band names of the resulting Earth Engine asset
- Ingesting data files uploaded to GCP as assets into Earth Engine with
earthengine-api and manifest uploads

Repository content
- ERA5 in GEE functions
- Example workflows
- Example manifest files
- Example scripts for
Python packages required
Naming convention and folder structure
The scripts base on the following naming convention and folder_structure:
Naming Convention
Data is downloaded from the Climate Data Store with the following syntax:
era5_ + name_of_variable_ + year_ + month_ + day + .nc
- Example for a file of 2m dewpoint temperature from 1 January 1980:
era5_2m_dewpoint_temperature_1980_01_01.nc
Folder Structure:
(example for 2m air temperature)
era5_t2m
manifests
era5_hourly
era5_daily
era5_monthly
manifest_template.json
References
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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