Clim2Agri: User guide for extracting and sub-setting geospatial climate data from gridded products for their use in crop modelling and agricultural research

Multiple information platforms currently provide fully open-access data and services from satellite Earth observations, ground observations and model outputs (Buontempo et al., 2020). These platforms are largely the result of extensive international programs coordinated by specialized agencies such as the National Aeronautics and Space Administration (NASA) of the United States (e.g., Rienecker et al., 2011), the European Centre for Medium Range Weather Forecasts (ECMWF) (e.g., C3S, 2019), or outputs from research and development programs led by research institutions associated with universities or national services. The data generated by these active platforms are provided following multiple formats and structures, which may differ considerably depending on the objectives and applications for which they were originally conceived. For example, the increasingly demanded outputs from General Circulation Models (GCM) from both historical periods and future projections are typically very large datasets of three-dimensional fields at high temporal resolutions (i.e., daily time steps) that can be difficult to handle if computing capacities are not appropriate. Moreover, agricultural research applications usually require surface weather variables (i.e., 2- m height), which in the case of GCMs must be extracted as the lower of multiple vertical levels at which the atmosphere has been discretized. Added to the above, it is common for the agricultural research community to use data formats different from those in which the original products were generated, which in some cases can represent a bottle neck since the data processing tools are not necessarily those designed to process, for example, geophysical data (e.g., Network Common Data Form NetCDF, Hierarchical Data Format HDF, General Regularlydistributed Information in Binary form GRIB).