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Working with Forcing Data

CryoGrid-pyTools provides functionality to work with ERA5 forcing data for CryoGrid simulations.

Reading ERA5 Data

You can read ERA5 forcing data from MATLAB files using the read_mat_ear5 function:

import cryogrid_pytools as cg

# Read ERA5 forcing data
ds = cg.read_mat_ear5('path/to/ERA5.mat')

The returned xarray Dataset contains all ERA5 forcing variables needed for CryoGrid simulations.

Converting ERA5 Data

If you have ERA5 data from the Copernicus Climate Data Store (CDS) in netCDF format, you can convert it to the format expected by CryoGrid using era5_to_matlab:

import cryogrid_pytools as cg

# Convert ERA5 netCDF to MATLAB format
ds = cg.era5_to_matlab(
    era5_dataset,  # xarray Dataset from ERA5 CDS
    save_path='ERA5.mat'  # Optional: save to MATLAB file
)

The function expects the following variables in the input dataset:

Single Level Variables

  • u10, v10: 10m wind components
  • sp: Surface pressure
  • d2m: 2m dewpoint temperature
  • t2m: 2m temperature
  • ssrd: Surface solar radiation downwards
  • strd: Surface thermal radiation downwards
  • tisr: TOA incident solar radiation
  • tp: Total precipitation
  • Zs: Surface geopotential (static)

Pressure Level Variables

  • t: Temperature
  • z: Geopotential
  • q: Specific humidity
  • u, v: Wind components

The output will be formatted to match the requirements of the CryoGrid.POST_PROC.read_mat_ERA class in MATLAB.