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Description
CDO tools are widely used by climate science and well understood. Being able to use CDO through a pyearthtools pipeline which can than easily integrate into a ML framework (e.g. pytorch), would be a really good way for climate scientists to make use of existing skills as part of an ML project.
We shoukd demonstrate
- a pyearthtools accessor to some climate data e.g. CMIP6 models
- extract variables with CDO
cdo selname, pressure levels etc. using CDO - extract a region using CDO
cdo sellatlonbox - possibly calculate daily means
cdo daymean - rename to CF names
cdo chname,old_name,new_name - regrid to coarser resolution using CDO
cdo remapcon,outgrid.txt -setgrid,ingrid.txt infile.nc outfile.nc - return pairs of coarse predictors and high-res target(s)
- train basic CNN downscaler (use current tutorial)
- inference
- evaluate with scores package
- Look at monthly and yearly means of variables to check whether climatology correctly represented.
cdo monmean,cdo yearmean - Calculate climate indices with spaital averages
cdo fldmeanorcdo fldmax
- Look at monthly and yearly means of variables to check whether climatology correctly represented.
Some example material
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