CHIMP models and results

Example

The animation below compares the retrieved radar reflectivity for the different model versions.

Credit: Simon Pfreundschuh

Models

chimp_smhi_v0

• ResNeXt architecture with 5M parameters

• Trained on 1 year of collocations

• Scene size 128

chimp_smhi_v1

• EfficientNet-V2 architecture with 20M parameters

• Trained on 1 year of collocations

• Scene size 256

Note

The chimp_smhi_v1 models should be run with a tile size of 256.

chimp_smhi_v2

• EfficientNet-V2 2p1 architecture with ~40M parameters

• Trained on 2 years of collocations over Europe and the Nordics

• Scene size 256

Note

The chimp_smhi_v2 models should be run with a tile size of 256 and a sequence length of 16.

chimp_smhi_v3

There are two chimp_smhi version 3 models. The chimp_smhi_v3 model processes single inputs, while the chimp_smhi_v3_seq model processes multiple inputs.

Note

The chimp_smhi_v3 model should be run with a tile size of 256.

The chimp_smhi_v3_seq model should be run with a tile size of 256 and a sequence length of 16.

Results

The results are written as NetCDF4 datasets to the provided output directory. Currently, the only retrieved variable is dbz_mean. Since CHIMP retrievals are probabilistic, the _mean suffix is added to the variable name to highlight that it is the expected value of the retrieved posterior distribution.