CHIMP models and results
========================

Example
-------

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

.. figure:: https://github.com/user-attachments/assets/d96ef894-fc82-4640-a4c8-bbd2b359cd4d
  :alt: Comparison of the retrieved radar reflectivity for the different model versions
  :align: center

  Credit: `Simon Pfreundschuh <https://github.com/simonpf>`_


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.