Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
Journal:
arXiv
Published Date:
Apr 22, 2025
Abstract
The continuous improvement in weather forecast skill over the past several
decades is largely due to the increasing quantity of available satellite
observations and their assimilation into operational forecast systems.
Assimilating these observations requires observation operators in the form of
radiative transfer models. Significant efforts have been dedicated to enhancing
the computational efficiency of these models. Computational cost remains a
bottleneck, and a large fraction of available data goes unused for
assimilation. To address this, we used machine learning to build an efficient
neural network based probabilistic emulator of the Community Radiative Transfer
Model (CRTM), applied to the GOES Advanced Baseline Imager. The trained NN
emulator predicts brightness temperatures output by CRTM and the corresponding
error with respect to CRTM. RMSE of the predicted brightness temperature is 0.3
K averaged across all channels. For clear sky conditions, the RMSE is less than
0.1 K for 9 out of 10 infrared channels. The error predictions are generally
reliable across a wide range of conditions. Explainable AI methods demonstrate
that the trained emulator reproduces the relevant physics, increasing
confidence that the model will perform well when presented with new data.
