Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning

• URL: http://arxiv.org/abs/2504.16192v1
• Date: Tue, 22 Apr 2025 18:30:43 GMT
• Title: Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
• Authors: Lucas Howard, Aneesh C. Subramanian, Gregory Thompson, Benjamin Johnson, Thomas Auligne,
• Abstract summary: We use machine learning to build an efficient neural network based probabilistic emulator of the Community Radiative Transfer Model (CRTM)<n>The trained NN emulator predicts brightness temperatures output by CRTM and the corresponding error with respect to CRTM.<n>For clear sky conditions, the RMSE is less than 0.1 K for 9 out of 10 infrared channels.
• Score: 0.04448495628113797
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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