A Prototype Machine learning-based all-sky radiance data assimilation framework at microwave and submillimeter frequencies in preparation for AOS and CoSSIR

Leveraging the GSFC expertise in passive microwave all-sky radiance data assimilation, hydrometeor retrieval and ML techniques, we propose to build a prototype Machine Learning (ML) framework for all-sky radiance data assimilation (DA) and geophysical retrieval algorithms, targeting the new millimeter (mmwave) and submillimeter (sub-mm) channels (89 – 680 GHz) that Atmosphere Observing System (AOS) mission passive microwave radiometer (PMW) and Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR) will include.

This proposal will help to maximize the usage of all-sky radiances from radiometers in estimating global atmosphere states and hydrometeors and improving model forecast. The proposed framework development will also advance the applications of ML in the data assimilation and provide a platform for future retrieval algorithm development. This proposal is highly relevant to NASA and meets the Science Mission Directorate’s strategic science objectives “Science 2020-2024: A Vision for Scientific Excellence”.

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