Traditional Data Assimilation and Its Effective Integration with Meteorological Deep Learning

Xiaolei Zou

doi:10.1016/j.eng.2025.11.023

Engineering ›› :202511023 DOI: 10.1016/j.eng.2025.11.023

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Traditional Data Assimilation and Its Effective Integration with Meteorological Deep Learning

Xiaolei Zou ^*

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Abstract

This article reviews traditional practices in atmospheric data assimilation and explores scientific strategies to assimilate data at resolutions spanning from tens of kilometers to even hundreds of meters. Such advances leverage the latest technological developments in computing hardware and methods. Two focal points are specifically chosen to illustrate the opportunities and the associated challenges: ① How to fully exploit satellite-observed cloud and rainband structures in tropical cyclones for high-resolution data assimilation; and ② which traditional data assimilation core techniques need re-evaluation and improvement. Specific topics include making an innovative advancement in all-sky brightness temperature assimilation by seeking the connection between satellite observed brightness temperature and unobservable relative vorticity within tropical cyclones; constructing a global data assimilation framework suitable for satellite-orbit-data observation times; developing advanced data thinning and quality control methods to avoid losing the most needed atmospheric small-scale and large gradient structural information. Finally, how to effectively integrate meteorological data assimilation with deep learning is discussed, such as incorporating AI physical parameterization models into 4D-Var system; combining image assimilation with AI to make prediction from image data; making data assimilation and meteorological deep learning mutually beneficial.

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Data assimilation / Satellite data / Tropical cyclone / Rainband / Dynamic constraint / Deep learning

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Xiaolei Zou. Traditional Data Assimilation and Its Effective Integration with Meteorological Deep Learning. Engineering 202511023 DOI:10.1016/j.eng.2025.11.023

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