Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8569-2025
https://doi.org/10.5194/gmd-18-8569-2025
Development and technical paper
 | 
14 Nov 2025
Development and technical paper |  | 14 Nov 2025

All-sky AMSU-A radiance data assimilation using the gain-form of Local Ensemble Transform Kalman filter within MPAS-JEDI-2.1.0: implementation, tuning, and evaluation

Tao Sun, Jonathan J. Guerrette, Zhiquan Liu, Junmei Ban, Byoung-Joo Jung, Ivette Hernandez Banos, and Chris Snyder

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Short summary
We evaluated a new ensemble data assimilation system that uses satellite observations in all weather conditions for global weather forecasts. The results show that including cloud- and precipitation-affected satellite data improves forecasts of moisture, wind, and clouds, especially in the tropics. This work highlights the potential of this new ensemble data assimilation system to enhance global weather forecasts.
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