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

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

doi:10.5194/gmd-18-8569-2025

Articles | Volume 18, issue 22

https://doi.org/10.5194/gmd-18-8569-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

The application of JEDI (Joint Effort for Data assimilation...

https://doi.org/10.5194/gmd-18-8569-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 22

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

Data sets

Global Forecast System analyses National Centers For Environmental Prediction/National Weather Service/NOAA/U.S. Department Of Commerce https://rda.ucar.edu/datasets/ds084.1/

Global Ensemble Forecast System ensemble analyses NOAA https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast

Conventional and satellite observations National Centers For Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce https://rda.ucar.edu/datasets/d337000

Conventional and satellite observations National Centers For Environmental Prediction/National Weather Service/NOAA/U.S. Department Of Commerce https://rda.ucar.edu/datasets/d735000/

ATMS radiance data NOAA https://sounder.gesdisc.eosdis.nasa.gov/opendap

Model code and software

MPAS-JEDI 2.1.0 Joint Center for Satellite Data Assimilation & National Center for Atmospheric Research https://doi.org/10.5281/zenodo.15201032

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.