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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Cited articles

Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, Bull. Amer. Meteor. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009. 
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, https://doi.org/10.1111/j.1600-0870.2008.00361.x, 2009. 
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001. 
Bauer, P., Geer, A.  J., Lopez, P., and Salmond, D.: Direct 4D-Var assimilation of all-sky radiances, Quart. J. Roy. Meteor. Soc., 136, 1868–1885, https://doi.org/10.1002/qj.659, 2010. 
Berner, J., Shutts, G. J., Leutbecher M., and Palmer, T. N.: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system, J. Atmos. Sci., 66, 603–626, https://doi.org/10.1175/2008JAS2677.1, 2009. 
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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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