Impact of reduced non-Gaussianity on analysis and forecast accuracy by assimilating every-30&thinsp;s radar observation with ensemble Kalman filter: idealized experiments of deep convection

Amemiya, Arata; Miyoshi, Takemasa

doi:10.5194/npg-33-1-2026

Articles | Volume 33, issue 1

https://doi.org/10.5194/npg-33-1-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-33-1-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 33, issue 1

Research article

|

05 Jan 2026

Research article |

| 05 Jan 2026

Impact of reduced non-Gaussianity on analysis and forecast accuracy by assimilating every-30 s radar observation with ensemble Kalman filter: idealized experiments of deep convection

Arata Amemiya and Takemasa Miyoshi

Model code and software

SCALE-LETKF-RIKEN/scale-letkf: 5.5.5-v1 Arata Amemiya et al. https://doi.org/10.5281/zenodo.17156801

Short summary

The accurate estimation of atmospheric state variables from radar observation in rapidly growing deep convection, which causes heavy thunderstorms, is a major challenge. This study examines the advantage of incorporating radar observation data with very high frequency such as 30 s compared with the conventional case of 5 min, from a theoretical perspective.