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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jan 2026
Research article |
| 05 Jan 2026
| 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
RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Wako, Japan
RIKEN Center for Computational Science (R-CCS), Kobe, Japan
currently at: Japan Weather Association, Tokyo, Japan
Takemasa Miyoshi
RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Wako, Japan
RIKEN Center for Computational Science (R-CCS), Kobe, Japan
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Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
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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.
The accurate estimation of atmospheric state variables from radar observation in rapidly growing...
