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

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Dec 2025	42	26	4	72
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Cumulative views and downloads (calculated since 25 Jun 2025)

Month	HTML	PDF	XML	Total
Jun 2025	102	22	6	130
Jul 2025	206	52	22	280
Aug 2025	572	42	0	614
Sep 2025	2,526	24	16	2,566
Oct 2025	56	12	6	74
Nov 2025	48	28	4	80
Dec 2025	42	26	4	72
Jan 2026	213	90	24	327
Feb 2026	276	43	15	334
Mar 2026	183	82	10	275
Apr 2026	128	37	22	187
May 2026	204	25	20	249
Jun 2026	43	24	4	71
Jul 2026	39	25	5	69
Aug 2026	45	37	5	87
Sep 2026	154	72	66	292
Oct 2026	161	102	42	305
Nov 2026	45	33	13	91

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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.