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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

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Published in:Nonlinear Processes in Geophysics (NPG)
Format: Online Article RSS Article
Published: 2026
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container_title Nonlinear Processes in Geophysics (NPG)
description
discipline_display Earth Sciences
discipline_facet Earth Sciences
format Online Article
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genre Journal Article
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institution FRELIP
journal_source_facet Nonlinear Processes in Geophysics (NPG)
last_indexed 2026-06-20T21:38:57.850Z
publishDate 2026
publishDateSort 2026
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spellingShingle 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
Earth Sciences
General
Earth Sciences
sub_discipline_display General
sub_discipline_facet General
subject_display Earth Sciences
General
Earth Sciences
subject_facet Earth Sciences
General
Earth Sciences
title 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
title_alt Impacto de la reducción de la no gaussianidad en la precisión del análisis y pronóstico mediante la asimilación de observaciones de radar cada 30 s con el filtro de Kalman de conjunto: experimentos idealizados de convección profunda
Impact de la réduction de la non-gaussianité sur la précision de l'analyse et de la prévision en assimilant des observations radar toutes les 30 secondes avec un filtre de Kalman d'ensemble : expériences idéalisées de convection profonde
Impacto da redução da não-Gaussianidade na precisão da análise e previsão ao assimilar observações de radar a cada 30 s com filtro de Kalman por ensemble: experimentos idealizados de convecção profunda
title_auth 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
title_es_txt Impacto de la reducción de la no gaussianidad en la precisión del análisis y pronóstico mediante la asimilación de observaciones de radar cada 30 s con el filtro de Kalman de conjunto: experimentos idealizados de convección profunda
title_fr_txt Impact de la réduction de la non-gaussianité sur la précision de l'analyse et de la prévision en assimilant des observations radar toutes les 30 secondes avec un filtre de Kalman d'ensemble : expériences idéalisées de convection profonde
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_pt_txt Impacto da redução da não-Gaussianidade na precisão da análise e previsão ao assimilar observações de radar a cada 30 s com filtro de Kalman por ensemble: experimentos idealizados de convecção profunda
title_short 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
title_sort 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
topic Earth Sciences
General
Earth Sciences
url https://doi.org/10.5194/npg-33-1-2026