Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems

Fil: Casaretto, Gimena. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina.

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Detalles Bibliográficos
Autores principales: Casaretto, Gimena, Schwartz, Craig S., Dillon, María Eugenia, García Skabar, Yanina, Ruiz, Juan José
Formato: Artículo
Lenguaje:Inglés
Publicado: American Meteorological Society 2025
Materias:
Observation impact
EFSOI
Ensemble Kalman filter
Data assimilation
CC
PC
Rapid Refresh Model
Acceso en línea:https://doi.org/10.1175/WAF-D-24-0127.1
http://hdl.handle.net/20.500.12160/3004
Aporte de:
El Abrigo - Repositorio Institucional del Servicio Meteorológico Nacional (SMN) de Servicio Meteorológico Nacional (SMN)
id I63-R169-20.500.12160-3004
record_format dspace
spelling I63-R169-20.500.12160-30042025-05-01T07:50:39Z Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems Casaretto, Gimena Schwartz, Craig S. Dillon, María Eugenia García Skabar, Yanina Ruiz, Juan José Observation impact EFSOI Ensemble Kalman filter Data assimilation CC PC Rapid Refresh Model Fil: Casaretto, Gimena. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina. This study applies the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) technique to two 80-member ensemble Kalman filter (EnKF) data assimilation (DA) systems over the United States, differing only in cycling strategy: continuous cycling (CC) and partial cycling (PC). EFSOI calculations were performed using 1-hour, 6-hour and 12-hour evaluation forecast times, verified against the Rapid Refresh Model (RAP) analysis. Beneficial impact rates indicated that most observations were beneficial for both DA systems and forecast times, with no significant difference between PC and CC. Differences in cumulative observation impacts were statistically significant only for sources with few observations and small impacts, like mesonet observations. For numerous and impactful observations, such as rawinsondes and aircraft, differences were not statistically significant, suggesting similar use of important observations by PC and CC. PC forecasts were better than CC forecasts, but this improvement is not clearly due to better use of observations. Variable-wise analysis showed similar behavior between PC and CC for impact rates and cumulative impacts of U, V, T, RH, and surface zonal wind. Overall, there was no evidence that either PC or CC systematically used observations better, with mixed results across observation types and sources. Differences between PC and CC were typically small and not statistically significant for the most impactful observations and variables. Fundamental methodological differences between PC and CC did not significantly impact their ability to assimilate observations, the process of ingesting global fields likely responsible for improved PC forecasts relative to CC. 2025-04-30T18:18:19Z 2025-04-30T18:18:19Z 2025-04-18 Artículo Casaretto, G., C. S. Schwartz, M. E. Dillon, Y. Gracia Skabar, and J. J. Ruiz, 2025: Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems. Wea. Forecasting, https://doi.org/10.1175/WAF-D-24-0127.1, in press. 1534-7486 https://doi.org/10.1175/WAF-D-24-0127.1 http://hdl.handle.net/20.500.12160/3004 eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ info:eu-repo/semantics/openAccess application/pdf American Meteorological Society
institution Servicio Meteorológico Nacional (SMN)
institution_str I-63
repository_str R-169
collection El Abrigo - Repositorio Institucional del Servicio Meteorológico Nacional (SMN)
language Inglés
topic Observation impact
EFSOI
Ensemble Kalman filter
Data assimilation
CC
PC
Rapid Refresh Model
spellingShingle Observation impact
EFSOI
Ensemble Kalman filter
Data assimilation
CC
PC
Rapid Refresh Model
Casaretto, Gimena
Schwartz, Craig S.
Dillon, María Eugenia
García Skabar, Yanina
Ruiz, Juan José
Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
topic_facet Observation impact
EFSOI
Ensemble Kalman filter
Data assimilation
CC
PC
Rapid Refresh Model
description Fil: Casaretto, Gimena. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina.
format Artículo
author Casaretto, Gimena
Schwartz, Craig S.
Dillon, María Eugenia
García Skabar, Yanina
Ruiz, Juan José
author_facet Casaretto, Gimena
Schwartz, Craig S.
Dillon, María Eugenia
García Skabar, Yanina
Ruiz, Juan José
author_sort Casaretto, Gimena
title Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_short Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_full Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_fullStr Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_full_unstemmed Exploring Quantitative Observation Impact in Partial and Continuous Cycling Ensemble Kalman Filter Data Assimilation Systems
title_sort exploring quantitative observation impact in partial and continuous cycling ensemble kalman filter data assimilation systems
publisher American Meteorological Society
publishDate 2025
url https://doi.org/10.1175/WAF-D-24-0127.1
http://hdl.handle.net/20.500.12160/3004
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