Evaluation of a Strategy for the Assimilation of Satellite Radiance Observations with the Local Ensemble Transform Kalman Filter

José A. Aravéquia Centro de Previsão de Tempo e Estudos Climáticos, São Paulo, Brazil

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Istvan Szunyogh Texas A&M University, College Station, Texas

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Elana J. Fertig John Hopkins University, Baltimore, Maryland

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Eugenia Kalnay University of Maryland, College Park, College Park, Maryland

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David Kuhl Naval Research Laboratory, Washington, D.C.

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Eric J. Kostelich Arizona State University, Tempe, Arizona

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Abstract

This paper evaluates a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter (LETKF) data assimilation scheme. The assimilation strategy includes a mechanism to select the radiance observations that are assimilated at a given grid point and an ensemble-based observation bias-correction technique. Numerical experiments are carried out with a reduced (T62L28) resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The observations used for the evaluation of the assimilation strategy are AMSU-A level 1B brightness temperature data from the Earth Observing System (EOS) Aqua spacecraft. The assimilation of these observations, in addition to all operationally assimilated nonradiance observations, leads to a statistically significant improvement of both the temperature and wind analysis in the Southern Hemisphere. This result suggests that the LETKF, combined with the proposed data assimilation strategy for the assimilation of satellite radiance observations, can efficiently extract information from radiance observations.

Corresponding author address: José A. Aravéquia, Centro de Previsão de Tempo e Estudos Climáticos, Rodovia Dutra, km 40, CEP 12630-000, Cachoeira Paulista, São Paulo, Brazil. E-mail: araveq@cptec.inpe.br

This article is included in the Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter special collection.

Abstract

This paper evaluates a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter (LETKF) data assimilation scheme. The assimilation strategy includes a mechanism to select the radiance observations that are assimilated at a given grid point and an ensemble-based observation bias-correction technique. Numerical experiments are carried out with a reduced (T62L28) resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The observations used for the evaluation of the assimilation strategy are AMSU-A level 1B brightness temperature data from the Earth Observing System (EOS) Aqua spacecraft. The assimilation of these observations, in addition to all operationally assimilated nonradiance observations, leads to a statistically significant improvement of both the temperature and wind analysis in the Southern Hemisphere. This result suggests that the LETKF, combined with the proposed data assimilation strategy for the assimilation of satellite radiance observations, can efficiently extract information from radiance observations.

Corresponding author address: José A. Aravéquia, Centro de Previsão de Tempo e Estudos Climáticos, Rodovia Dutra, km 40, CEP 12630-000, Cachoeira Paulista, São Paulo, Brazil. E-mail: araveq@cptec.inpe.br

This article is included in the Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter special collection.

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