A composite state method for ensemble data assimilation with multiple limited-area models
Limited-area models (LAMs) allow high-resolution forecasts to be made for geographic regions of interest when resources are limited. Typically, boundary conditions for these models are provided through one-way boundary coupling from a coarser resolution global model. Here, data assimilation is consi...
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doaj-0be95b4f76ee416d971208e8b70c03eb2020-11-24T20:58:23ZengTaylor & Francis GroupTellus: Series A, Dynamic Meteorology and Oceanography1600-08702015-04-0167011710.3402/tellusa.v67.2649526495A composite state method for ensemble data assimilation with multiple limited-area modelsMatthew Kretschmer0Brian R. Hunt1Edward Ott2Craig H. Bishop3Sabrina Rainwater4Istvan Szunyogh5 Department of Physics, University of Maryland, College Park, MD, USA Department of Mathematics, University of Maryland, College Park, MD, USA Department of Physics, University of Maryland, College Park, MD, USA Naval Research Laboratory, Monterey, California, CA, USA National Research Council, Monterey, CA, USA Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USALimited-area models (LAMs) allow high-resolution forecasts to be made for geographic regions of interest when resources are limited. Typically, boundary conditions for these models are provided through one-way boundary coupling from a coarser resolution global model. Here, data assimilation is considered in a situation in which a global model supplies boundary conditions to multiple LAMs. The data assimilation method presented combines information from all of the models to construct a single ‘composite state’, on which data assimilation is subsequently performed. The analysis composite state is then used to form the initial conditions of the global model and all of the LAMs for the next forecast cycle. The method is tested by using numerical experiments with simple, chaotic models. The results of the experiments show that there is a clear forecast benefit to allowing LAM states to influence one another during the analysis. In addition, adding LAM information at analysis time has a strong positive impact on global model forecast performance, even at points not covered by the LAMs.http://www.tellusa.net/index.php/tellusa/article/view/26495/pdf_27Ensemble Kalman Filterlimited-area modelscomposite state |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Matthew Kretschmer Brian R. Hunt Edward Ott Craig H. Bishop Sabrina Rainwater Istvan Szunyogh |
spellingShingle |
Matthew Kretschmer Brian R. Hunt Edward Ott Craig H. Bishop Sabrina Rainwater Istvan Szunyogh A composite state method for ensemble data assimilation with multiple limited-area models Tellus: Series A, Dynamic Meteorology and Oceanography Ensemble Kalman Filter limited-area models composite state |
author_facet |
Matthew Kretschmer Brian R. Hunt Edward Ott Craig H. Bishop Sabrina Rainwater Istvan Szunyogh |
author_sort |
Matthew Kretschmer |
title |
A composite state method for ensemble data assimilation with multiple limited-area models |
title_short |
A composite state method for ensemble data assimilation with multiple limited-area models |
title_full |
A composite state method for ensemble data assimilation with multiple limited-area models |
title_fullStr |
A composite state method for ensemble data assimilation with multiple limited-area models |
title_full_unstemmed |
A composite state method for ensemble data assimilation with multiple limited-area models |
title_sort |
composite state method for ensemble data assimilation with multiple limited-area models |
publisher |
Taylor & Francis Group |
series |
Tellus: Series A, Dynamic Meteorology and Oceanography |
issn |
1600-0870 |
publishDate |
2015-04-01 |
description |
Limited-area models (LAMs) allow high-resolution forecasts to be made for geographic regions of interest when resources are limited. Typically, boundary conditions for these models are provided through one-way boundary coupling from a coarser resolution global model. Here, data assimilation is considered in a situation in which a global model supplies boundary conditions to multiple LAMs. The data assimilation method presented combines information from all of the models to construct a single ‘composite state’, on which data assimilation is subsequently performed. The analysis composite state is then used to form the initial conditions of the global model and all of the LAMs for the next forecast cycle. The method is tested by using numerical experiments with simple, chaotic models. The results of the experiments show that there is a clear forecast benefit to allowing LAM states to influence one another during the analysis. In addition, adding LAM information at analysis time has a strong positive impact on global model forecast performance, even at points not covered by the LAMs. |
topic |
Ensemble Kalman Filter limited-area models composite state |
url |
http://www.tellusa.net/index.php/tellusa/article/view/26495/pdf_27 |
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