An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods
An adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system...
Ausführliche Beschreibung
Autor*in: |
Yonghan Choi [verfasserIn] Gyu-HO Lim [verfasserIn] Dong-Kyou Lee [verfasserIn] Xiang-Yu Huang [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2014 |
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Übergeordnetes Werk: |
In: Tellus: Series A, Dynamic Meteorology and Oceanography ; 66(2014), 0, Seite 18 volume:66 ; year:2014 ; number:0 ; pages:18 |
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Link aufrufen |
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DOI / URN: |
10.3402/tellusa.v66.21584 |
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Katalog-ID: |
DOAJ025427008 |
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Tellus: Series A, Dynamic Meteorology and Oceanography |
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Tellus: Series A, Dynamic Meteorology and Oceanography |
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An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
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title_full |
An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
author_sort |
Yonghan Choi |
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Tellus: Series A, Dynamic Meteorology and Oceanography |
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Tellus: Series A, Dynamic Meteorology and Oceanography |
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eng |
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2014 |
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Yonghan Choi Gyu-HO Lim Dong-Kyou Lee Xiang-Yu Huang |
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GC1-1581 QC851-999 |
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Elektronische Aufsätze |
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Yonghan Choi |
doi_str_mv |
10.3402/tellusa.v66.21584 |
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verfasserin |
title_sort |
adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
callnumber |
GC1-1581 |
title_auth |
An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
abstract |
An adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system (MCS) related to the heavy rainfall was classified as training line/adjoining stratiform (TL/AS)-type for the earlier period, and back building (BB)-type for the later period. In the ASDA method, an adjoint model is run backwards with forecast-error gradient as input, and the adjoint sensitivity of the forecast error to the initial condition is scaled by an optimal scaling factor. The optimal scaling factor is determined by minimising the observational cost function of the four-dimensional variational (4D-Var) method, and the scaled sensitivity is added to the original first guess. Finally, the observations at the analysis time are assimilated using a 3D-Var method with the improved first guess. The simulated rainfall distribution is shifted northeastward compared to the observations when no radar data are assimilated or when radar data are assimilated using the 3D-Var method. The rainfall forecasts are improved when radar data are assimilated using the 4D-Var or ASDA method. Simulated atmospheric fields such as horizontal winds, temperature, and water vapour mixing ratio are also improved via the 4D-Var or ASDA method. Due to the improvement in the analysis, subsequent forecasts appropriately simulate the observed features of the TL/AS- and BB-type MCSs and the corresponding heavy rainfall. The computational cost associated with the ASDA method is significantly lower than that of the 4D-Var method. |
abstractGer |
An adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system (MCS) related to the heavy rainfall was classified as training line/adjoining stratiform (TL/AS)-type for the earlier period, and back building (BB)-type for the later period. In the ASDA method, an adjoint model is run backwards with forecast-error gradient as input, and the adjoint sensitivity of the forecast error to the initial condition is scaled by an optimal scaling factor. The optimal scaling factor is determined by minimising the observational cost function of the four-dimensional variational (4D-Var) method, and the scaled sensitivity is added to the original first guess. Finally, the observations at the analysis time are assimilated using a 3D-Var method with the improved first guess. The simulated rainfall distribution is shifted northeastward compared to the observations when no radar data are assimilated or when radar data are assimilated using the 3D-Var method. The rainfall forecasts are improved when radar data are assimilated using the 4D-Var or ASDA method. Simulated atmospheric fields such as horizontal winds, temperature, and water vapour mixing ratio are also improved via the 4D-Var or ASDA method. Due to the improvement in the analysis, subsequent forecasts appropriately simulate the observed features of the TL/AS- and BB-type MCSs and the corresponding heavy rainfall. The computational cost associated with the ASDA method is significantly lower than that of the 4D-Var method. |
abstract_unstemmed |
An adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system (MCS) related to the heavy rainfall was classified as training line/adjoining stratiform (TL/AS)-type for the earlier period, and back building (BB)-type for the later period. In the ASDA method, an adjoint model is run backwards with forecast-error gradient as input, and the adjoint sensitivity of the forecast error to the initial condition is scaled by an optimal scaling factor. The optimal scaling factor is determined by minimising the observational cost function of the four-dimensional variational (4D-Var) method, and the scaled sensitivity is added to the original first guess. Finally, the observations at the analysis time are assimilated using a 3D-Var method with the improved first guess. The simulated rainfall distribution is shifted northeastward compared to the observations when no radar data are assimilated or when radar data are assimilated using the 3D-Var method. The rainfall forecasts are improved when radar data are assimilated using the 4D-Var or ASDA method. Simulated atmospheric fields such as horizontal winds, temperature, and water vapour mixing ratio are also improved via the 4D-Var or ASDA method. Due to the improvement in the analysis, subsequent forecasts appropriately simulate the observed features of the TL/AS- and BB-type MCSs and the corresponding heavy rainfall. The computational cost associated with the ASDA method is significantly lower than that of the 4D-Var method. |
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An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods |
url |
https://doi.org/10.3402/tellusa.v66.21584 https://doaj.org/article/8b0a1ace327a4814ab54ff8477c1191b http://www.tellusa.net/index.php/tellusa/article/download/21584/pdf_1 https://doaj.org/toc/0280-6495 https://doaj.org/toc/1600-0870 |
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author2 |
Gyu-HO Lim Dong-Kyou Lee Xiang-Yu Huang |
author2Str |
Gyu-HO Lim Dong-Kyou Lee Xiang-Yu Huang |
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GC - Oceanography |
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doi_str |
10.3402/tellusa.v66.21584 |
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up_date |
2024-07-03T14:55:58.040Z |
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