Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation
By utilizing spatiotemporal biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem dynamical model. The results of twin experiments demonstrated that the mean absolute error (MAE) of phytoplankton in the surface layer and the reduced cost function (RCF) cou...
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ftchinacasciocas:oai:ir.qdio.ac.cn:337002/16426 2023-05-15T17:35:56+02:00 Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation Li, Xiaoyan Wang, Chunhui Fan, Wei Lv, Xianqing Lv, XQ 2013 http://ir.qdio.ac.cn/handle/337002/16426 https://doi.org/10.1155/2013/373540 英语 eng MATHEMATICAL PROBLEMS IN ENGINEERING Li, Xiaoyan; Wang, Chunhui; Fan, Wei; Lv, Xianqing.Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation,MATHEMATICAL PROBLEMS IN ENGINEERING,2013,():373540 http://ir.qdio.ac.cn/handle/337002/16426 doi:10.1155/2013/373540 6 Engineering Mathematics Multidisciplinary Interdisciplinary Applications Science & Technology Technology Physical Sciences PHYSICAL-BIOLOGICAL MODEL NORTH-ATLANTIC SEAWIFS DATA OCEAN SEA FILTER BASIN GULF Article 期刊论文 2013 ftchinacasciocas https://doi.org/10.1155/2013/373540 2022-06-27T05:35:36Z By utilizing spatiotemporal biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem dynamical model. The results of twin experiments demonstrated that the mean absolute error (MAE) of phytoplankton in the surface layer and the reduced cost function (RCF) could be used to evaluate both the simulation results and parameter estimation. Spatiotemporal variation of key parameters (KPs) was optimized in real experiments. The RCF and MAE in each assimilation period (72 periods per year) decreased obviously. The spatially varying KP (KPS), temporally varying KP (KPT), and constant KP (KPC) were obtained by averaging KPs of spatiotemporal variation. Another type of spatiotemporal KP (KPST) was represented by KPS, KPT, and KPC. The correlation analysis of KPs, either KPS or KPT, accorded with the real ecological mechanism. Running the model with KPS, KPT, KPC, and KPST, respectively, we found that MAE was the minimum when KPs were spatiotemporal variation (KPST), while MAE reached its maximum when KPs were constant (KPC). Using spatiotemporal KPs could improve simulation precision compared with only using spatially varying KPs, temporally varying KPs, or constant KPs (these forms are the results in a previous study). KPST, a representation of spatiotemporal variation, reduces the variable number in calculation. By utilizing spatiotemporal biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem dynamical model. The results of twin experiments demonstrated that the mean absolute error (MAE) of phytoplankton in the surface layer and the reduced cost function (RCF) could be used to evaluate both the simulation results and parameter estimation. Spatiotemporal variation of key parameters (KPs) was optimized in real experiments. The RCF and MAE in each assimilation period (72 periods per year) decreased obviously. The spatially varying KP (KPS), temporally varying KP (KPT), and constant KP (KPC) were obtained by averaging KPs of spatiotemporal ... Article in Journal/Newspaper North Atlantic Institute of Oceanology, Chinese Academy of Sciences: IOCAS-IR Mathematical Problems in Engineering 2013 1 12 |
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Open Polar |
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Institute of Oceanology, Chinese Academy of Sciences: IOCAS-IR |
op_collection_id |
ftchinacasciocas |
language |
English |
topic |
Engineering Mathematics Multidisciplinary Interdisciplinary Applications Science & Technology Technology Physical Sciences PHYSICAL-BIOLOGICAL MODEL NORTH-ATLANTIC SEAWIFS DATA OCEAN SEA FILTER BASIN GULF |
spellingShingle |
Engineering Mathematics Multidisciplinary Interdisciplinary Applications Science & Technology Technology Physical Sciences PHYSICAL-BIOLOGICAL MODEL NORTH-ATLANTIC SEAWIFS DATA OCEAN SEA FILTER BASIN GULF Li, Xiaoyan Wang, Chunhui Fan, Wei Lv, Xianqing Lv, XQ Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation |
topic_facet |
Engineering Mathematics Multidisciplinary Interdisciplinary Applications Science & Technology Technology Physical Sciences PHYSICAL-BIOLOGICAL MODEL NORTH-ATLANTIC SEAWIFS DATA OCEAN SEA FILTER BASIN GULF |
description |
By utilizing spatiotemporal biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem dynamical model. The results of twin experiments demonstrated that the mean absolute error (MAE) of phytoplankton in the surface layer and the reduced cost function (RCF) could be used to evaluate both the simulation results and parameter estimation. Spatiotemporal variation of key parameters (KPs) was optimized in real experiments. The RCF and MAE in each assimilation period (72 periods per year) decreased obviously. The spatially varying KP (KPS), temporally varying KP (KPT), and constant KP (KPC) were obtained by averaging KPs of spatiotemporal variation. Another type of spatiotemporal KP (KPST) was represented by KPS, KPT, and KPC. The correlation analysis of KPs, either KPS or KPT, accorded with the real ecological mechanism. Running the model with KPS, KPT, KPC, and KPST, respectively, we found that MAE was the minimum when KPs were spatiotemporal variation (KPST), while MAE reached its maximum when KPs were constant (KPC). Using spatiotemporal KPs could improve simulation precision compared with only using spatially varying KPs, temporally varying KPs, or constant KPs (these forms are the results in a previous study). KPST, a representation of spatiotemporal variation, reduces the variable number in calculation. By utilizing spatiotemporal biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem dynamical model. The results of twin experiments demonstrated that the mean absolute error (MAE) of phytoplankton in the surface layer and the reduced cost function (RCF) could be used to evaluate both the simulation results and parameter estimation. Spatiotemporal variation of key parameters (KPs) was optimized in real experiments. The RCF and MAE in each assimilation period (72 periods per year) decreased obviously. The spatially varying KP (KPS), temporally varying KP (KPT), and constant KP (KPC) were obtained by averaging KPs of spatiotemporal ... |
format |
Article in Journal/Newspaper |
author |
Li, Xiaoyan Wang, Chunhui Fan, Wei Lv, Xianqing Lv, XQ |
author_facet |
Li, Xiaoyan Wang, Chunhui Fan, Wei Lv, Xianqing Lv, XQ |
author_sort |
Li, Xiaoyan |
title |
Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation |
title_short |
Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation |
title_full |
Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation |
title_fullStr |
Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation |
title_full_unstemmed |
Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation |
title_sort |
optimization of the spatiotemporal parameters in a dynamical marine ecosystem model based on the adjoint assimilation |
publishDate |
2013 |
url |
http://ir.qdio.ac.cn/handle/337002/16426 https://doi.org/10.1155/2013/373540 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
MATHEMATICAL PROBLEMS IN ENGINEERING Li, Xiaoyan; Wang, Chunhui; Fan, Wei; Lv, Xianqing.Optimization of the Spatiotemporal Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation,MATHEMATICAL PROBLEMS IN ENGINEERING,2013,():373540 http://ir.qdio.ac.cn/handle/337002/16426 doi:10.1155/2013/373540 |
op_rights |
6 |
op_doi |
https://doi.org/10.1155/2013/373540 |
container_title |
Mathematical Problems in Engineering |
container_volume |
2013 |
container_start_page |
1 |
op_container_end_page |
12 |
_version_ |
1766135229497475072 |