The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model

Data assimilation has been widely applied in atmospheric and oceanic forecasting systems and particle filters (PFs) have unique advantages in dealing with nonlinear data assimilation. They have been applied to many scientific fields, but their application in geoscientific systems is limited because...

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Autores principales:	Yuxin Zhao, Shuo Yang, Di Zhou, Xiong Deng, Mengbin Zhu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	data assimilation particle filter equivalent weights particle filter coupled model Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/ae8dcb8a9ce342dcb65560dd3419e720
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spelling	oai:doaj.org-article:ae8dcb8a9ce342dcb65560dd3419e7202021-11-25T18:03:39ZThe Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model10.3390/jmse91111532077-1312https://doaj.org/article/ae8dcb8a9ce342dcb65560dd3419e7202021-10-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1153https://doaj.org/toc/2077-1312Data assimilation has been widely applied in atmospheric and oceanic forecasting systems and particle filters (PFs) have unique advantages in dealing with nonlinear data assimilation. They have been applied to many scientific fields, but their application in geoscientific systems is limited because of their inefficiency in standard settings systems. To address these issues, this paper further refines the statistical observation and localization scheme which used in the classic localized equivalent-weights particle filter with statistical observation (LEWPF-Sobs). The improved method retains the advantages of equivalent-weights particle filter (EWPF) and the localized particle filter (LPF), while further refinements incorporate the effect of time series on the reanalyzed data into the statistical observation calculations, in addition to incorporating the statistical observation proposal density into the localization scheme to further improve the assimilation accuracy under sparse observation conditions. In order to better simulate the geoscientific system, we choose an intermediate atmosphere-ocean-land coupled model (COAL-IC) as the experimental model and divide the experiment into two parts: standard observation and sparse observation, which are analyzed by the spatial distribution results and root mean square error (RMSE) histogram. In order to better analyze the characteristics of the improved method, this method was chosen to be analyzed in comparison with the localized weighted ensemble Kalman filter (LWEnKF), the LPF and classical LEWPF-Sobs. From the experimental results, it can be seen that the improved method is better than the LWEnKF and LPF methods for various observation conditions. The improved method reduces the RMSE by about 7% under standard observation conditions compared to the traditional method, while the advantage of the improved method is even more obvious under sparse observation conditions, where the RMSE is reduced by about 85% compared to the traditional method. In particular, this improved filter not only combine the advantage of the two algorithms, but also overcome the computing resources.Yuxin ZhaoShuo YangDi ZhouXiong DengMengbin ZhuMDPI AGarticledata assimilationparticle filterequivalent weights particle filtercoupled modelNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1153, p 1153 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data assimilation particle filter equivalent weights particle filter coupled model Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	data assimilation particle filter equivalent weights particle filter coupled model Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Yuxin Zhao Shuo Yang Di Zhou Xiong Deng Mengbin Zhu The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model
description	Data assimilation has been widely applied in atmospheric and oceanic forecasting systems and particle filters (PFs) have unique advantages in dealing with nonlinear data assimilation. They have been applied to many scientific fields, but their application in geoscientific systems is limited because of their inefficiency in standard settings systems. To address these issues, this paper further refines the statistical observation and localization scheme which used in the classic localized equivalent-weights particle filter with statistical observation (LEWPF-Sobs). The improved method retains the advantages of equivalent-weights particle filter (EWPF) and the localized particle filter (LPF), while further refinements incorporate the effect of time series on the reanalyzed data into the statistical observation calculations, in addition to incorporating the statistical observation proposal density into the localization scheme to further improve the assimilation accuracy under sparse observation conditions. In order to better simulate the geoscientific system, we choose an intermediate atmosphere-ocean-land coupled model (COAL-IC) as the experimental model and divide the experiment into two parts: standard observation and sparse observation, which are analyzed by the spatial distribution results and root mean square error (RMSE) histogram. In order to better analyze the characteristics of the improved method, this method was chosen to be analyzed in comparison with the localized weighted ensemble Kalman filter (LWEnKF), the LPF and classical LEWPF-Sobs. From the experimental results, it can be seen that the improved method is better than the LWEnKF and LPF methods for various observation conditions. The improved method reduces the RMSE by about 7% under standard observation conditions compared to the traditional method, while the advantage of the improved method is even more obvious under sparse observation conditions, where the RMSE is reduced by about 85% compared to the traditional method. In particular, this improved filter not only combine the advantage of the two algorithms, but also overcome the computing resources.
format	article
author	Yuxin Zhao Shuo Yang Di Zhou Xiong Deng Mengbin Zhu
author_facet	Yuxin Zhao Shuo Yang Di Zhou Xiong Deng Mengbin Zhu
author_sort	Yuxin Zhao
title	The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model
title_short	The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model
title_full	The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model
title_fullStr	The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model
title_full_unstemmed	The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model
title_sort	improved localized equivalent-weights particle filter with statistical observation in an intermediate coupled model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ae8dcb8a9ce342dcb65560dd3419e720
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The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model

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