{"title":"海洋环流模式NEMO数据同化方法及其在俄罗斯北极地区海洋特征计算中的应用","authors":"K. Belyaev, A. Kuleshov, I. Smirnov","doi":"10.1109/ISPRAS47671.2019.00019","DOIUrl":null,"url":null,"abstract":"Based on the GKF (Generalized Kalman Filter) data assimilation method, which we have proposed earlier, jointly with the NEMO (Nucleus for European Modelling of the Ocean) model of ocean circulation, the spatial-temporal variability of several model characteristics, in particular, the ocean level field and the water temperature field in the Arctic Zone of Russia, is studied in numerical experiments. The ocean level data taken from the AVISO (Archiving, Validating and Interpolation Satellite Observation) archive are assimilated into the NEMO model. The ocean level and temperature are calculated both with and without data assimilation (the control run). The results of calculations are analyzed and it is shown that the main spatial variability of the characteristics after data assimilation is in a good agreement with the localization of currents in the Northern Atlantics and the Arctic Zone of Russia. The authors have performed the installation and adaptation of the NEMO software package on the K-60 high-performance computer (HPC) in the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences (Moscow, Russia). The qualitative estimate of this variability is presented and it is shown at what time interval the dependence of the calculated characteristics on the observation data manifests itself.","PeriodicalId":154688,"journal":{"name":"2019 Ivannikov Ispras Open Conference (ISPRAS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Data Assimilation Method for the Ocean Circulation Model NEMO and Its Application for the Calculation of Ocean Characteristics in the Arctic Zone of Russia\",\"authors\":\"K. Belyaev, A. Kuleshov, I. Smirnov\",\"doi\":\"10.1109/ISPRAS47671.2019.00019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the GKF (Generalized Kalman Filter) data assimilation method, which we have proposed earlier, jointly with the NEMO (Nucleus for European Modelling of the Ocean) model of ocean circulation, the spatial-temporal variability of several model characteristics, in particular, the ocean level field and the water temperature field in the Arctic Zone of Russia, is studied in numerical experiments. The ocean level data taken from the AVISO (Archiving, Validating and Interpolation Satellite Observation) archive are assimilated into the NEMO model. The ocean level and temperature are calculated both with and without data assimilation (the control run). The results of calculations are analyzed and it is shown that the main spatial variability of the characteristics after data assimilation is in a good agreement with the localization of currents in the Northern Atlantics and the Arctic Zone of Russia. The authors have performed the installation and adaptation of the NEMO software package on the K-60 high-performance computer (HPC) in the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences (Moscow, Russia). The qualitative estimate of this variability is presented and it is shown at what time interval the dependence of the calculated characteristics on the observation data manifests itself.\",\"PeriodicalId\":154688,\"journal\":{\"name\":\"2019 Ivannikov Ispras Open Conference (ISPRAS)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Ivannikov Ispras Open Conference (ISPRAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPRAS47671.2019.00019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Ivannikov Ispras Open Conference (ISPRAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPRAS47671.2019.00019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
基于本文提出的GKF (Generalized Kalman Filter)数据同化方法,结合NEMO (Nucleus for European Modelling of the Ocean)海洋环流模式,通过数值实验研究了俄罗斯北极地区几种模式特征,特别是海平面场和水温场的时空变异性。来自AVISO(存档、验证和插值卫星观测)存档的海平面数据被同化到NEMO模型中。在数据同化和不同化的情况下计算海平面和温度(对照运行)。对计算结果进行了分析,结果表明,同化后的主要空间变异性与北大西洋和俄罗斯北极地区的海流局域化具有较好的一致性。作者在俄罗斯科学院Keldysh应用数学研究所(莫斯科,俄罗斯)的K-60高性能计算机(HPC)上进行了NEMO软件包的安装和适配。本文给出了这种变率的定性估计,并说明了计算特征对观测数据的依赖性在什么时间间隔内表现出来。
Data Assimilation Method for the Ocean Circulation Model NEMO and Its Application for the Calculation of Ocean Characteristics in the Arctic Zone of Russia
Based on the GKF (Generalized Kalman Filter) data assimilation method, which we have proposed earlier, jointly with the NEMO (Nucleus for European Modelling of the Ocean) model of ocean circulation, the spatial-temporal variability of several model characteristics, in particular, the ocean level field and the water temperature field in the Arctic Zone of Russia, is studied in numerical experiments. The ocean level data taken from the AVISO (Archiving, Validating and Interpolation Satellite Observation) archive are assimilated into the NEMO model. The ocean level and temperature are calculated both with and without data assimilation (the control run). The results of calculations are analyzed and it is shown that the main spatial variability of the characteristics after data assimilation is in a good agreement with the localization of currents in the Northern Atlantics and the Arctic Zone of Russia. The authors have performed the installation and adaptation of the NEMO software package on the K-60 high-performance computer (HPC) in the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences (Moscow, Russia). The qualitative estimate of this variability is presented and it is shown at what time interval the dependence of the calculated characteristics on the observation data manifests itself.