Enhancing the Assimilation of SWOT Simulated Observations Using a Multi-Scale 4DVAR Method in Regional Ocean Modeling System

被引:0
|
作者
Zhou, Chaojie [1 ,2 ]
Cui, Wei [3 ]
Sun, Ruili [1 ]
Huang, Ying [1 ]
Zhuang, Zhanpeng [3 ]
机构
[1] Zhejiang Univ, Hainan Inst, Sanya 572024, Peoples R China
[2] Minist Nat Resources, Key Lab Marine Sci & Numer Modeling, Qingdao 266061, Peoples R China
[3] Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Peoples R China
关键词
SWOT SSH observations; ROMS-4DVAR; multi-scale assimilation; REANALYSIS; SCHEME; WATER;
D O I
10.3390/rs16050778
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents an innovative approach to enhance the assimilation of high-resolution simulated observations, specifically targeting Surface Water Ocean Topography (SWOT) Ka-band Radar Interferometer Sea Surface Height (SSH) products, within the Regional Ocean Modeling System (ROMS). Responding to the demand for improved assimilation techniques, we developed a multi-scale Four-Dimensional Variational Data Assimilation (4DVAR) system, building upon validated fine-scale correction capabilities from prior studies. The multi-scale strategy was extended to the ROMS-4DVAR system, providing a comprehensive solution for assimilating high-resolution observations. Leveraging the Observing System Simulation Experiment (OSSE) framework, we conducted a twin experiment comprising a nature run and a free run case. Subsequently, synthetic SWOT SSH measurements were decomposed, considering the model configuration resolution. These components, derived from dense SSH observations, were integrated into a two-step 4DVAR assimilation scheme. The first cycle targets large-scale features for model field correction, and the updated analysis serves as the background for the second assimilation step, addressing fine-scale observation components. Comparisons with the primitive ROMS-4DVAR using a single-scale scheme highlight the superiority of the multi-scale strategy in reducing gaps between the model and the SSH observations. The Root Mean Squared Error (RMSE) is halved, and the Mean Absolute Percentage Error (MAPE) decreases from 2.237% to 0.93%. The two-step assimilation procedure ensures comprehensive multi-scale updates in the SSH field simulation, enhancing fine-scale features in the analysis fields. The quantification of three-dimensional-model dynamic fields further validates the efficiency and superiority of the multi-scale 4DVAR approach, offering a robust methodology for assimilating high-resolution observations within the ROMS.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Multi-scale assimilation of simulated SWOT observations
    Souopgui, Innocent
    D'Addezio, Joseph M.
    Rowley, Clark D.
    Smith, Scott R.
    Jacobs, Gregg A.
    Helber, Robert W.
    Yaremchuk, Max
    Osborne, John J.
    [J]. OCEAN MODELLING, 2020, 154
  • [2] 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)
    Institute of Marine Sciences, University of California, Santa Cruz, CA 95064, United States
    不详
    不详
    不详
    不详
    不详
    不详
    [J]. Ocean Model., 3-4 (130-145):
  • [3] 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)
    Powell, B. S.
    Arango, H. G.
    Moore, A. M.
    Di Lorenzo, E.
    Milliff, R. F.
    Foley, D.
    [J]. OCEAN MODELLING, 2008, 25 (3-4) : 173 - 188
  • [4] 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)
    Powell, B. S.
    Arango, H. G.
    Moore, A. M.
    Di Lorenzo, E.
    Milliff, R. F.
    Foley, D.
    [J]. OCEAN MODELLING, 2008, 23 (3-4) : 130 - 145
  • [5] 4DVAR Data Assimilation with the Regional Ocean Modeling System (ROMS): Impact on the Water Mass Distributions in the Yellow Sea
    Lee, Joon-Ho
    Kim, Taekyun
    Pang, Ig-Chan
    Moon, Jae-Hong
    [J]. OCEAN SCIENCE JOURNAL, 2018, 53 (02) : 165 - 178
  • [6] 4DVAR Data Assimilation with the Regional Ocean Modeling System (ROMS): Impact on the Water Mass Distributions in the Yellow Sea
    Joon-Ho Lee
    Taekyun Kim
    Ig-Chan Pang
    Jae-Hong Moon
    [J]. Ocean Science Journal, 2018, 53 : 165 - 178
  • [7] Dynamical and Microphysical Retrieval from Simulated Doppler Radar Observations Using the 4DVAR Assimilation Technique
    许小永
    刘黎平
    郑国光
    [J]. Journal of Meteorological Research, 2005, (02) : 160 - 173
  • [8] Evaluation of a 4DVAR Assimilation System in the California Current at the SWOT Calibration/Validation Site
    Tchonang, Babette C.
    Archer, Matthew R.
    Gopalakrishnan, Ganesh
    Cornuelle, Bruce
    Mazloff, Matthew R.
    Wang, Jinbo
    Fu, Lee-Lueng
    [J]. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2024, 41 (06) : 533 - 549
  • [9] 4DVAR assimilation of multi-parameter radar observations in an explicit cloud model
    Wu, B
    Verlinde, J
    Sun, JZ
    [J]. 28TH CONFERENCE ON RADAR METEOROLOGY, 1997, : 532 - 533
  • [10] 4DVAR assimilation of ADCP data with the Navy Coastal Ocean model using the cycling representer method
    Smith, S. R.
    Ngodock, H. E.
    Jacobs, G. A.
    [J]. 2007 OCEANS, VOLS 1-5, 2007, : 1821 - 1833