DocumentCode
2017
Title
Assimilation of MODIS Chlorophyll-a Data Into a Coupled Hydrodynamic-Biological Model of Taihu Lake
Author
Lin Qi ; Ronghua Ma ; Weiping Hu ; Loiselle, Steven A.
Author_Institution
State Key Lab. of Lake Sci. & Environ., Nanjing Inst. of Geogr. & Limnology, Nanjing, China
Volume
7
Issue
5
fYear
2014
fDate
May-14
Firstpage
1623
Lastpage
1631
Abstract
MODIS chlorophyll-a concentration (Chla) data were assimilated into a coupled hydrodynamic-biological model using an Optimal Interpolation method. Simulations were conducted using MODIS data covering Taihu Lake in May 2009, when algal blooms typically begin to occur. The results of the assimilation approach showed improvements in the estimation of Chla distributions in spatial coherency and temporal continuity. Bias of assimilation (model run after assimilation) was 5.1%, with a RMSE of 49.7%. In comparison, the free run (model run without assimilation) had a bias of -34.9% and RMSE of 176.5%. In situ data used for comparison showed reduced RMSE and the Bias for assimilation. Two sensitivity experiments were used to determine the suitable correlation length scale with respect to observation data accuracy. The result showed that 500m is the optimum scale to construct the background error covariance matrix. The sensitivity experiment of observational data accuracy also showed that more accurate observation data allowed for better assimilation results.
Keywords
covariance matrices; data assimilation; geophysical signal processing; interpolation; lakes; microorganisms; remote sensing; AD 2009 05; China; MODIS Chlorophyll-a data assimilation; Taihu Lake; background error covariance matrix; coupled hydrodynamic-biological model; optimal interpolation method; spatial coherency; temporal continuity; Biological system modeling; Data models; Lakes; MODIS; Mathematical model; Numerical models; Water pollution; Chlorophyll-a; Data assimilation; MODIS; Taihu Lake; optimum interpolation;
fLanguage
English
Journal_Title
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Publisher
ieee
ISSN
1939-1404
Type
jour
DOI
10.1109/JSTARS.2013.2280815
Filename
6675888
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