An Observing System Simulation Experiments framework based on the ensemble square root Kalman Filter for evaluating the concentration of chlorophyll a by multi-source data: A case study in Taihu Lake

Authors

  • Yuan Li Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China
  • Yun-Mei Li Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China
  • Qiao Wang Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China
  • Li Zhu Satellite Environment Application Center, Ministry of Environmental Protection, Beijing 100029, China
  • Yu-Long Guo Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China

Keywords:

EnSRF, remote sensing

Abstract

Data assimilation is a method to produce a description of the system state, as accurately as possible, under the control of observations by using all the available information and by taking into account the observation and model errors. We developed a framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Klaman filter (EnSRF) technique, and the framework could assimilate two data sets of chlorophyll a retrieved from Environmental Satellite 1 (HJ-1) and moderate resolution imaging spectro-radiometer (MODIS) onboard the Terra platform, separately. We assumed that one of the retrieved results was the proxy “truth value” and the other one contained errors. Based on EnSRF technique, combined with the three dimensional numerical model of wind-driven circulation and pollutant transportation in a large-scale lake, we investigated the potential impact of location distributions of simulated observation stations in Taihu Lake (China) on the performance of data assimilation. In addition, the effectiveness of this method for evaluation and prediction of the concentration of chlorophyll a was validated. The results showed that the location of simulated observation stations not only influenced the accuracy of evaluating and forecasting results, but also the performance of data assimilation. We also discuss the impact of assimilation time and background error on the results. This study demonstrated that this method of data assimilation is effective for evaluation and prediction of the concentration of chlorophyll a in highly turbid case 2 waters.

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Published

2014-07-03

Issue

Vol. 17 No. 3 (2014): Application of Remote Sensing for Assessing Aquatic Ecosystem Health of China

Section

Research article

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