Sensitivity analysis on Lake Taihu data assimilation scheme of chlorophyll-a concentration

Abstract

太湖叶绿素a同化系统对于不同参数的敏感性将直接影响到该系统能否精确的估算太湖叶绿素a的浓度分布.利用2009年4月21日环境一号卫星(HJ-1B CCD2)影像数据反演太湖叶绿素a浓度场信息.以此作为背景场信息,结合基于集合均方根滤波的太湖叶绿素a同化系统,分析和评价了样本数目、同化时长、背景场误差、观测误差和模型误差对于同化系统性能的影响.结果表明:从计算成本、系统运行时间和同化效果等方面分析,当集合样本数目达到30~40左右时同化系统取得了较好的结果;同化系统对于背景场误差的估计变化不是很敏感,即初始场的估计是否准确对于同化系统的性能影响不是很大;同化系统对于模型误差和观测误差的变化较为敏感,不同的测试点位由于水体动力学性质不一,其敏感性的表现形式有所差异;利用数据同化方法可以有效地估算太湖叶绿素a浓度.;Sensibility of the Lake Taihu chlorophyll-a assimilation system to different parameters directly control the accuracy of estimate the chlorophyll-a concentration distribution when using this assimilation system. We used multispectral data of Environmental Satellite 1(HJ-1), obtained on April 21st, 2009, combined with in situ data to retrieve the concentration of chlorophyll-a in Lake Taihu. We developed a Lake Taihu chlorophyll-a data assimilation system based on ensemble square root Klaman filter(EnSRF) technique. Take the retrieved chlorophyll-a concentration of Lake Taihu as the initial background value, then combined with the data assimilation system to analyze the influence of the ensemble size, the assimilation time, the background error, the observation error and the model error on the assimilation system. The results indicate:taking the computing cost, time cost of system and the performance of the assimilation system into consideration, the assimilation system performs well when the ensemble size are 30-40; the assimilation system is not very sensitive to the accuracy of estimation of the background; both the observation and the model errors are very important for the performance of the system; different test stations have different water dynamic properties, so they have different performance; the estimation of chlorophyll-a concentration can be improved by using the data assimilation method.