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中国科学院大学学报 ›› 2013, Vol. 30 ›› Issue (5): 628-636.DOI: 10.7523/j.issn.2095-6134.2013.05.009

• 环境科学与地理学 • 上一篇    下一篇

基于集合卡尔曼滤波的太湖叶绿素a浓度同化试验系统设计及实现

王泽人1,2, 马荣华1, 段洪涛1, 张玉超1, 齐琳1,2   

  1. 1. 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2012-11-05 修回日期:2013-02-25 发布日期:2013-09-15
  • 基金资助:

    中国科学院知识创新重要方向性项目(KZCX2-XY-QN311,KZCX2-EW-QN308);国家自然科学基金(41171271,41171273)资助 

Design and implementation of an experimental data assimilation system for chlorophyll-a in Lake Taihu based on the ensemble Kalman filter

WANG Ze-Ren1,2, MA Rong-Hua1, DUAN Hong-Tao1, ZHANG Yu-Chao1, QI Lin1,2   

  1. 1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2012-11-05 Revised:2013-02-25 Published:2013-09-15
  • Contact: 马荣华,E-mail:mrhua2002@niglas.ac.cn

摘要:

以太湖作为研究区,把数据同化技术引入蓝藻水华预测研究,设计并实现了太湖叶绿素a质量浓度同化试验系统,该系统结合基于WASP原理的二维水动力水质耦合模型,采用集合卡尔曼滤波方法同化空间分辨率为250 m的MODIS叶绿素a质量浓度反演数据. 结果表明,利用同化技术将衡量模型预报值、分析值和观测值之间的偏差指标RMSE减小了15.5%,可以有效地提高叶绿素a质量浓度的预测精度.

关键词: 数据同化, 集合卡尔曼滤波, 叶绿素a, 藻华预测

Abstract:

We develop a novel data assimilation approach for the prediction of algal blooms in Lake Taihu. Our approach is based on a 2D ecological model which combines a physical model, 250 m MODIS inversion chlorophyll-a data, and an ensemble Kalman filter (EnKF) analysis scheme. Our results indicate that the data assimilation approach is reliable for predicting algal blooms in these complex waters.

Key words: data assimilation, EnKF, Chlorophyll-a, algal bloom forecast

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