Research Article
Using ensemble Kalman filter to assimilate land surface temperature and evapotranspiration
@INPROCEEDINGS{10.4108/adim.2010.4, author={Yang Wang and Yaonan Zhang and Guohui Zhao}, title={Using ensemble Kalman filter to assimilate land surface temperature and evapotranspiration}, proceedings={International Workshop on Advances in Data and Information Management}, publisher={IEEE}, proceedings_a={ADIM}, year={2011}, month={1}, keywords={Biological system modeling Data assimilation Data models Kalman filters Land surface Land surface temperature MODIS}, doi={10.4108/adim.2010.4} }- Yang Wang
Yaonan Zhang
Guohui Zhao
Year: 2011
Using ensemble Kalman filter to assimilate land surface temperature and evapotranspiration
ADIM
IEEE
DOI: 10.4108/adim.2010.4
Abstract
Ensemble Kalman filter (EnKF) is an efficient algorithm in dealing with nonlinear and discontinuous data assimilation problems. We designed a scheme that integrated the EnKF and Simplified Simple Biosphere model (SSiB) to improve the estimate of land surface temperature and evapotranspiration (ET) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products. This scheme can make a judgment whether there are MODSI LST products available to assimilate at every time step. Then we compared the assimilation results with SSiB open loop simulation and station observations. The results showed that the EnKF algorithm could improve the land surface temperature and evapotranspiration estimate. Then we discussed five challenges during the experiment. In a word, this scheme provides a practical way for improving land surface models estimates with assimilating remote sensing observations.
