%0 Journal Article
%T An Evapotranspiration Assimilation Method Based on Ensemble Kalman Filter and ид Trous Wavelet
%A Shaohui Chen
%A Jianwei Qi
%A Xiaomin Sun
%A Xiangzheng Deng
%A Jing Tian
%J Advances in Meteorology
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/531810
%X It is challenging to assimilate the evapotranspiration product (EP) retrieved from satellite data into land surface models (LSMs). In this paper, a perturbed ensemble Kalman filter (PEKF) and ид trous wavelet transform (AWT) integrated method are proposed to implement the evapotranspiration assimilation. In this method, the AWT is used to decompose the EPs into multiple channels since it is very powerful in fusing high frequency spatial information of multisource data, and then the Kalman filter is performed in the AWT domain. The proposed method combines the advantages of the PEKF that is capable of accommodating model error and observation error, and the AWT can effectively perform multiresolution fusion. Assimilation experiment conducted with the Noah model and the EP retrieved from the MODIS data shows that the proposed method performs better than the traditional ensemble Kalman filter (EnKF) and PEKF methods. The analysis results fit well with the evapotranspiration observation at two field sites with different land surface conditions. These indicate that the proposed method is promising for assimilating regional scale satellite retrieved EP into LSMs. 1. Introduction Evapotranspiration (ET) is an important component of the water and energy exchanges between the atmosphere and land surface. It is crucial to accurately estimate ET for studying global or regional water and energy balances. Hence, good quality of spatial and temporal ET production (EP) can help to improve comprehension of water and energy cycle. However, this kind of EP is generally difficult to obtain in both dimensions of space and time because ET is influenced by many factors, such as air and skin temperatures, soil moisture, vegetation fraction, and horizontal advection. Up to now, there are two approaches to estimate the ET. One is site observations or remote sensing retrievals. Site observations have high spatial resolutions, but can only provide the EP for limited spatial locations [1]. Remote sensing retrievals have high spatial resolutions and can cover large range, but can only retrieve the instantaneous EP. The other is land surface models (LSMs). LSMs are probably the most efficient approach for continuously estimating ET on a large range [1]. Because of the imperfection of the physics of LSMs and the uncertainties of input and driving data, the EP of the LSMs may contain significant errors. Hence, data assimilation (DA) has been applied to integrate observational ET into LSMs [2]. DA provides a framework for improving the LSMs by updating the state variables of the LSMs
%U http://www.hindawi.com/journals/amete/2013/531810/