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	A Trous Wavelet Based Four-Dimensional Evapotranspiration Assimilation
	Chen S. H.
	2016
关键词	A trous wavelet transform ensemble Kalman filter evapotranspiration four-dimensional variational assimilation generalized Gaussian distribution uncertainty estimation variational data assimilation ensemble kalman filter error covariances model equations parameter
英文摘要	Generalized Gaussian distribution (GGD) can more accurately model the uncertainties involved in evapotranspiration assimilation than Normal distribution for ensemble based assimilation methods. In this paper, GGD derived perturbing samples are introduced to track the flow characteristics of the uncertainties in four-dimensional variational assimilation (4Dva) after the parameters of GGD are estimated from the coefficients of a trous wavelet transform (AWT) using global convergence method. For dissecting the contribution of the multiscale nature of the uncertainties on 4Dva, cost function is in advance converted with AWT from time domain to frequency domain by using wavelet profile as the constraint. Then, the unmatch of background field from model solution is approximated with a linear combination of the perturbing samples, with which the implicit optimization problem is transformed into an explicit one. Finally, assimilated variables are updated by executing multiscale Kalman filter in frequency domain. The outlined method avoids the tangent linear models required in 4Dva so that it can be easily implemented. Evapotranspiration assimilations in Noah prove that the outlined method performs much better than ensemble Kalman filter and 4Dva derived 4DLETKF when there are errors in background initial field or the forcing.
出处	Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing
卷	9
期	1
页	467-474
语种	英语
ISSN号	1939-1404
DOI标识	10.1109/jstars.2015.2501542
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/42703]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Chen S. H.. A Trous Wavelet Based Four-Dimensional Evapotranspiration Assimilation. 2016.

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