19 March 2018 Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation
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Abstract
Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Kai Wu, Kai Wu, Hong Shu, Hong Shu, Lei Nie, Lei Nie, Zhenhang Jiao, Zhenhang Jiao, } "Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation," Journal of Applied Remote Sensing 12(1), 016039 (19 March 2018). https://doi.org/10.1117/1.JRS.12.016039 . Submission: Received: 15 October 2017; Accepted: 9 February 2018
Received: 15 October 2017; Accepted: 9 February 2018; Published: 19 March 2018
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