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Hydrologic Data Assimilation with the Ensemble Kalman Filter (2002)
| Citations: | 89 - 7 self |
BibTeX
@MISC{Reichle02hydrologicdata,
author = {Rolf H. Reichle and Dennis B. Mclaughlin and Dara Entekhabi},
title = {Hydrologic Data Assimilation with the Ensemble Kalman Filter},
year = {2002}
}
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Abstract
Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter's performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed.
Keyphrases
ensemble kalman filter hydrologic data assimilation optimal smoother surface soil moisture synthetic experiment radiobrightness observation estimation accuracy ensemble size land surface small ensemble size dynamic variational method energy flux final update time soil moisture estimation robust error variance estimate actual error key variable land surface model state vector dimension climate prediction soil moisture non-gaussian forecast error
