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United States Department of Agriculture

Agricultural Research Service

Title: Correctinh Land Surface Model Predictions for the Impact of Sparsely Sampled Rainfall Rate Retrievals Using An Ensemble Kalman Filter and Remote Seurface Brightness Temperature Observations

Author
item Crow, Wade

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 29, 2003
Publication Date: October 15, 2003
Citation: Crow, W.T., 2003. Correcting land surface model predictions for the impact of sparsely sampled rainfall rate retrievals using an ensemble kalman filter and remote surface brightness temperature. Journal of Hydrometeorology. 4:960-973.

Interpretive Summary: For large portions of the world, inaccurate rainfall observations are a major impediment for effort to monitoring root-zone soil moisture and surface evapotranspiration. While spaceborne precipitation measurements offer a valuable global capability, next-generation systems will allow for only 8 measurements of rainfall rate per day at any single point. Sampling errors associated with such sparse sampling rates are expected to be large. This paper details strategy for correcting the impact of rainfall sampling errors on land surface model predictions of root-zone soil moisture and surface evapotranspiration. The strategy is based on using a statistical representation of rainfall uncertainty to generate ensembles of land surface model predictions that are then updated using a Kalman filter and direct observations of surface soil moisture from a spaceborne sensor. Consequently, it offers a framework for the efficient data fusion of spaceborne rainfall and surface soil moisture retrievals.

Technical Abstract: Current attempts to measure short-term (< 1 month) rainfall accumulations using spaceborne radiometers are characterized by large sampling errors associated with relatively infrequent observation rates (2 to 8 measurements per day). This degrades the value of spaceborne rainfall retrievals for the monitoring of surface water and energy balance processes. Here a data assimilation system, based on the assimilation of surface L-band brightness temperature (T_B) observations via the Ensemble Kalman Filter (EnKF), is introduced to correct for the impact of poorly sampled rainfall on land surface model predictions of root-zone soil moisture and surface energy fluxes. The system is evaluated during the period April 1, 1997 to March 31, 1998 over two sites within the United States Southern Great Plains. This evaluation includes both a fraternal twin experiment based on synthetically generated T_B measurements and the assimilation of real T_B observations acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97). Results suggest that the EnKF-based assimilation system is capable of correcting a substantial fraction (> 50%) of model error in root-zone (40-cm) soil moisture and latent heat flux predictions associated with the use of temporally sparse rainfall measurements as forcing data. Comparable gains in accuracy are demonstrated when actual T_B measurements made during the SGP97 experiment are assimilated. The appropriateness of the EnKF for this particular application, relative to other possible data assimilation strategies, is discussed.

Last Modified: 9/2/2014
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