A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part II: Application to Land Surface Modeling with Satellite Rainfall Forcing

Ming Pan, Eric F. Wood
<span title="">2009</span> <i title="American Meteorological Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i5od5eofrnhevoxsbkipb4pmxi" style="color: black;">Journal of Hydrometeorology</a> </i> &nbsp;
Part I of this series of studies developed procedures to implement the multiscale filtering algorithm for land surface hydrology and performed assimilation experiments with rainfall ensembles from a climate model. However, a most important application of the multiscale technique is to assimilate satellite-based remote sensing observations into a land surface model-and this has not been realized. This paper focuses on enabling the multiscale assimilation system to use remotely sensed
more &raquo; ... n data. The major challenge is the generation of a rainfall ensemble given one satellite rainfall map. An acceptable rainfall ensemble must contain a proper multiscale spatial correlation structure, and each ensemble member presents a realistic rainfall process in both space and time. A pattern-based sampling approach is proposed, in which random samples are drawn from a historical rainfall database according to the pattern of the satellite rainfall and then a cumulative distribution function matching procedure is applied to ensure the proper statistics for the pixellevel rainfall intensity. The assimilation system is applied using Tropical Rainfall Measuring Mission real-time satellite rainfall over the Red-Arkansas River basin. Results show that the ensembles so generated satisfy the requirements for spatial correlation and realism and the multiscale assimilation works reasonably well. A number of limitations also exist in applying this generation method, mainly stemming from the high dimensionality of the problem and the lack of historical records.
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