A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit <a rel="external noopener" href="http://hydrology.princeton.edu/~mpan/academics/uploads/content/articles/2009JHM1155.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="American Meteorological Society">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i5od5eofrnhevoxsbkipb4pmxi" style="color: black;">Journal of Hydrometeorology</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1175/2009jhm1155.1">doi:10.1175/2009jhm1155.1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/arnkfbvvjbhjrgn7dcm73ykweq">fatcat:arnkfbvvjbhjrgn7dcm73ykweq</a> </span>
more »... 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.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120712183758/http://hydrology.princeton.edu/~mpan/academics/uploads/content/articles/2009JHM1155.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/81/a1/81a10a4f4d69a0c80195395718c18e4d0260d27c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1175/2009jhm1155.1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>