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contributor authorOtsuka, Shigenori
contributor authorKotsuki, Shunji
contributor authorMiyoshi, Takemasa
date accessioned2017-06-09T17:37:22Z
date available2017-06-09T17:37:22Z
date copyright2016/10/01
date issued2016
identifier issn0882-8156
identifier otherams-88232.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231990
description abstractpace?time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space?time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency?s Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.
publisherAmerican Meteorological Society
titleNowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation
typeJournal Paper
journal volume31
journal issue5
journal titleWeather and Forecasting
identifier doi10.1175/WAF-D-16-0039.1
journal fristpage1409
journal lastpage1416
treeWeather and Forecasting:;2016:;volume( 031 ):;issue: 005
contenttypeFulltext


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