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contributor authorKotsuki, Shunji
contributor authorKurosawa, Kenta
contributor authorOtsuka, Shigenori
contributor authorTerasaki, Koji
contributor authorMiyoshi, Takemasa
date accessioned2019-10-05T06:44:52Z
date available2019-10-05T06:44:52Z
date copyright4/3/2019 12:00:00 AM
date issued2019
identifier otherWAF-D-18-0164.1.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4263294
description abstractAbstractOver the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN?s global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)?s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial.
publisherAmerican Meteorological Society
titleGlobal Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights
typeJournal Paper
journal volume34
journal issue3
journal titleWeather and Forecasting
identifier doi10.1175/WAF-D-18-0164.1
journal fristpage701
journal lastpage714
treeWeather and Forecasting:;2019:;volume 034:;issue 003
contenttypeFulltext


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