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    Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model 

    Source: Monthly Weather Review:;2017:;volume( 145 ):;issue: 012:;page 4977
    Author(s): Kotsuki, Shunji;Greybush, Steven J.;Miyoshi, Takemasa
    Publisher: American Meteorological Society
    Abstract: AbstractWith the serial treatment of observations in the ensemble Kalman filter (EnKF), the assimilation order of observations is usually assumed to have no significant impact on analysis accuracy. However, Nerger derived ...
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    Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation 

    Source: Weather and Forecasting:;2016:;volume( 031 ):;issue: 005:;page 1409
    Author(s): Otsuka, Shigenori; Kotsuki, Shunji; Miyoshi, Takemasa
    Publisher: American Meteorological Society
    Abstract: pace?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 ...
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    Emulating Rainfall–Runoff-Inundation Model Using Deep Neural Network with Dimensionality Reduction 

    Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 001
    Author(s): Momoi, Masahiro; Kotsuki, Shunji; Kikuchi, Ryota; Watanabe, Satoshi; Yamada, Masafumi; Abe, Shiori
    Publisher: American Meteorological Society
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    Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights 

    Source: Weather and Forecasting:;2019:;volume 034:;issue 003:;page 701
    Author(s): Kotsuki, Shunji; Kurosawa, Kenta; Otsuka, Shigenori; Terasaki, Koji; Miyoshi, Takemasa
    Publisher: American Meteorological Society
    Abstract: AbstractOver 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 ...
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    Emulating Rainfall–Runoff-Inundation Model Using Deep Neural Network with Dimensionality Reduction 

    Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 001
    Author(s): Momoi, Masahiro; Kotsuki, Shunji; Kikuchi, Ryota; Watanabe, Satoshi; Yamada, Masafumi; Abe, Shiori
    Publisher: American Meteorological Society
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