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    Physics-Constrained Deep Learning Postprocessing of Temperature and Humidity 

    Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 004
    Author(s): Zanetta, Francesco; Nerini, Daniele; Beucler, Tom; Liniger, Mark A.
    Publisher: American Meteorological Society
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    A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles 

    Source: Monthly Weather Review:;2019:;volume 147:;issue 003:;page 987
    Author(s): Nerini, Daniele; Foresti, Loris; Leuenberger, Daniel; Robert, Sylvain; Germann, Urs
    Publisher: American Meteorological Society
    Abstract: AbstractA Bayesian precipitation nowcasting system based on the ensemble Kalman filter is formulated. Starting from the last available radar observations, the prediction step of the filter consists of a stochastic radar ...
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    A Comparative Analysis of TRMM–Rain Gauge Data Merging Techniques at the Daily Time Scale for Distributed Rainfall–Runoff Modeling Applications 

    Source: Journal of Hydrometeorology:;2015:;Volume( 016 ):;issue: 005:;page 2153
    Author(s): Nerini, Daniele; Zulkafli, Zed; Wang, Li-Pen; Onof, Christian; Buytaert, Wouter; Lavado-Casimiro, Waldo; Guyot, Jean-Loup
    Publisher: American Meteorological Society
    Abstract: his study compares two nonparametric rainfall data merging methods?the mean bias correction and double-kernel smoothing?with two geostatistical methods?kriging with external drift and Bayesian combination?for optimizing ...
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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