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    LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration 

    Source: Journal of Hydrometeorology:;2009:;Volume( 010 ):;issue: 005:;page 1081
    Author(s): Bellerby, Tim; Hsu, Kuo-lin; Sorooshian, Soroosh
    Publisher: American Meteorological Society
    Abstract: The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated ...
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    LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part II: Validation 

    Source: Journal of Hydrometeorology:;2009:;Volume( 010 ):;issue: 005:;page 1096
    Author(s): Hsu, Kuo-lin; Bellerby, Tim; Sorooshian, S.
    Publisher: American Meteorological Society
    Abstract: A new satellite-based rainfall monitoring algorithm that integrates the strengths of both low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite information has been developed. The Lagrangian Model ...
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    A Statistical Model for the Uncertainty Analysis of Satellite Precipitation Products 

    Source: Journal of Hydrometeorology:;2015:;Volume( 016 ):;issue: 005:;page 2101
    Author(s): Sarachi, Sepideh; Hsu, Kuo-lin; Sorooshian, Soroosh
    Publisher: American Meteorological Society
    Abstract: arth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying ...
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    An Artificial Neural Network Model to Reduce False Alarms in Satellite Precipitation Products Using MODIS and CloudSat Observations 

    Source: Journal of Hydrometeorology:;2013:;Volume( 014 ):;issue: 006:;page 1872
    Author(s): Nasrollahi, Nasrin; Hsu, Kuolin; Sorooshian, Soroosh
    Publisher: American Meteorological Society
    Abstract: he Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Earth Observing System (EOS) Aqua and Terra platform with 36 spectral bands provides valuable information about cloud microphysical ...
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    Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System 

    Source: Journal of Applied Meteorology:;2004:;volume( 043 ):;issue: 012:;page 1834
    Author(s): Hong, Yang; Hsu, Kuo-Lin; Sorooshian, Soroosh; Gao, Xiaogang
    Publisher: American Meteorological Society
    Abstract: A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts ...
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    A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products 

    Source: Journal of Hydrometeorology:;2016:;Volume( 017 ):;issue: 003:;page 931
    Author(s): Tao, Yumeng; Gao, Xiaogang; Hsu, Kuolin; Sorooshian, Soroosh; Ihler, Alexander
    Publisher: American Meteorological Society
    Abstract: espite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and ...
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    Using Densely Distributed Soil Moisture Observations for Calibration of a Hydrologic Model 

    Source: Journal of Hydrometeorology:;2015:;Volume( 017 ):;issue: 002:;page 571
    Author(s): Thorstensen, Andrea; Nguyen, Phu; Hsu, Kuolin; Sorooshian, Soroosh
    Publisher: American Meteorological Society
    Abstract: alibration is a crucial step in hydrologic modeling that is typically handled by tuning parameters to match an observed hydrograph. In this research, an alternative calibration scheme based on soil moisture was investigated ...
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    Precipitation Identification with Bispectral Satellite Information Using Deep Learning Approaches 

    Source: Journal of Hydrometeorology:;2017:;Volume( 018 ):;issue: 005:;page 1271
    Author(s): Tao, Yumeng; Gao, Xiaogang; Ihler, Alexander; Sorooshian, Soroosh; Hsu, Kuolin
    Publisher: American Meteorological Society
    Abstract: n the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information ...
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    A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information 

    Source: Journal of Hydrometeorology:;2018:;volume 019:;issue 002:;page 393
    Author(s): Tao, Yumeng; Hsu, Kuolin; Ihler, Alexander; Gao, Xiaogang; Sorooshian, Soroosh
    Publisher: American Meteorological Society
    Abstract: AbstractCompared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based ...
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    Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin 

    Source: Journal of Hydrometeorology:;2021:;volume( 022 ):;issue: 001:;page 217
    Author(s): Ombadi, Mohammed;Nguyen, Phu;Sorooshian, Soroosh;Hsu, Kuo-lin
    Publisher: American Meteorological Society
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    DSpace software copyright © 2002-2015  DuraSpace
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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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