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    Improving Probabilistic Quantitative Precipitation Forecasts Using Short Training Data through Artificial Neural Networks 

    Source: Journal of Hydrometeorology:;2022:;volume( 023 ):;issue: 009:;page 1365
    Author(s): Mohammadvaghef Ghazvinian; Yu Zhang; Thomas M. Hamill; Dong-Jun Seo; Nelun Fernando
    Publisher: American Meteorological Society
    Abstract: Conventional statistical postprocessing techniques offer limited ability to improve the skills of probabilistic guidance for heavy precipitation. This paper introduces two artificial neural network (ANN)-based, geographically ...
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    North American Climate in CMIP5 Experiments. Part I: Evaluation of Historical Simulations of Continental and Regional Climatology 

    Source: Journal of Climate:;2013:;volume( 026 ):;issue: 023:;page 9209
    Author(s): Sheffield, Justin; Barrett, Andrew P.; Colle, Brian; Nelun Fernando, D.; Fu, Rong; Geil, Kerrie L.; Hu, Qi; Kinter, Jim; Kumar, Sanjiv; Langenbrunner, Baird; Lombardo, Kelly; Long, Lindsey N.; Maloney, Eric; Mariotti, Annarita; Meyerson, Joyce E.; Mo, Kingtse C.; David Neelin, J.; Nigam, Sumant; Pan, Zaitao; Ren, Tong; Ruiz-Barradas, Alfredo; Serra, Yolande L.; Seth, Anji; Thibeault, Jeanne M.; Stroeve, Julienne C.; Yang, Ze; Yin, Lei
    Publisher: American Meteorological Society
    Abstract: his is the first part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the historical simulations of continental and regional climatology with ...
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