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    Understanding Spatial Context in Convolutional Neural Networks Using Explainable Methods: Application to Interpretable GREMLIN 

    Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 003
    Author(s): Hilburn, Kyle A.
    Publisher: American Meteorological Society
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    Validating GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) Product over CONUS 

    Source: Journal of Applied Meteorology and Climatology:;2024:;volume( 063 ):;issue: 003:;page 471
    Author(s): Lee, Yoonjin; Hilburn, Kyle
    Publisher: American Meteorological Society
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    Evaluation, Tuning and Interpretation of Neural Networks for Working with Images in Meteorological Applications 

    Source: Bulletin of the American Meteorological Society:;2020:;volume( ):;issue: -:;page 1
    Author(s): Ebert-Uphoff, Imme;Hilburn, Kyle
    Publisher: American Meteorological Society
    Abstract: This article discusses strategies for the development of neural networks (aka deep learning) for meteorological applications. Topics include evaluation, tuning and interpretation of neural networks for working with ...
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    Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations 

    Source: Journal of Applied Meteorology and Climatology:;2021:;volume( 060 ):;issue: 001:;page 3
    Author(s): Hilburn, Kyle A.;Ebert-Uphoff, Imme;Miller, Steven D.
    Publisher: American Meteorological Society
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    Using Microwave Satellite Data to Assess Changes in Storminess over the Pacific Ocean 

    Source: Monthly Weather Review:;2015:;volume( 143 ):;issue: 008:;page 3214
    Author(s): Kruk, Michael C.; Hilburn, Kyle; Marra, John J.
    Publisher: American Meteorological Society
    Abstract: his study analyzes 25 years of Special Sensor Microwave Imager (SSM/I) retrievals of rain rate and wind speed to assess changes in storminess over the open water of the Pacific Ocean. Changes in storminess are characterized ...
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    Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts 

    Source: Artificial Intelligence for the Earth Systems:;2024:;volume( 003 ):;issue: 003
    Author(s): Shaddy, Bryan; Ray, Deep; Farguell, Angel; Calaza, Valentina; Mandel, Jan; Haley, James; Hilburn, Kyle; Mallia, Derek V.; Kochanski, Adam; Oberai, Assad
    Publisher: American Meteorological Society
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    An Evaluation of NOAA Modeled and In Situ Soil Moisture Values and Variability across the Continental United States 

    Source: Weather and Forecasting:;2024:;volume( 039 ):;issue: 003:;page 523
    Author(s): Marinescu, Peter J.; Abdi, Daniel; Hilburn, Kyle; Jankov, Isidora; Lin, Liao-Fan
    Publisher: American Meteorological Society
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