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Classification of Convective Areas Using Decision Trees

Source: Journal of Atmospheric and Oceanic Technology:;2009:;volume( 026 ):;issue: 007:;page 1341

Author(s): Gagne, David John; McGovern, Amy; Brotzge, Jerry

Publisher: American Meteorological Society

Abstract: This paper presents an automated approach for classifying storm type from weather radar reflectivity using decision trees. Recent research indicates a strong relationship between storm type (morphology) and severe weather, ...

Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts

Source: Weather and Forecasting:;2014:;volume( 029 ):;issue: 004:;page 1024

Author(s): Gagne, David John; McGovern, Amy; Xue, Ming

Publisher: American Meteorological Society

Abstract: robabilistic quantitative precipitation forecasts challenge meteorologists due to the wide variability of precipitation amounts over small areas and their dependence on conditions at multiple spatial and temporal scales. ...

Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations

Source: Artificial Intelligence for the Earth Systems:;2024:;volume( 003 ):;issue: 003

Author(s): Fan, Da; Greybush, Steven J.; Clothiaux, Eugene E.; Gagne, David John

Publisher: American Meteorological Society

Diagnosing Storm Mode with Deep Learning in Convection-Allowing Models

Source: Monthly Weather Review:;2023:;volume( 151 ):;issue: 008:;page 2009

Author(s): Sobash, Ryan A.; Gagne, David John; Becker, Charlie L.; Ahijevych, David; Gantos, Gabrielle N.; Schwartz, Craig S.

Publisher: American Meteorological Society

Tornadic Supercell Environments Analyzed Using Surface and Reanalysis Data: A Spatiotemporal Relational Data-Mining Approach

Source: Journal of Applied Meteorology and Climatology:;2012:;volume( 051 ):;issue: 012:;page 2203

Author(s): Gagne, David John; McGovern, Amy; Basara, Jeffrey B.; Brown, Rodger A.

Publisher: American Meteorological Society

Abstract: klahoma Mesonet surface data and North American Regional Reanalysis data were integrated with the tracks of over 900 tornadic and nontornadic supercell thunderstorms in Oklahoma from 1994 to 2003 to observe the evolution ...

Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems

Source: Bulletin of the American Meteorological Society:;2015:;volume( 096 ):;issue: 008:;page 1388

Author(s): McGovern, Amy; Gagne, David John; Basara, Jeffrey; Hamill, Thomas M.; Margolin, David

Publisher: American Meteorological Society

Generative Ensemble Deep Learning Severe Weather Prediction from a Deterministic Convection-Allowing Model

Source: Artificial Intelligence for the Earth Systems:;2024:;volume( 003 ):;issue: 002

Author(s): Sha, Yingkai; Sobash, Ryan A.; Gagne, David John

Publisher: American Meteorological Society

Identifying and Categorizing Bias in AI/ML for Earth Sciences

Source: Bulletin of the American Meteorological Society:;2024:;volume( 105 ):;issue: 003:;page E567

Author(s): McGovern, Amy; Bostrom, Ann; McGraw, Marie; Chase, Randy J.; Gagne, David John; Ebert-Uphoff, Imme; Musgrave, Kate D.; Schumacher, Andrea

Publisher: American Meteorological Society

Exploring NWS Forecasters’ Assessment of AI Guidance Trustworthiness

Source: Weather and Forecasting:;2024:;volume( 039 ):;issue: 008:;page 1219

Author(s): Cains, Mariana G.; Wirz, Christopher D.; Demuth, Julie L.; Bostrom, Ann; Gagne, David John; McGovern, Amy; Sobash, Ryan A.; Madlambayan, Deianna

Publisher: American Meteorological Society

Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

Source: Weather and Forecasting:;2017:;volume( 032 ):;issue: 005:;page 1819

Author(s): Gagne, David John;McGovern, Amy;Haupt, Sue Ellen;Sobash, Ryan A.;Williams, John K.;Xue, Ming

Publisher: American Meteorological Society

Abstract: AbstractForecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching ...

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