Artículos con órdenes de acceso público - Amy McGovernMás información
No disponibles en ningún lugar: 2
CREST-iMAP v1. 0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction
Z Li, M Chen, S Gao, X Luo, JJ Gourley, P Kirstetter, T Yang, R Kolar, ...
Environmental Modelling & Software 141, 105051, 2021
Órdenes: US National Science Foundation
Spot the Difference: Tornado Visualizations
G Foss, A McGovern, CK Potvin, B Dahl, G Abram, A Bowen, N Hulkoti, ...
Proceedings of the Practice and Experience in Advanced Research Computing …, 2017
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Disponibles en algún lugar: 37
Making the black box more transparent: Understanding the physical implications of machine learning
A McGovern, R Lagerquist, DJ Gagne, GE Jergensen, KL Elmore, ...
Bulletin of the American Meteorological Society 100 (11), 2175-2199, 2019
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Using artificial intelligence to improve real-time decision-making for high-impact weather
A McGovern, KL Elmore, DJ Gagne, SE Haupt, CD Karstens, R Lagerquist, ...
Bulletin of the American Meteorological Society 98 (10), 2073-2090, 2017
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles
DJ Gagne, A McGovern, SE Haupt, RA Sobash, JK Williams, M Xue
Weather and forecasting 32 (5), 1819-1840, 2017
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Deep learning for spatially explicit prediction of synoptic-scale fronts
R Lagerquist, A McGovern, DJ Gagne II
Weather and Forecasting 34 (4), 1137-1160, 2019
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Machine learning for real-time prediction of damaging straight-line convective wind
R Lagerquist, A McGovern, T Smith
Weather and Forecasting 32 (6), 2175-2193, 2017
Órdenes: US National Oceanic and Atmospheric Administration
Deep learning on three-dimensional multiscale data for next-hour tornado prediction
R Lagerquist, A McGovern, CR Homeyer, DJ Gagne II, T Smith
Monthly Weather Review 148 (7), 2837-2861, 2020
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science
A McGovern, I Ebert-Uphoff, DJ Gagne, A Bostrom
Environmental Data Science 1, e6, 2022
Órdenes: US National Science Foundation
Outlook for exploiting artificial intelligence in the earth and environmental sciences
SA Boukabara, V Krasnopolsky, SG Penny, JQ Stewart, A McGovern, ...
Bulletin of the American Meteorological Society 102 (5), E1016-E1032, 2021
Órdenes: US National Science Foundation, US Department of Defense, US National …
Calibration of machine learning–based probabilistic hail predictions for operational forecasting
A Burke, N Snook, DJ Gagne II, S McCorkle, A McGovern
Weather and Forecasting 35 (1), 149-168, 2020
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Classifying convective storms using machine learning
GE Jergensen, A McGovern, R Lagerquist, T Smith
Weather and Forecasting 35 (2), 537-559, 2020
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Evaluating knowledge to support climate action: A framework for sustained assessment. Report of an independent advisory committee on applied climate assessment
RH Moss, S Avery, K Baja, M Burkett, AM Chischilly, J Dell, PA Fleming, ...
Weather, Climate, and Society 11 (3), 465-487, 2019
Órdenes: US Department of Energy, US National Oceanic and Atmospheric Administration
A machine learning tutorial for operational meteorology. Part I: Traditional machine learning
RJ Chase, DR Harrison, A Burke, GM Lackmann, A McGovern
Weather and Forecasting 37 (8), 1509-1529, 2022
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks
C Chilson, K Avery, A McGovern, E Bridge, D Sheldon, J Kelly
Remote Sensing in Ecology and Conservation 5 (1), 20-32, 2019
Órdenes: US National Science Foundation
Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status, and outlook
PD Dueben, MG Schultz, M Chantry, DJ Gagne, DM Hall, A McGovern
Artificial Intelligence for the Earth Systems 1 (3), e210002, 2022
Órdenes: US National Science Foundation, Helmholtz Association, European Commission
Using machine learning to generate storm-scale probabilistic guidance of severe weather hazards in the Warn-on-Forecast system
ML Flora, CK Potvin, PS Skinner, S Handler, A McGovern
Monthly Weather Review 149 (5), 1535-1557, 2021
Órdenes: US National Oceanic and Atmospheric Administration
Evaluation of statistical learning configurations for gridded solar irradiance forecasting
DJ Gagne II, A McGovern, SE Haupt, JK Williams
Solar Energy 150, 383-393, 2017
Órdenes: US National Science Foundation
Postprocessing next-day ensemble probabilistic precipitation forecasts using random forests
ED Loken, AJ Clark, A McGovern, M Flora, K Knopfmeier
Weather and Forecasting 34 (6), 2017-2044, 2019
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
The history and practice of AI in the environmental sciences
SE Haupt, DJ Gagne, WW Hsieh, V Krasnopolsky, A McGovern, ...
Bulletin of the American Meteorological Society 103 (5), E1351-E1370, 2022
Órdenes: US National Science Foundation, US National Oceanic and Atmospheric …
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