Amy McGovern

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David John Gagne IINational Center for Atmospheric ResearchVerified email at ucar.edu
Travis M. SmithCooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO)Verified email at ou.edu
Montgomery FloraPostDoctoral Research AssociateVerified email at noaa.gov
Ann BostromWeyerhaeuser Endowed Professor in Environmental Policy, Evans School of Public Policy & GovernanceVerified email at uw.edu
Imme Ebert-UphoffColorado State UniversityVerified email at colostate.edu
Sue Ellen HauptNational Center for Atmospheric ResearchVerified email at ucar.edu
Philippe TissotConrad Blucher Institute, Texas A&M University-Corpus ChristiVerified email at tamucc.edu
Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Ming Xue, Ph.D., FAMS, FAGUUniversity of OklahomaVerified email at ou.edu
David HarrisonCooperative Institute for Severe and High-Impact Weather Research and Operations, The University ofVerified email at ou.edu
Corey K PotvinResearch Scientist, NOAA National Severe Storms LabVerified email at noaa.gov
Kimberly L. ElmoreResearch Scientist, Cooperative Institute for Mesoscale Meteorological StudiesVerified email at noaa.gov
Jason HickeyGoogle ResearchVerified email at google.com
Ryan SobashNational Center for Atmospheric ResearchVerified email at ucar.edu
John AllenCentral Michigan UniversityVerified email at cmich.edu
Richard S. SuttonKeen, Amii, and University of AlbertaVerified email at richsutton.com
Cameron R HomeyerAssociate Professor, School of Meteorology, University of OklahomaVerified email at ou.edu
Kelvin DroegemeierUniversity of OklahomaVerified email at ou.edu
Randy J. ChaseResearch Scientist, Cooperative Institute for Research in the Atmosphere, Colorado State UniversityVerified email at colostate.edu
Andrew FaggUniversity of OklahomaVerified email at cs.ou.edu

Amy McGovern

University of Oklahoma

Verified email at ou.edu - Homepage

Artificial Intelligence Machine Learning Severe Weather


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Automatic discovery of subgoals in reinforcement learning using diverse density A McGovern, AG Barto	664	2001
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	477	2019
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	452	2017
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	202	2017
Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction A McGovern, DH Rosendahl, RA Brown, KK Droegemeier Data Mining and Knowledge Discovery 22, 232-258, 2011	162	2011
Roles of macro-actions in accelerating reinforcement learning A McGovern, RS Sutton, AH Fagg Grace Hopper celebration of women in computing 1317, 15, 1997	140	1997
Autonomous discovery of temporal abstractions from interaction with an environment EA Mcgovern University of Massachusetts Amherst, 2002	128	2002
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	113	2019
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	110	2017
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	105	2020
Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts DJ Gagne, A McGovern, M Xue Weather and Forecasting 29 (4), 1024-1043, 2014	93	2014
Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science A McGovern, I Ebert-Uphoff, DJ Gagne II, A Bostrom Environmental Data Science 1, e6, 2022	86	2022
Macro-actions in reinforcement learning: An empirical analysis A McGovern, RS Sutton Computer Science Department Faculty Publication Series, 15, 1998	85	1998
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	77	2021
Classification of convective areas using decision trees DJ Gagne, A McGovern, J Brotzge Journal of Atmospheric and Oceanic Technology 26 (7), 1341-1353, 2009	74	2009
Exploiting relational structure to understand publication patterns in high-energy physics A McGovern, L Friedland, M Hay, B Gallagher, A Fast, J Neville, D Jensen Acm Sigkdd Explorations Newsletter 5 (2), 165-172, 2003	74	2003
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	69	2020
Classifying convective storms using machine learning GE Jergensen, A McGovern, R Lagerquist, T Smith Weather and Forecasting 35 (2), 537-559, 2020	66	2020
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	65	2019
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	63	2022

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