| Year |
Citation |
Score |
| 2020 |
Handler SL, Reeves HD, McGovern A. Development of a Probabilistic Subfreezing Road Temperature Nowcast and Forecast Using Machine Learning Weather and Forecasting. 35: 1845-1863. DOI: 10.1175/Waf-D-19-0159.1 |
0.42 |
|
| 2020 |
Burke A, Snook N, Gagne II DJ, McCorkle S, McGovern A. Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting Weather and Forecasting. 35: 149-168. DOI: 10.1175/Waf-D-19-0105.1 |
0.398 |
|
| 2020 |
Lagerquist R, McGovern A, Homeyer CR, Gagne II DJ, Smith T. Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction Monthly Weather Review. 148: 2837-2861. DOI: 10.1175/Mwr-D-19-0372.1 |
0.458 |
|
| 2019 |
Jergensen GE, McGovern A, Lagerquist R, Smith T. Classifying Convective Storms Using Machine Learning Weather and Forecasting. 35: 537-559. DOI: 10.1175/Waf-D-19-0170.1 |
0.406 |
|
| 2019 |
Loken ED, Clark AJ, McGovern A, Flora M, Knopfmeier K. Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using Random Forests Weather and Forecasting. 34: 2017-2044. DOI: 10.1175/Waf-D-19-0109.1 |
0.416 |
|
| 2019 |
Lagerquist R, McGovern A, Ii DJG. Deep learning for spatially explicit prediction of synoptic-scale fronts Weather and Forecasting. 34: 1137-1160. DOI: 10.1175/Waf-D-18-0183.1 |
0.432 |
|
| 2019 |
McGovern A, Karstens CD, Smith T, Lagerquist R. Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning Weather and Forecasting. 34: 1437-1451. DOI: 10.1175/Waf-D-18-0141.1 |
0.409 |
|
| 2019 |
McGovern A, Lagerquist R, John Gagne D, Jergensen GE, Elmore KL, Homeyer CR, Smith T. Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning Bulletin of the American Meteorological Society. 100: 2175-2199. DOI: 10.1175/Bams-D-18-0195.1 |
0.403 |
|
| 2017 |
Lagerquist R, McGovern A, Smith T. Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind Weather and Forecasting. 32: 2175-2193. DOI: 10.1175/Waf-D-17-0038.1 |
0.392 |
|
| 2017 |
Gagne DJ, McGovern A, Haupt SE, Sobash RA, Williams JK, Xue M. Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles Weather and Forecasting. 32: 1819-1840. DOI: 10.1175/Waf-D-17-0010.1 |
0.459 |
|
| 2017 |
McGovern A, Elmore KL, Gagne DJ, Haupt SE, Karstens CD, Lagerquist R, Smith T, Williams JK. Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather Bulletin of the American Meteorological Society. 98: 2073-2090. DOI: 10.1175/Bams-D-16-0123.1 |
0.407 |
|
| 2017 |
Gagne DJ, McGovern A, Haupt SE, Williams JK. Evaluation of statistical learning configurations for gridded solar irradiance forecasting Solar Energy. 150: 383-393. DOI: 10.1016/J.Solener.2017.04.031 |
0.402 |
|
| 2015 |
Clark AJ, Mackenzie A, Mcgovern A, Lakshmanan V, Brown RA. An automated, multiparameter dryline identification algorithm Weather and Forecasting. 30: 1781-1794. DOI: 10.1175/Waf-D-15-0070.1 |
0.303 |
|
| 2015 |
McGovern A, Gagne DJ, Basara J, Hamill TM, Margolin D. Solar energy prediction : An international contest to initiate interdisciplinary research on compelling meteorological problems Bulletin of the American Meteorological Society. 96: 1388-1393. DOI: 10.1175/Bams-D-14-00006.1 |
0.38 |
|
| 2015 |
McGovern A, Balfour A, Beene M, Harrison D. Storm evader: Using an ipad to teach kids about meteorology and technology Bulletin of the American Meteorological Society. 96: 397-403. DOI: 10.1175/Bams-D-13-00202.1 |
0.32 |
|
| 2014 |
McGovern A, Gagne DJ, Williams JK, Brown RA, Basara JB. Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Machine Learning. 95: 27-50. PMID 26549932 DOI: 10.1007/S10994-013-5343-X |
0.483 |
|
| 2014 |
Gagne DJ, Mcgovern A, Xue M. Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts Weather and Forecasting. 29: 1024-1043. DOI: 10.1175/Waf-D-13-00108.1 |
0.444 |
|
| 2013 |
McGovern A, Troutman N, Brown RA, Williams JK, Abernethy J. Enhanced spatiotemporal relational probability trees and forests Data Mining and Knowledge Discovery. 26: 398-433. DOI: 10.1007/S10618-012-0261-2 |
0.421 |
|
| 2012 |
Gagne DJ, Mcgovern A, Basara JB, Brown RA. Tornadic supercell environments analyzed using surface and reanalysis data: A spatiotemporal relational data-mining approach Journal of Applied Meteorology and Climatology. 51: 2203-2217. DOI: 10.1175/Jamc-D-11-060.1 |
0.342 |
|
| 2012 |
Gagne DJ, McGovern A, Xue M. Machine learning enhancement of storm scale ensemble precipitation forecasts Proceedings - 2012 Conference On Intelligent Data Understanding, Cidu 2012. 39-46. DOI: 10.1109/CIDU.2012.6382199 |
0.338 |
|
| 2011 |
McGovern A, Wagstaff KL. Machine learning in space: Extending our reach Machine Learning. 84: 335-340. DOI: 10.1007/S10994-011-5249-4 |
0.396 |
|
| 2011 |
McGovern A, Rosendahl DH, Brown RA, Droegemeier KK. Identifying predictive multi-dimensional time series motifs: An application to severe weather prediction Data Mining and Knowledge Discovery. 22: 232-258. DOI: 10.1007/S10618-010-0193-7 |
0.362 |
|
| 2011 |
McGovern A, Gagne DJ, Troutman N, Brown RA, Basara J, Williams JK. Using spatiotemporal relational random forests to improve our understanding of severe weather processes Statistical Analysis and Data Mining. 4: 407-429. DOI: 10.1002/Sam.10128 |
0.382 |
|
| 2009 |
Gagne DJ, McGovern A, Brotzge J. Classification of convective areas using decision trees Journal of Atmospheric and Oceanic Technology. 26: 1341-1353. DOI: 10.1175/2008Jtecha1205.1 |
0.36 |
|
| 2008 |
McGovern A, Jensen D. Optimistic pruning for multiple instance learning Pattern Recognition Letters. 29: 1252-1260. DOI: 10.1016/J.Patrec.2008.01.024 |
0.354 |
|
| 2003 |
McGovern A, Jensen D. Identifying Predictive Structures in Relational Data Using Multiple Instance Learning Proceedings, Twentieth International Conference On Machine Learning. 2: 528-535. |
0.317 |
|
| 2002 |
McGovern A, Moss E, Barto AG. Building a basic block instruction scheduler with reinforcement learning and rollouts Machine Learning. 49: 141-160. DOI: 10.1023/A:1017976211990 |
0.364 |
|
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