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Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning

CITE Title : Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning Personal Author(s) : McGovern, Amy;Lagerquist, Ryan;John Gagne, David;Jergensen, G. Eli;Elmore, Kimberly L.;Homeyer, Cameron R.;Smith, Travis; Published Date : 2019 Source : Bulletin of the American Meteorological Society, 100(11), 2175-2199 DOI : https://doi.org/10.1175/bams-d-18-0195.1 Journal Title : Bulletin of the American Meteorological Society McGovern, Amy et al. (2019). Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning. 100(11). https://doi.org/10.1175/bams-d-18-0195.1 McGovern, Amy et al. "Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning" 100, no. 11 (2019), https://doi.org/10.1175/bams-d-18-0195.1 McGovern, Amy et al. "Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning" vol. 100, no. 11, 2019, https://doi.org/10.1175/bams-d-18-0195.1 Export RIS Citation Information.

:

McGovern, Amy ;

Lagerquist, Ryan

2019 | Bulletin of the American Meteorological Society, 100(11), 2175-2199

:

This paper synthesizes multiple methods for machine learning (ML) model interpretation and visualization (MIV) focusing on meteorological applications...
Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning

CITE Title : Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning Personal Author(s) : McGovern, Amy;Karstens, Christopher D.;Smith, Travis;Lagerquist, Ryan; Published Date : 2019 Source : Weather and Forecasting, 34(5), 1437-1451 DOI : https://doi.org/10.1175/waf-d-18-0141.1 Journal Title : Weather and Forecasting McGovern, Amy et al. (2019). Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning. 34(5). https://doi.org/10.1175/waf-d-18-0141.1 McGovern, Amy et al. "Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning" 34, no. 5 (2019), https://doi.org/10.1175/waf-d-18-0141.1 McGovern, Amy et al. "Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning" vol. 34, no. 5, 2019, https://doi.org/10.1175/waf-d-18-0141.1 Export RIS Citation Information.

:

McGovern, Amy ;

Karstens, Christopher D.

2019 | Weather and Forecasting, 34(5), 1437-1451

:

Real-time prediction of storm longevity is a critical challenge for National Weather Service (NWS) forecasters. These predictions can guide forecaster...
Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction

CITE Title : Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction Personal Author(s) : Lagerquist, Ryan;McGovern, Amy;Homeyer, Cameron R.;Gagne II, David John;Smith, Travis; Published Date : 2020 Source : Monthly Weather Review, 148(7), 2837-2861 DOI : https://doi.org/10.1175/mwr-d-19-0372.1 Journal Title : Monthly Weather Review Lagerquist, Ryan et al. (2020). Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction. 148(7). https://doi.org/10.1175/mwr-d-19-0372.1 Lagerquist, Ryan et al. "Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction" 148, no. 7 (2020), https://doi.org/10.1175/mwr-d-19-0372.1 Lagerquist, Ryan et al. "Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction" vol. 148, no. 7, 2020, https://doi.org/10.1175/mwr-d-19-0372.1 Export RIS Citation Information.

:

Lagerquist, Ryan

;

McGovern, Amy

2020 | Monthly Weather Review, 148(7), 2837-2861

:

This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. ...
Classifying Convective Storms Using Machine Learning

CITE Title : Classifying Convective Storms Using Machine Learning Personal Author(s) : Jergensen, G. Eli;McGovern, Amy;Lagerquist, Ryan;Smith, Travis; Published Date : 2019 Source : Weather and Forecasting, 35(2), 537-559 DOI : https://doi.org/10.1175/waf-d-19-0170.1 Journal Title : Weather and Forecasting Jergensen, G. Eli et al. (2019). Classifying Convective Storms Using Machine Learning. 35(2). https://doi.org/10.1175/waf-d-19-0170.1 Jergensen, G. Eli et al. "Classifying Convective Storms Using Machine Learning" 35, no. 2 (2019), https://doi.org/10.1175/waf-d-19-0170.1 Jergensen, G. Eli et al. "Classifying Convective Storms Using Machine Learning" vol. 35, no. 2, 2019, https://doi.org/10.1175/waf-d-19-0170.1 Export RIS Citation Information.

:

Jergensen, G. Eli ;

McGovern, Amy

2019 | Weather and Forecasting, 35(2), 537-559

:

We demonstrate that machine learning (ML) can skillfully classify thunderstorms into three categories: supercell, part of a quasi-linear convective sy...
Evaluation of Near Real-Time Preliminary Tornado Damage Paths

CITE Title : Evaluation of Near Real-Time Preliminary Tornado Damage Paths Personal Author(s) : Karstens, Christopher;Shourd, Kacie;Speheger, Doug;Anderson, Aaron;Smith, Richard;Andra, David;Smith, Travis;Lakshmanan, Valliappa;Erickson, Somer; Published Date : 2016 Source : J. Operational Meteor., 4(10), 132-141 DOI : https://doi.org/10.15191/nwajom.2016.0410 Journal Title : Journal of Operational Meteorology Karstens, Christopher et al. (2016). Evaluation of Near Real-Time Preliminary Tornado Damage Paths. 4(10). https://doi.org/10.15191/nwajom.2016.0410 Karstens, Christopher et al. "Evaluation of Near Real-Time Preliminary Tornado Damage Paths" 4, no. 10 (2016), https://doi.org/10.15191/nwajom.2016.0410 Karstens, Christopher et al. "Evaluation of Near Real-Time Preliminary Tornado Damage Paths" vol. 4, no. 10, 2016, https://doi.org/10.15191/nwajom.2016.0410 Export RIS Citation Information.

:

Karstens, Christopher ;

Shourd, Kacie

2016 | J. Operational Meteor., 4(10), 132-141

:

The ability to preliminarily diagnose areas damaged by a tornado is examined using both a manual and an automated approach. The manual method consists...
Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather

CITE Title : Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather Personal Author(s) : McGovern, Amy;Elmore, Kimberly L.;Gagne, David John;Haupt, Sue Ellen;Karstens, Christopher D.;Lagerquist, Ryan;Smith, Travis;Williams, John K.; Published Date : 2017 Source : Bulletin of the American Meteorological Society, 98(10), 2073-2090 DOI : https://doi.org/10.1175/BAMS-D-16-0123.1 Journal Title : Bulletin of the American Meteorological Society McGovern, Amy et al. (2017). Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. 98(10). https://doi.org/10.1175/BAMS-D-16-0123.1 McGovern, Amy et al. "Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather" 98, no. 10 (2017), https://doi.org/10.1175/BAMS-D-16-0123.1 McGovern, Amy et al. "Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather" vol. 98, no. 10, 2017, https://doi.org/10.1175/BAMS-D-16-0123.1 Export RIS Citation Information.

:

McGovern, Amy ;

Elmore, Kimberly L.

2017 | Bulletin of the American Meteorological Society, 98(10), 2073-2090

:

High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, a...
Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind

CITE Title : Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind Personal Author(s) : Lagerquist, Ryan;McGovern, Amy;Smith, Travis; Published Date : 2017 Source : Weather and Forecasting, 32(6), 2175-2193 DOI : https://doi.org/10.1175/waf-d-17-0038.1 Journal Title : Weather and Forecasting Lagerquist, Ryan et al. (2017). Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind. 32(6). https://doi.org/10.1175/waf-d-17-0038.1 Lagerquist, Ryan and McGovern, Amy and Smith, Travis "Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind" 32, no. 6 (2017), https://doi.org/10.1175/waf-d-17-0038.1 Lagerquist, Ryan et al. "Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind" vol. 32, no. 6, 2017, https://doi.org/10.1175/waf-d-17-0038.1 Export RIS Citation Information.

:

Lagerquist, Ryan ;

McGovern, Amy

2017 | Weather and Forecasting, 32(6), 2175-2193

:

Thunderstorms in the United States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-...