Travis M. Smith
Travis M. Smith
Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO)
Adresă de e-mail confirmată pe ou.edu
Citat de
Citat de
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
The warning decision support system–integrated information
V Lakshmanan, T Smith, G Stumpf, K Hondl
Weather and Forecasting 22 (3), 596-612, 2007
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
Multi-Radar Multi-Sensor (MRMS) severe weather and aviation products: Initial operating capabilities
TM Smith, V Lakshmanan, GJ Stumpf, KL Ortega, K Hondl, K Cooper, ...
Bulletin of the American Meteorological Society 97 (9), 1617-1630, 2016
An automated technique to quality control radar reflectivity data
V Lakshmanan, A Fritz, T Smith, K Hondl, G Stumpf
Journal of applied meteorology and climatology 46 (3), 288-305, 2007
An objective high-resolution hail climatology of the contiguous United States
JL Cintineo, TM Smith, V Lakshmanan, HE Brooks, KL Ortega
Weather and Forecasting 27 (5), 1235-1248, 2012
Progress and challenges with Warn-on-Forecast
DJ Stensrud, LJ Wicker, M Xue, DT Dawson II, N Yussouf, DM Wheatley, ...
Atmospheric Research 123, 2-16, 2013
Rapid sampling of severe storms by the national weather radar testbed phased array radar
PL Heinselman, DL Priegnitz, KL Manross, TM Smith, RW Adams
Weather and Forecasting 23 (5), 808-824, 2008
A real-time, three-dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity, and derived products
V Lakshmanan, T Smith, K Hondl, GJ Stumpf, A Witt
Weather and Forecasting 21 (5), 802-823, 2006
The severe hazards analysis and verification experiment
KL Ortega, TM Smith, KL Manross, KA Scharfenberg, A Witt, AG Kolodziej, ...
Bulletin of the American Meteorological Society 90 (10), 1519-1530, 2009
The use of radial velocity derivatives to diagnose rotation and divergence
TM Smith, KL Elmore
Preprints, 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer …, 2004
An objective method of evaluating and devising storm-tracking algorithms
V Lakshmanan, T Smith
Weather and Forecasting 25 (2), 701-709, 2010
Tornado pathlength forecasts from 2010 to 2011 using ensemble updraft helicity
AJ Clark, J Gao, PT Marsh, T Smith, JS Kain, J Correia Jr, M Xue, F Kong
Weather and Forecasting 28 (2), 387-407, 2013
FACETs: A proposed next-generation paradigm for high-impact weather forecasting
LP Rothfusz, R Schneider, D Novak, K Klockow-McClain, AE Gerard, ...
Bulletin of the American Meteorological Society 99 (10), 2025-2043, 2018
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
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
Evaluation of a probabilistic forecasting methodology for severe convective weather in the 2014 hazardous weather testbed
CD Karstens, G Stumpf, C Ling, L Hua, D Kingfield, TM Smith, J Correia, ...
Weather and Forecasting 30 (6), 1551-1570, 2015
Data mining storm attributes from spatial grids
V Lakshmanan, T Smith
Journal of Atmospheric and Oceanic Technology 26 (11), 2353-2365, 2009
The emergence of weather-related test beds linking research and forecasting operations
FM Ralph, J Intrieri, D Andra, R Atlas, S Boukabara, D Bright, P Davidson, ...
Bulletin of the American Meteorological Society 94 (8), 1187-1211, 2013
A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings
J Gao, TM Smith, DJ Stensrud, C Fu, K Calhoun, KL Manross, J Brogden, ...
Weather and Forecasting 28 (3), 727-745, 2013
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