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Dive into the research topics where Kevin W. Thomas is active.

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Featured researches published by Kevin W. Thomas.


Weather and Forecasting | 2008

Some Practical Considerations Regarding Horizontal Resolution in the First Generation of Operational Convection-Allowing NWP

John S. Kain; Steven J. Weiss; David R. Bright; Michael E. Baldwin; Jason J. Levit; Gregory W. Carbin; Craig S. Schwartz; Morris L. Weisman; Kelvin K. Droegemeier; Daniel B. Weber; Kevin W. Thomas

Abstract During the 2005 NOAA Hazardous Weather Testbed Spring Experiment two different high-resolution configurations of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model were used to produce 30-h forecasts 5 days a week for a total of 7 weeks. These configurations used the same physical parameterizations and the same input dataset for the initial and boundary conditions, differing primarily in their spatial resolution. The first set of runs used 4-km horizontal grid spacing with 35 vertical levels while the second used 2-km grid spacing and 51 vertical levels. Output from these daily forecasts is analyzed to assess the numerical forecast sensitivity to spatial resolution in the upper end of the convection-allowing range of grid spacing. The focus is on the central United States and the time period 18–30 h after model initialization. The analysis is based on a combination of visual comparison, systematic subjective verification conducted during the Spring Experiment, and objectiv...


Monthly Weather Review | 2009

Next-Day Convection-Allowing WRF Model Guidance: A Second Look at 2-km versus 4-km Grid Spacing

Craig S. Schwartz; John S. Kain; Steven J. Weiss; Ming Xue; David R. Bright; Fanyou Kong; Kevin W. Thomas; Jason J. Levit; Michael C. Coniglio

Abstract During the 2007 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced convection-allowing forecasts from a single deterministic 2-km model and a 10-member 4-km-resolution ensemble. In this study, the 2-km deterministic output was compared with forecasts from the 4-km ensemble control member. Other than the difference in horizontal resolution, the two sets of forecasts featured identical Advanced Research Weather Research and Forecasting model (ARW-WRF) configurations, including vertical resolution, forecast domain, initial and lateral boundary conditions, and physical parameterizations. Therefore, forecast disparities were attributed solely to differences in horizontal grid spacing. This study is a follow-up to similar work that was based on results from the 2005 Spring Experiment. Unlike the 2005 experiment, however, model configurations were more rigorously controlled in the present study, providing...


Bulletin of the American Meteorological Society | 2012

An Overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment

Adam J. Clark; Steven J. Weiss; John S. Kain; Israel L. Jirak; Michael C. Coniglio; Christopher J. Melick; Christopher Siewert; Ryan A. Sobash; Patrick T. Marsh; Andrew R. Dean; Ming Xue; Fanyou Kong; Kevin W. Thomas; Yunheng Wang; Keith Brewster; Jidong Gao; Xuguang Wang; Jun Du; David R. Novak; Faye E. Barthold; Michael J. Bodner; Jason J. Levit; C. Bruce Entwistle; Tara Jensen; James Correia

The NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis and prediction of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new scientific concepts and tools from research to operations through the use of intensive real-time experimental forecasting and evaluation activities conducted during the spring and early summer convective storm period. The 2010 NOAA/HWT Spring Forecasting Experiment (SE2010), conducted 17 May through 18 June, had a broad focus, with emphases on heavy rainfall and aviation weather, through collaboration with the Hydrometeorological Prediction Center (HPC) and the Aviation Weather Center (AWC), respectively. In addition, using the computing resources of the National Institute for Computational Sciences at the University of Tennessee, the Center for A...


Weather and Forecasting | 2010

Toward Improved Convection-Allowing Ensembles: Model Physics Sensitivities and Optimizing Probabilistic Guidance with Small Ensemble Membership

Craig S. Schwartz; John S. Kain; Steven J. Weiss; Ming Xue; David R. Bright; Fanyou Kong; Kevin W. Thomas; Jason J. Levit; Michael C. Coniglio; Matthew S. Wandishin

Abstract During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced a daily 10-member 4-km horizontal resolution ensemble forecast covering approximately three-fourths of the continental United States. Each member used the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model core, which was initialized at 2100 UTC, ran for 33 h, and resolved convection explicitly. Different initial condition (IC), lateral boundary condition (LBC), and physics perturbations were introduced in 4 of the 10 ensemble members, while the remaining 6 members used identical ICs and LBCs, differing only in terms of microphysics (MP) and planetary boundary layer (PBL) parameterizations. This study focuses on precipitation forecasts from the ensemble. The ensemble forecasts reveal WRF-ARW sensitivity to MP and PBL schemes. For example, over the 7-week experiment, the Mellor–Yamada–Janjic PBL and Ferrier M...


Monthly Weather Review | 2011

Probabilistic Precipitation Forecast Skill as a Function of Ensemble Size and Spatial Scale in a Convection-Allowing Ensemble

Adam J. Clark; John S. Kain; David J. Stensrud; Ming Xue; Fanyou Kong; Michael C. Coniglio; Kevin W. Thomas; Yunheng Wang; Keith Brewster; Jidong Gao; Xuguang Wang; Steven J. Weiss; Jun Du

Probabilistic quantitative precipitation forecasts (PQPFs) from the storm-scale ensemble forecast system run by the Center for Analysis and Prediction of Storms during the spring of 2009 are evaluated using area under the relative operating characteristic curve (ROC area). ROC area, which measures discriminating ability, is examined for ensemble size n from 1 to 17 members and for spatial scales ranging from 4 to 200 km. Expectedly, incremental gains in skill decrease with increasing n. Significance tests comparing ROC areas for each n to those of the full 17-member ensemble revealed that more members are required to reach statistically indistinguishable PQPF skill relative to the full ensemble as forecast lead time increases and spatial scale decreases. These results appear to reflect the broadening of the forecast probability distribution function (PDF) of future atmospheric states associated with decreasing spatial scale and increasing forecast lead time. They also illustrate that efficient allocation of computing resources for convection-allowing ensembles requires careful consideration of spatial scale and forecast length desired.


Weather and Forecasting | 2010

Assessing Advances in the Assimilation of Radar Data and Other Mesoscale Observations within a Collaborative Forecasting-Research Environment

John S. Kain; Ming Xue; Michael C. Coniglio; Steven J. Weiss; Fanyou Kong; Tara Jensen; Barbara G. Brown; Jidong Gao; Keith Brewster; Kevin W. Thomas; Yunheng Wang; Craig S. Schwartz; Jason J. Levit

The impacts of assimilating radar data and other mesoscale observations in real-time, convection-allowing model forecasts were evaluated during the spring seasons of 2008 and 2009 as part of the Hazardous Weather Test Bed Spring Experiment activities. In tests of a prototype continental U.S.-scale forecast system, focusing primarily on regions with active deep convection at the initial time, assimilation of these observations had a positive impact. Daily interrogation of output by teams of modelers, forecasters, and verification experts provided additional insights into the value-added characteristics of the unique assimilation forecasts. This evaluation revealed that the positive effects of the assimilation were greatest during the first 3‐6 h of each forecast, appeared to be most pronounced with larger convective systems, and may have been related to a phase lag that sometimes developed when the convective-scale information was not assimilated. These preliminary results are currently being evaluated further using advanced objective verification techniques.


Weather and Forecasting | 2013

A Real-Time Weather-Adaptive 3DVAR Analysis System for Severe Weather Detections and Warnings

Jidong Gao; Travis M. Smith; David J. Stensrud; Chenghao Fu; Kristin M. Calhoun; Kevin L. Manross; Jeffrey Brogden; Valliappa Lakshmanan; Yunheng Wang; Kevin W. Thomas; Keith Brewster; Ming Xue

AbstractA real-time, weather-adaptive three-dimensional variational data assimilation (3DVAR) system has been adapted for the NOAA Warn-on-Forecast (WoF) project to incorporate all available radar observations within a moveable analysis domain. The key features of the system include 1) incorporating radar observations from multiple Weather Surveillance Radars-1988 Doppler (WSR-88Ds) with NCEP forecast products as a background state, 2) the ability to automatically detect and analyze severe local hazardous weather events at 1-km horizontal resolution every 5 min in real time based on the current weather situation, and 3) the identification of strong circulation patterns embedded in thunderstorms. Although still in the early development stage, the system performed very well within the NOAAs Hazardous Weather Testbed (HWT) Experimental Warning Program during preliminary testing in spring 2010 when many severe weather events were successfully detected and analyzed. This study represents a first step in the a...


Monthly Weather Review | 2014

Multiscale Characteristics and Evolution of Perturbations for Warm Season Convection-Allowing Precipitation Forecasts: Dependence on Background Flow and Method of Perturbation

Aaron Johnson; Xuguang Wang; Ming Xue; Fanyou Kong; Gang Zhao; Yunheng Wang; Kevin W. Thomas; Keith Brewster; Jidong Gao

AbstractMultiscale convection-allowing precipitation forecast perturbations are examined for two forecasts and systematically over 34 forecasts out to 30-h lead time using Haar Wavelet decomposition. Two small-scale initial condition (IC) perturbation methods are compared to the larger-scale IC and physics perturbations in an experimental convection-allowing ensemble. For a precipitation forecast driven primarily by a synoptic-scale baroclinic disturbance, small-scale IC perturbations resulted in little precipitation forecast perturbation energy on medium and large scales, compared to larger-scale IC and physics (LGPH) perturbations after the first few forecast hours. However, for a case where forecast convection at the initial time grew upscale into a mesoscale convective system (MCS), small-scale IC and LGPH perturbations resulted in similar forecast perturbation energy on all scales after about 12 h. Small-scale IC perturbations added to LGPH increased total forecast perturbation energy for this case. ...


Advances in Meteorology | 2013

Prediction of Convective Storms at Convection-Resolving 1 km Resolution over Continental United States with Radar Data Assimilation: An Example Case of 26 May 2008 and Precipitation Forecasts from Spring 2009

Ming Xue; Fanyou Kong; Kevin W. Thomas; Jidong Gao; Yunheng Wang; Keith Brewster; Kelvin K. Droegemeier

For the first time ever, convection-resolving forecasts at 1 km grid spacing were produced in realtime in spring 2009 by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The forecasts assimilated both radial velocity and reflectivity data from all operational WSR-88D radars within a domain covering most of the continental United States. In preparation for the realtime forecasts, 1 km forecast tests were carried out using a case from spring 2008 and the forecasts with and without assimilating radar data are compared with corresponding 4 km forecasts produced in realtime. Significant positive impact of radar data assimilation is found to last at least 24 hours. The 1 km grid produced a more accurate forecast of organized convection, especially in structure and intensity details. It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4 km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1 km grid and radar data assimilation for severe weather prediction for up to 24 hours.


Monthly Weather Review | 1984

Diagnosis of a Jet Streak in the Vicinity of a Severe Weather Outbreak in the Texas Panhandle

Howard B. Bluestein; Kevin W. Thomas

Abstract This is a case study of the synoptic and mesoscale aspects of a severe-weather outbreak in the Texas Panhandle. We offer circumstantial evidence that the rising branch of a thermally indirect circulation in the exit region of an unusually intense upper-level jet streak played a role in storm formation and sustenance. The jet streaks vertical circulation could not be accounted for by straight dynamics alone; curvature was important, especially along the right side of the exit region. The geostrophic momentum approximation leads to a reasonable qualitative explanation of the ageostrophic circulation, while quasi-geostrophic theory does not.

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Ming Xue

University of Oklahoma

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Fanyou Kong

University of Oklahoma

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Steven J. Weiss

National Oceanic and Atmospheric Administration

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John S. Kain

National Oceanic and Atmospheric Administration

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Michael C. Coniglio

National Oceanic and Atmospheric Administration

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Adam J. Clark

National Oceanic and Atmospheric Administration

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Jason J. Levit

National Oceanic and Atmospheric Administration

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