Search Results

You are looking at 1 - 5 of 5 items for

  • Author or Editor: David H. Kitzmiller x
  • Refine by Access: All Content x
Clear All Modify Search
David H. Kitzmiller and Wayne E. McGovern

Abstract

Objective experiments have been carried out to determine which moisture and stability indices as derived from the VISSR Atmospheric Sounder (VAS) contain the greatest amount of predictive information with respect to thunderstorm and severe local storm events. In these experiments, stability and moisture parameters derived from 1700 UTC VAS retrievals were compared and correlated to storm observations made during the subsequent 2000–0000 UTC period. The amount of predictive information in these indices was also compared to that possessed by indices derived from VAS first-guess profiles, concurrently available rawinsonde measurements, and numerical model forecasts. The correlation in the form of the computed information ratio (Ic) was used as a measure of predictive power in these experiments.

It was found that precipitable water and modified versions of the classic K index which included recent surface data had the highest values of Ic for general thunderstorm occurrence. The 50-kPa gradient wind speed (derived from VAS geopotential heights) and the temperature lapse rate in the 70–50 kPa layer were the best predictors for discriminating severe local storm cases from general thunderstorm cases. The 3-h and 6-h changes in VAS stability and moisture indices were poorly correlated to severe storm occurrence. Of all the variables examined, only the 3-h and 6-h change in surface blackbody temperature appeared to be even moderately correlated to severe storms.

A series of probability forecast and verification experiments was carried out to determine if the incorporation of VAS observations might improve automated thunderstorm probability forecasts. It was found that the retrievals possessed more information with respect to thunderstorm occurrence than do their own first guess profiles, which were derived from a forecast of the 0000 UTC limited-area Fine Mesh (LFM) model. However, the VAS-derived stability at 1700 UTC appears to possess little or no additional information beyond that available from 1200 UTC rawinsonde measurements, and indices derived from 1200 UTC LFM forecasts valid near the observation period (2000–0000 UTC) have more information than the 1700 UTC VAS-based indices. Possible reasons for these findings, and their implications for future satellite operations and applied research are discussed.

Full access
David H. Kitzmiller and Wayne E. Mcgovern

Abstract

Wind profiler, rawinsonde, and surface observations of the atmosphere over northeastern Colorado during the morning hours on 44 days were compared to the severity of subsequent thunderstorm activity. On half of thes days, large hail (diameter ≥2 cm) was observed over the region, while on the other half, only thunderstorms with no large hail or other severe local storm phenomena were reported. Statistical comparisons revealed that the wind speed near 8 km above ground level (AGL), the southerly wind component between 2.0 and 2.5 km AGL, and a thermal advection index computed from the degree of wind veering in the 1.5–2.5-km layer, were all significantly greater on the large-hail days than on the nonsevere weather days. Concurrently available rawinsonde observations did not detect some of these differences as clearly as did the profiler observations. A screening discriminant analysis of possible predictor combinations showed that the optimum discrimination between the cases with and without large hail was given by a linear combination of 8-km wind speed from profiler measurements and positive buoyant energy from rawinsonde temperature profiles.

Full access
David H. Kitzmiller, Wayne E. McGovern, and Robert F. Saffle

Abstract

The WSR-88D severe weather potential (SWP) algorithm is an automated procedure for the detection of severe local storms. The algorithm identifies individual thunderstorm cells within radar imagery and, for each cell, yields an index proportional to the probability that the cell will shortly produce damaging surface winds, large hail, or tornadoes. This index is a statistically derived function of the storm's maximum vertically integrated liquid (VIL) and horizontal areal extent. The correlation between these storm characteristics and severe weather occurrence was first documented in the 1970s. Several National Weather Service field offices in the central plains and Northeast regions of the United States have successfully used VIL as a discriminator between severe and nonsevere thunderstorms.

This paper describes the observational data and statistical methodology employed in the development of the SWP algorithm, and regional and seasonal variations in the SWP/severe weather relationship. The expected operational performance of the algorithm, in terms of probability of detection and false alarm ratio, is also documented.

Full access
Jay P. Breidenbach, David H. Kitzmiller, Wayne E. McGovern, and Robert E. Saffle

Abstract

The operational WSR-88D Severe Weather Potential (SWP) algorithm is an automated nowcasting procedure aimed at providing guidance in the detection of severe local storms. It yields a numerical index proportional to the probability that an individual storm cell is producing, or will shortly produce, large hail, damaging surface winds, or tornadoes.

Currently, the SWP algorithm consists of a statistically derived function of the cell's maximum vertically integrated liquid and horizontal areal extent. In an attempt to refine the algorithm, a wide variety of new statistical predictors of severe weather have been derived from volumetric reflectivity observations. Experimental second-generation SWP equations incorporating these new predictors were evaluated and their skill was compared to that of the operational SWP algorithm.

Those predictors that parameterize the magnitude of the reflectivity in the middle and upper portions of convective storms were found to have the most diagnostic information with respect to severe weather. Some of these predictors rely only on reflectivity above 15 000 ft (4572 m) and thus could be applied to storms beyond the current algorithm's range of 230 km. The skill of the second-generation equations within 230 km was found to be comparable to that of the current algorithm.

Full access
Steven V. Vasiloff, Dong-Jun Seo, Kenneth W. Howard, Jian Zhang, David H. Kitzmiller, Mary G. Mullusky, Witold F. Krajewski, Edward A. Brandes, Robert M. Rabin, Daniel S. Berkowitz, Harold E. Brooks, John A. McGinley, Robert J. Kuligowski, and Barbara G. Brown

Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nation's needs for the precipitation information effectively, the authors herein propose a community-wide integrated approach for precipitation information that fully capitalizes on recent advances in science and technology, and leverages the wide range of expertise and experience that exists in the research and operational communities. The concepts and recommendations from the workshop form the Q2 science plan and a suggested path to operations. Implementation of these concepts is expected to improve river forecasts and flood and flash flood watches and warnings, and to enhance various hydrologic and hydrometeorological services for a wide range of users and customers. In support of this initiative, the National Mosaic and Q2 (NMQ) system is being developed at the National Severe Storms Laboratory to serve as a community test bed for QPE and VSTQPF research and to facilitate the transition to operations of research applications. The NMQ system provides a real-time, around-the-clock data infusion and applications development and evaluation environment, and thus offers a community-wide platform for development and testing of advances in the focus areas.

Full access