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Abstract
This study is an application of the Statistical Severe Convective Risk Assessment Model (SSCRAM), which objectively assesses conditional severe thunderstorm probabilities based on archived hourly mesoscale data across the United States collected from 2006 to 2014. In the present study, SSCRAM is used to assess the utility of severe thunderstorm parameters commonly employed by forecasters in anticipating thunderstorms that produce significant tornadoes (i.e., causing F2/EF2 or greater damage) from June through October. The utility during June–October is compared to that during other months. Previous studies have identified some aspects of the summertime challenge in severe storm forecasting, and this study provides an in-depth quantification of the within-year variability of severe storms predictability. Conditional probabilities of significant tornadoes downstream of lightning occurrence using common parameter values, such as the effective-layer significant tornado parameter, convective available potential energy, and vertical shear, are found to substantially decrease during the months of June–October compared to other months. Furthermore, conditional probabilities of significant tornadoes during June–October associated with these parameters are nearly invariable regardless of value, highlighting the challenge of using objective environmental data to attempt to forecast significant tornadoes from June through October.
Abstract
This study is an application of the Statistical Severe Convective Risk Assessment Model (SSCRAM), which objectively assesses conditional severe thunderstorm probabilities based on archived hourly mesoscale data across the United States collected from 2006 to 2014. In the present study, SSCRAM is used to assess the utility of severe thunderstorm parameters commonly employed by forecasters in anticipating thunderstorms that produce significant tornadoes (i.e., causing F2/EF2 or greater damage) from June through October. The utility during June–October is compared to that during other months. Previous studies have identified some aspects of the summertime challenge in severe storm forecasting, and this study provides an in-depth quantification of the within-year variability of severe storms predictability. Conditional probabilities of significant tornadoes downstream of lightning occurrence using common parameter values, such as the effective-layer significant tornado parameter, convective available potential energy, and vertical shear, are found to substantially decrease during the months of June–October compared to other months. Furthermore, conditional probabilities of significant tornadoes during June–October associated with these parameters are nearly invariable regardless of value, highlighting the challenge of using objective environmental data to attempt to forecast significant tornadoes from June through October.
Abstract
Nonconvective high winds are a deceptively hazardous meteorological phenomenon. Though the National Weather Service (NWS) possesses an array of products designed to alert the public to nonconvective wind potential, documentation justifying the choice of issuance thresholds is scarce. Measured wind speeds from the Global Historical Climatology Network (GHCN)-Daily dataset associated with human-reported nonconvective wind events from Storm Data are examined in order to assess the suitability of the current gust criteria for the NWS wind advisory and high wind warning. Nearly 92% (45%) of the nonconvective wind events considered from Storm Data were accompanied by peak gusts beneath the high wind warning (wind advisory) threshold of 58 mi h−1 (25.9 m s−1) [46 mi h−1 (20.6 m s−1)], and greater than 74% (28%) of all fatal and injury-causing events were associated with peak gusts below these same product gust criteria. NWS wind products were disproportionately issued in areas of complex terrain where wind climatologies include a greater frequency of high wind warning threshold-level gusts, irrespective of observed impacts. For many areas of the eastern United States, a 58 mi h−1 (25.9 m s−1) gust of convective, tropical, or nonconvective origin falls within the top 0.5% of all observed daily maximum wind gusts, nearly eliminating the possibility of a nonconvective gust meeting the issuance criterion.
Abstract
Nonconvective high winds are a deceptively hazardous meteorological phenomenon. Though the National Weather Service (NWS) possesses an array of products designed to alert the public to nonconvective wind potential, documentation justifying the choice of issuance thresholds is scarce. Measured wind speeds from the Global Historical Climatology Network (GHCN)-Daily dataset associated with human-reported nonconvective wind events from Storm Data are examined in order to assess the suitability of the current gust criteria for the NWS wind advisory and high wind warning. Nearly 92% (45%) of the nonconvective wind events considered from Storm Data were accompanied by peak gusts beneath the high wind warning (wind advisory) threshold of 58 mi h−1 (25.9 m s−1) [46 mi h−1 (20.6 m s−1)], and greater than 74% (28%) of all fatal and injury-causing events were associated with peak gusts below these same product gust criteria. NWS wind products were disproportionately issued in areas of complex terrain where wind climatologies include a greater frequency of high wind warning threshold-level gusts, irrespective of observed impacts. For many areas of the eastern United States, a 58 mi h−1 (25.9 m s−1) gust of convective, tropical, or nonconvective origin falls within the top 0.5% of all observed daily maximum wind gusts, nearly eliminating the possibility of a nonconvective gust meeting the issuance criterion.
Abstract
An objective technique to determine forecast snowfall ranges consistent with the risk tolerance of users is demonstrated. The forecast snowfall ranges are based on percentiles from probability distribution functions that are assumed to be perfectly calibrated. A key feature of the technique is that the snowfall range varies dynamically, with the resultant ranges varying based on the spread of ensemble forecasts at a given forecast projection, for a particular case, for a particular location. Furthermore, this technique allows users to choose their risk tolerance, quantified in terms of the expected false alarm ratio for forecasts of snowfall range. The technique is applied to the 4–7 March 2013 snowstorm at two different locations (Chicago, Illinois, and Washington, D.C.) to illustrate its use in different locations with different forecast uncertainties. The snowfall range derived from the Weather Prediction Center Probabilistic Winter Precipitation Forecast suite is found to be statistically reliable for the day 1 forecast during the 2013/14 season, providing confidence in the practical applicability of the technique.
Abstract
An objective technique to determine forecast snowfall ranges consistent with the risk tolerance of users is demonstrated. The forecast snowfall ranges are based on percentiles from probability distribution functions that are assumed to be perfectly calibrated. A key feature of the technique is that the snowfall range varies dynamically, with the resultant ranges varying based on the spread of ensemble forecasts at a given forecast projection, for a particular case, for a particular location. Furthermore, this technique allows users to choose their risk tolerance, quantified in terms of the expected false alarm ratio for forecasts of snowfall range. The technique is applied to the 4–7 March 2013 snowstorm at two different locations (Chicago, Illinois, and Washington, D.C.) to illustrate its use in different locations with different forecast uncertainties. The snowfall range derived from the Weather Prediction Center Probabilistic Winter Precipitation Forecast suite is found to be statistically reliable for the day 1 forecast during the 2013/14 season, providing confidence in the practical applicability of the technique.
Abstract
The proliferation of ensemble forecast system output in recent years motivates this investigation into how operational forecasters utilize convection-permitting ensemble forecast system guidance in the forecast preparation process. A 16-member, convection-permitting ensemble forecast of the high-impact heavy precipitation resulting from Tropical Storm Fay (2008) is conducted and evaluated. The ensemble provides a skillful, albeit underdispersive and bimodal, forecast at all precipitation thresholds considered. A forecasting exercise is conducted to evaluate how forecasters utilize the ensemble forecast system guidance. Forecasters made two storm-total accumulated precipitation forecasts: one before and one after evaluating the ensemble guidance. Concurrently, forecasters were presented with questionnaires designed to gauge their thought processes in preparing each of their forecasts. Exercise participants felt that the high-resolution ensemble guidance added value and confidence to their forecasts, although it did not meaningfully reduce forecast uncertainty. Incorporation of the ensemble guidance into the forecast preparation process resulted in a modest mean improvement in forecast skill, with each forecast found to be skillful at all accumulated precipitation thresholds. Forecasters primarily utilized the ensemble guidance to identify a “most likely” forecast outcome from disparate deterministic guidance solutions and to help quantify the uncertainty associated with the forecast. Forecasters preferred ensemble guidance that enabled them to quickly understand the range of solutions provided by the ensemble, particularly over the entirety of the domain. Forecasters were generally aware of the diversity of solutions provided by the ensemble guidance; however, only a select few actively interrogated this information when revising their forecasts and each did so in different ways.
Abstract
The proliferation of ensemble forecast system output in recent years motivates this investigation into how operational forecasters utilize convection-permitting ensemble forecast system guidance in the forecast preparation process. A 16-member, convection-permitting ensemble forecast of the high-impact heavy precipitation resulting from Tropical Storm Fay (2008) is conducted and evaluated. The ensemble provides a skillful, albeit underdispersive and bimodal, forecast at all precipitation thresholds considered. A forecasting exercise is conducted to evaluate how forecasters utilize the ensemble forecast system guidance. Forecasters made two storm-total accumulated precipitation forecasts: one before and one after evaluating the ensemble guidance. Concurrently, forecasters were presented with questionnaires designed to gauge their thought processes in preparing each of their forecasts. Exercise participants felt that the high-resolution ensemble guidance added value and confidence to their forecasts, although it did not meaningfully reduce forecast uncertainty. Incorporation of the ensemble guidance into the forecast preparation process resulted in a modest mean improvement in forecast skill, with each forecast found to be skillful at all accumulated precipitation thresholds. Forecasters primarily utilized the ensemble guidance to identify a “most likely” forecast outcome from disparate deterministic guidance solutions and to help quantify the uncertainty associated with the forecast. Forecasters preferred ensemble guidance that enabled them to quickly understand the range of solutions provided by the ensemble, particularly over the entirety of the domain. Forecasters were generally aware of the diversity of solutions provided by the ensemble guidance; however, only a select few actively interrogated this information when revising their forecasts and each did so in different ways.
Abstract
Several storms produced extensive hail damage over Iowa on 9 August 2009. The hail associated with these supercells was observed with radar data, reported by surface observers, and the resulting hail swaths were identified within satellite data. This study includes an initial assessment of cross validation of several radar-derived products and surface observations with satellite data for this storm event. Satellite-derived vegetation index data appear to be a useful product for cross validation of surface-based reports and radar-derived products associated with severe hail damage events. Satellite imagery acquired after the storm event indicated that decreased vegetation index values corresponded to locations of surface reported damage. The areal extent of decreased vegetation index values also corresponded to the spatial extent of the storms as characterized by analysis of radar data. While additional analyses are required and encouraged, these initial results suggest that satellite data of vegetated land surfaces are useful for cross validation of surface and radar-based observations of hail swaths and associated severe weather.
Abstract
Several storms produced extensive hail damage over Iowa on 9 August 2009. The hail associated with these supercells was observed with radar data, reported by surface observers, and the resulting hail swaths were identified within satellite data. This study includes an initial assessment of cross validation of several radar-derived products and surface observations with satellite data for this storm event. Satellite-derived vegetation index data appear to be a useful product for cross validation of surface-based reports and radar-derived products associated with severe hail damage events. Satellite imagery acquired after the storm event indicated that decreased vegetation index values corresponded to locations of surface reported damage. The areal extent of decreased vegetation index values also corresponded to the spatial extent of the storms as characterized by analysis of radar data. While additional analyses are required and encouraged, these initial results suggest that satellite data of vegetated land surfaces are useful for cross validation of surface and radar-based observations of hail swaths and associated severe weather.
Abstract
An unusual severe weather event with supercell thunderstorms developed across portions of northern Arizona in the midst of the warm-season North American monsoon—a regime characteristically dominated by a subtropical upper-level high over the southwestern United States. The approach of a midlatitude, cold-core, upper-level low brought an environment of enhanced shear and increased instability supportive of supercells. This atypical system is described and how a correct interpretation of the winds and hodograph would allow a forecaster to maintain situational awareness is discussed.
Abstract
An unusual severe weather event with supercell thunderstorms developed across portions of northern Arizona in the midst of the warm-season North American monsoon—a regime characteristically dominated by a subtropical upper-level high over the southwestern United States. The approach of a midlatitude, cold-core, upper-level low brought an environment of enhanced shear and increased instability supportive of supercells. This atypical system is described and how a correct interpretation of the winds and hodograph would allow a forecaster to maintain situational awareness is discussed.
Abstract
Daily values of forecast scores are evaluated for students in a weather analysis and forecasting class (ATMS 452) offered by the Department of Atmospheric Sciences of the University of Washington during the spring terms of 1997–2007. The objective of this study is to determine the rate at which senior-level undergraduate students develop proficiency at short-term (next day) weather forecasting. Separate analyses are carried out for different categories of forecast parameters. Time series of the average skill achieved over the course of the quarter are presented for the median and the best–worst two student forecasters each year. An overall improvement in student forecast skill occurs over roughly the first 6 weeks of the quarter, followed by minimal systematic changes. Negligible trends in average forecast skill have occurred over the past 10 yr. The correlation coefficient between the students’ overall forecast performance and test scores in ATMS 452 is about 0.4. The results are relevant to the design of effective instructional programs for weather forecasting.
Abstract
Daily values of forecast scores are evaluated for students in a weather analysis and forecasting class (ATMS 452) offered by the Department of Atmospheric Sciences of the University of Washington during the spring terms of 1997–2007. The objective of this study is to determine the rate at which senior-level undergraduate students develop proficiency at short-term (next day) weather forecasting. Separate analyses are carried out for different categories of forecast parameters. Time series of the average skill achieved over the course of the quarter are presented for the median and the best–worst two student forecasters each year. An overall improvement in student forecast skill occurs over roughly the first 6 weeks of the quarter, followed by minimal systematic changes. Negligible trends in average forecast skill have occurred over the past 10 yr. The correlation coefficient between the students’ overall forecast performance and test scores in ATMS 452 is about 0.4. The results are relevant to the design of effective instructional programs for weather forecasting.
Abstract
The use of the potential vorticity (PV) framework by operational forecasters is advocated through case examples that demonstrate its utility for interpreting and evaluating numerical weather prediction (NWP) model output for weather systems characterized by strong latent heat release (LHR). The interpretation of the dynamical influence of LHR is straightforward in the PV framework; LHR can lead to the generation of lower-tropospheric cyclonic PV anomalies. These anomalies can be related to meteorological phenomena including extratropical cyclones and low-level jets (LLJs), which can impact lower-tropospheric moisture transport.
The nonconservation of PV in the presence of LHR results in a modification of the PV distribution that can be identified in NWP model output and evaluated through a comparison with observations and high-frequency gridded analyses. This methodology, along with the application of PV-based interpretation, can help forecasters identify aspects of NWP model solutions that are driven by LHR; such features are often characterized by increased uncertainty due to difficulties in model representation of precipitation amount and latent heating distributions, particularly for convective systems.
Misrepresentation of the intensity and/or distribution of LHR in NWP model forecasts can generate errors that propagate through the model solution with time, potentially degrading the representation of cyclones and LLJs in the model forecast. The PV framework provides human forecasters with a means to evaluate NWP model forecasts in a way that facilitates recognition of when and how value may be added by modifying NWP guidance. This utility is demonstrated in case examples of coastal extratropical cyclogenesis and LLJ enhancement. Information is provided regarding tools developed for applying PV-based techniques in an operational setting.
Abstract
The use of the potential vorticity (PV) framework by operational forecasters is advocated through case examples that demonstrate its utility for interpreting and evaluating numerical weather prediction (NWP) model output for weather systems characterized by strong latent heat release (LHR). The interpretation of the dynamical influence of LHR is straightforward in the PV framework; LHR can lead to the generation of lower-tropospheric cyclonic PV anomalies. These anomalies can be related to meteorological phenomena including extratropical cyclones and low-level jets (LLJs), which can impact lower-tropospheric moisture transport.
The nonconservation of PV in the presence of LHR results in a modification of the PV distribution that can be identified in NWP model output and evaluated through a comparison with observations and high-frequency gridded analyses. This methodology, along with the application of PV-based interpretation, can help forecasters identify aspects of NWP model solutions that are driven by LHR; such features are often characterized by increased uncertainty due to difficulties in model representation of precipitation amount and latent heating distributions, particularly for convective systems.
Misrepresentation of the intensity and/or distribution of LHR in NWP model forecasts can generate errors that propagate through the model solution with time, potentially degrading the representation of cyclones and LLJs in the model forecast. The PV framework provides human forecasters with a means to evaluate NWP model forecasts in a way that facilitates recognition of when and how value may be added by modifying NWP guidance. This utility is demonstrated in case examples of coastal extratropical cyclogenesis and LLJ enhancement. Information is provided regarding tools developed for applying PV-based techniques in an operational setting.
