scholarly journals Forecasting Tornadoes Using Convection-Permitting Ensembles

2016 ◽  
Vol 31 (1) ◽  
pp. 273-295 ◽  
Author(s):  
Burkely T. Gallo ◽  
Adam J. Clark ◽  
Scott R. Dembek
Keyword(s):  
Operating Characteristic ◽  
Wrf Model ◽  
Environmental Parameters ◽  
Environmental Information ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Critical Success ◽  
Weather Parameters ◽  
Critical Success Index ◽  
Success Index

Abstract Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.

scholarly journals Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

2010 ◽  
Vol 138 (11) ◽  
pp. 4098-4119 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Lance M. Leslie ◽  
Michael B. Richman ◽  
Charles A. Doswell
Keyword(s):  
Wrf Model ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Significant Events ◽  
Skill Scores ◽  
Time Of Year ◽  
Tornado Outbreaks ◽  
Thermodynamic Instability

Abstract Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes. This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

scholarly journals A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

2010 ◽  
Vol 27 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Patrick N. Gatlin ◽  
Steven J. Goodman
Keyword(s):  
Severe Weather ◽  
Lightning Flash ◽  
Severe Thunderstorm ◽  
Critical Success ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
Total Lightning ◽  
Jump Algorithm ◽  
Early Indication ◽  
Critical Success Index

Abstract An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstorm potential.

scholarly journals An Empirical Blowing Snow Forecast Technique for the Canadian Arctic and the Prairie Provinces

2005 ◽  
Vol 20 (1) ◽  
pp. 51-62 ◽  
Author(s):  
David G. Baggaley ◽  
John M. Hanesiak
Keyword(s):  
Wind Speed ◽  
Canadian Arctic ◽  
High Impact ◽  
Probability Of Detection ◽  
The Arctic ◽  
Blowing Snow ◽  
Critical Success ◽  
Critical Success Index ◽  
Success Index ◽  
Prairie Provinces

Abstract Blowing snow has a major impact on transportation and public safety. The goal of this study is to provide an operational technique for forecasting high-impact blowing snow on the Canadian arctic and the Prairie provinces using historical meteorological data. The focus is to provide some guidance as to the probability of reduced visibilities (e.g., less than 1 km) in blowing snow given a forecast wind speed and direction. The wind character associated with blowing snow was examined using a large database consisting of up to 40 yr of hourly observations at 15 locations in the Prairie provinces and at 17 locations in the arctic. Instances of blowing snow were divided into cases with and without concurrent falling snow. The latter group was subdivided by the time since the last snowfall in an attempt to account for aging processes of the snowpack. An empirical scheme was developed that could discriminate conditions that produce significantly reduced visibility in blowing snow using wind speed, air temperature, and time since last snowfall as predictors. This process was evaluated using actual hourly observations to compute the probability of detection, false alarm ratio, credibility, and critical success index. A critical success index as high as 66% was achieved. This technique can be used to give an objective first guess of the likelihood of high-impact blowing snow using common forecast parameters.

scholarly journals Evaluation of the Storm Prediction Center’s Day 1 Convective Outlooks

2012 ◽  
Vol 27 (6) ◽  
pp. 1580-1585 ◽  
Author(s):  
Nathan M. Hitchens ◽  
Harold E. Brooks
Keyword(s):  
Probability Of Detection ◽  
Grid Spacing ◽  
Forecast Performance ◽  
Critical Success ◽  
Initial Effort ◽  
Detection Frequency ◽  
Storm Prediction ◽  
Critical Success Index ◽  
Success Index

Abstract The Storm Prediction Center has issued daily convective outlooks since the mid-1950s. This paper represents an initial effort to examine the quality of these forecasts. Convective outlooks are plotted on a latitude–longitude grid with 80-km grid spacing and evaluated using storm reports to calculate verification measures including the probability of detection, frequency of hits, and critical success index. Results show distinct improvements in forecast performance over the duration of the study period, some of which can be attributed to apparent changes in forecasting philosophies.

Visualizing Multiple Measures of Forecast Quality

2009 ◽  
Vol 24 (2) ◽  
pp. 601-608 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Heavy Precipitation ◽  
Probability Of Detection ◽  
Ensemble Forecasts ◽  
Convective Initiation ◽  
Multiple Measures ◽  
Critical Success ◽  
Performance Statistics ◽  
Critical Success Index ◽  
Success Index ◽  
Forecast Quality

Abstract A method for visually representing multiple measures of dichotomous (yes–no) forecast quality (probability of detection, false alarm ratio, bias, and critical success index) in a single diagram is presented. Illustration of the method is provided using performance statistics from two previously published forecast verification studies (snowfall density and convective initiation) and a verification of several new forecast datasets: Storm Prediction Center forecasts of severe storms (nontornadic and tornadic), Hydrometeorological Prediction Center forecasts of heavy precipitation (greater than 12.5 mm in a 6-h period), National Weather Service Forecast Office terminal aviation forecasts (ceiling and visibility), and medium-range ensemble forecasts of 500-hPa height anomalies. The use of such verification metrics in concert with more detailed investigations to advance forecasting is briefly discussed.

scholarly journals Initial Development and Testing of a Convection-Allowing Model Scorecard

2019 ◽  
Vol 100 (12) ◽  
pp. ES367-ES384 ◽  
Author(s):  
Burkely T. Gallo ◽  
Christina P. Kalb ◽  
John Halley Gotway ◽  
Henry H. Fisher ◽  
Brett Roberts ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Model Performance ◽  
Skill Score ◽  
Initial Development ◽  
Convective Storms ◽  
Critical Success ◽  
Critical Success Index ◽  
Evidence Based Decision Making ◽  
Success Index

Abstract Evaluation of numerical weather prediction (NWP) is critical for both forecasters and researchers. Through such evaluation, forecasters can understand the strengths and weaknesses of NWP guidance, and researchers can work to improve NWP models. However, evaluating high-resolution convection-allowing models (CAMs) requires unique verification metrics tailored to high-resolution output, particularly when considering extreme events. Metrics used and fields evaluated often differ between verification studies, hindering the effort to broadly compare CAMs. The purpose of this article is to summarize the development and initial testing of a CAM-based scorecard, which is intended for broad use across research and operational communities and is similar to scorecards currently available within the enhanced Model Evaluation Tools package (METplus) for evaluating coarser models. Scorecards visualize many verification metrics and attributes simultaneously, providing a broad overview of model performance. A preliminary CAM scorecard was developed and tested during the 2018 Spring Forecasting Experiment using METplus, focused on metrics and attributes relevant to severe convective forecasting. The scorecard compared attributes specific to convection-allowing scales such as reflectivity and surrogate severe fields, using metrics like the critical success index (CSI) and fractions skill score (FSS). While this preliminary scorecard focuses on attributes relevant to severe convective storms, the scorecard framework allows for the inclusion of further metrics relevant to other applications. Development of a CAM scorecard allows for evidence-based decision-making regarding future operational CAM systems as the National Weather Service transitions to a Unified Forecast system as part of the Next-Generation Global Prediction System initiative.

Objective Limits on Forecasting Skill of Rare Events

2013 ◽  
Vol 28 (2) ◽  
pp. 525-534 ◽  
Author(s):  
Nathan M. Hitchens ◽  
Harold E. Brooks ◽  
Michael P. Kay
Keyword(s):  
Lower Bounds ◽  
Annual Cycle ◽  
Rare Event ◽  
Upper And Lower Bounds ◽  
Perfect Knowledge ◽  
Critical Success ◽  
Risk Areas ◽  
Annual Frequency ◽  
Critical Success Index ◽  
Success Index

Abstract A method for determining baselines of skill for the purpose of the verification of rare-event forecasts is described and examples are presented to illustrate the sensitivity to parameter choices. These “practically perfect” forecasts are designed to resemble a forecast that is consistent with that which a forecaster would make given perfect knowledge of the events beforehand. The Storm Prediction Center’s convective outlook slight risk areas are evaluated over the period from 1973 to 2011 using practically perfect forecasts to define the maximum values of the critical success index that a forecaster could reasonably achieve given the constraints of the forecast, as well as the minimum values of the critical success index that are considered the baseline for skillful forecasts. Based on these upper and lower bounds, the relative skill of convective outlook areas shows little to no skill until the mid-1990s, after which this value increases steadily. The annual frequency of skillful daily forecasts continues to increase from the beginning of the period of study, and the annual cycle shows maxima of the frequency of skillful daily forecasts occurring in May and June.

scholarly journals Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic-Scale Input

2009 ◽  
Vol 137 (4) ◽  
pp. 1250-1271 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Charles A. Doswell ◽  
Michael B. Richman ◽  
Lance M. Leslie
Keyword(s):  
Mesoscale Model ◽  
Wrf Model ◽  
Severe Weather ◽  
Synoptic Scale ◽  
Model Simulations ◽  
Resolution Model ◽  
Weather Parameters ◽  
Lifting Condensation Level ◽  
Tornado Outbreaks ◽  
Tornado Outbreak

Abstract Uncertainty exists concerning the links between synoptic-scale processes and tornado outbreaks. With continuously improving computer technology, a large number of high-resolution model simulations can be conducted to study these outbreaks to the storm scale, to determine the degree to which synoptic-scale processes appear to influence the occurrence of tornado outbreaks, and to determine how far in advance these processes are important. To this end, 50 tornado outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized with synoptic-scale input using the Weather Research and Forecasting (WRF) mesoscale model to determine if the model is able to distinguish the outbreak type 1, 2, and 3 days in advance of the event. The model simulations cannot resolve tornadoes explicitly; thus, the use of meteorological covariates (in the form of numerous severe-weather parameters) is necessary to determine whether or not the model is predicting a tornado outbreak. Results indicate that, using the covariates, the WRF model can discriminate outbreak type consistently at least up to 3 days in advance. The severe-weather parameters that are most helpful in discriminating between outbreak types include low-level and deep-layer shear variables and the lifting condensation level. An analysis of the spatial structures and temporal evolution, as well as the magnitudes, of the severe-weather parameters is critical to diagnose the outbreak type correctly. Thermodynamic instability parameters are not helpful in distinguishing the outbreak type, primarily because of a strong seasonal dependence and convective modification in the simulations.

scholarly journals PEMODELAN ATMOSFER DENGAN WRF PADA KEJADIAN BANJIR JAKARTA 17 JANUARI 2013

2013 ◽  
Vol 14 (1) ◽  
pp. 29
Author(s):  
Ardhi Adhary Arbain ◽  
Mahally Kudsy ◽  
M. Djazim Syaifullah
Keyword(s):  
False Alarm ◽  
Extreme Precipitation ◽  
Model Performance ◽  
Probability Of Detection ◽  
Ncep Reanalysis ◽  
Cold Surge ◽  
Qualitative And Quantitative ◽  
Critical Success ◽  
Critical Success Index ◽  
Success Index

Intisari  Simulasi WRF pada tanggal 16-17 Januari 2013 dilakukan untuk menguji performa model dalam mendeteksi fenomena seruak dingin dan hujan ekstrim yang merupakan pemicu utama bencana banjir Jakarta pada periode tersebut. Metode verifikasi kualitatif dan kuantitatif pada tiap grid secara dikotomi digunakan untuk membandingkan keluaran model dengan data observasi Global Satellite Mapping of Precipitation (GSMaP) dan NCEP Reanalysis. Performa model WRF dihitung berdasarkan nilai akurasi (ACC), Critical Success Index (CSI), Probability of Detection (POD) dan False Alarm Ratio (FAR) yang diperoleh dari hasil verifikasi numerik. Hasil pengujian menunjukkan bahwa WRF mampu melakukan deteksi waktu awal kejadian hujan ekstrim dengan tepat setelah 6-7 jam sejak inisiasi model dilakukan. Performa terbaik WRF teramati pada pukul 02-09 WIB (LT) dengan nilai CSI mencapai 0,32, POD 0,82 dan FAR 0,66. Hasil verifikasi secara kualitatif dan kuantitatif juga menunjukkan bahwa WRF dapat melakukan deteksi seruak dingin dan hujan ekstrim sebelum banjir terjadi, walaupun dengan ketepatan durasi waktu dan lokasi kejadian yang masih relatif rendah bila dibandingkan dengan data observasi.  Abstract  WRF simulation on January 16-17, 2013 has been conducted to evaluate the model performance in detecting cold surge and extreme precipitation phenomena which were the triggers of Jakarta flood event during the period. Qualitative and quantitative dichotomous grid-to-grid verification methods are utilized to compare the model output with Global Satellite Mapping of Precipitation (GSMaP) observation and NCEP Reanalysis dataset. WRF model performance is calculated based on the scores of accuracy (ACC), Critical Success Index (CSI), Probability of Detection (POD) and False Alarm Ration (FAR) which are generated from numerical verification. The results show that WRF could precisely detect the onset of extreme precipitation event in 6-7 hours after the model initiation.The best performance of the model is observed at 02-09 WIB (LT) with CSI score of 0.32, POD 0.82 and FAR 0.66. Despite the model inability to accurately predict the duration and location of cold surge and extreme precipitation, the qualitative and quantitative verification results also show that WRF could detect the phenomena just before the flood event occured.

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