Darrel M. Kingfield

Evaluation of a Probabilistic Forecasting Methodology for Severe Convective Weather in the 2014 Hazardous Weather Testbed

AbstractA proposed new method for hazard identification and prediction was evaluated with forecasters in the National Oceanic and Atmospheric Administration Hazardous Weather Testbed during 2014. This method combines hazard-following objects with forecaster-issued trends of exceedance probabilities to produce probabilistic hazard information, as opposed to the static, deterministic polygon and attendant text product methodology presently employed by the National Weather Service to issue severe thunderstorm and tornado warnings. Three components of the test bed activities are discussed: usage of the new tools, verification of storm-based warnings and probabilistic forecasts from a control–test experiment, and subjective feedback on the proposed paradigm change. Forecasters were able to quickly adapt to the new tools and concepts and ultimately produced probabilistic hazard information in a timely manner. The probabilistic forecasts from two severe hail events tested in a control–test experiment were more s...

Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind Events

AbstractThe ongoing Phased Array Radar Innovative Sensing Experiment (PARISE) investigates the impacts of higher-temporal-resolution radar data on the warning decision process of NWS forecasters. Twelve NWS forecasters participated in the 2013 PARISE and were assigned to either a control (5-min updates) or an experimental (1-min updates) group. Participants worked two case studies in simulated real time. The first case presented a marginally severe hail event, and the second case presented a severe hail and wind event. While working each event, participants made decisions regarding the detection, identification, and reidentification of severe weather. These three levels compose what has now been termed the compound warning decision process. Decisions were verified with respect to the three levels of the compound warning decision process and the experimental group obtained a lower mean false alarm ratio than the control group throughout both cases. The experimental group also obtained a higher mean probabi...

Tornado Warning Decisions Using Phased-Array Radar Data

AbstractThe 2012 Phased Array Radar Innovative Sensing Experiment identified how rapidly scanned full-volumetric data captured known mesoscale processes and impacted tornado-warning lead time. Twelve forecasters from nine National Weather Service forecast offices used this rapid-scan phased-array radar (PAR) data to issue tornado warnings on two low-end tornadic and two nontornadic supercell cases. Verification of the tornadic cases revealed that forecasters’ use of PAR data provided a median tornado-warning lead time (TLT) of 20 min. This 20-min TLT exceeded by 6.5 and 9 min, respectively, participants’ forecast office and regions’ median spring season, low-end TLTs (2008–13). Furthermore, polygon-based probability of detection ranged from 0.75 to 1.0 and probability of false alarm for all four cases ranged from 0.0 to 0.5. Similar performance was observed regardless of prior warning experience. Use of a cognitive task analysis method called the recent case walk-through showed that this performance was d...

Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities

AbstractThe Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.

Forecaster Use and Evaluation of Real-Time 3DVAR Analyses during Severe Thunderstorm and Tornado Warning Operations in the Hazardous Weather Testbed

AbstractA weather-adaptive three-dimensional variational data assimilation (3DVAR) system was included in the NOAA Hazardous Weather Testbed as a first step toward introducing warn-on-forecast initiatives into operations. NWS forecasters were asked to incorporate the data in conjunction with single-radar and multisensor products in the Advanced Weather Interactive Processing System (AWIPS) as part of their warning-decision process for real-time events across the United States. During the 2011 and 2012 experiments, forecasters examined more than 36 events, including tornadic supercells, severe squall lines, and multicell storms. Products from the 3DVAR analyses were available to forecasters at 1-km horizontal resolution every 5 min, with a 4–6-min latency, incorporating data from the national Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the North American Mesoscale model. Forecasters found the updraft, vertical vorticity, and storm-top divergence products the most useful for storm interrog...

Examination of a Real-Time 3DVAR Analysis System in the Hazardous Weather Testbed

AbstractForecasters and research meteorologists tested a real-time three-dimensional variational data assimilation (3DVAR) system in the Hazardous Weather Testbed during the springs of 2010–12 to determine its capabilities to assist in the warning process for severe storms. This storm-scale system updates a dynamically consistent three-dimensional wind field every 5 min, with horizontal and average vertical grid spacings of 1 km and 400 m, respectively. The system analyzed the life cycles of 218 supercell thunderstorms on 27 event days during these experiments, producing multiple products such as vertical velocity, vertical vorticity, and updraft helicity. These data are compared to multiradar–multisensor data from the Warning Decision Support System–Integrated Information to document the performance characteristics of the system, such as how vertical vorticity values compare to azimuthal shear fields calculated directly from Doppler radial velocity. Data are stratified by range from the nearest radar, as...

The Relationship between Automated Low-Level Velocity Calculations from the WSR-88D and Maximum Tornado Intensity Determined from Damage Surveys

AbstractThe relationship between automated low-level velocity derived from WSR-88D severe storm algorithms and two groups of tornado intensity were evaluated using a 4-yr climatology of 1975 tornado events spawned from 1655 supercells and 320 quasi-linear convective systems (QLCSs). A comparison of peak velocity from groups of detections from the Mesocyclone Detection Algorithm and Tornado Detection Algorithm for each tornado track found overlapping distributions when discriminating between weak [rated as category 0 or 1 on the enhanced Fujita scale (EF0 and EF1)] and strong (EF2–5) events for both rotational and delta velocities. Dataset thresholding by estimated affected population lowered the range of observed velocities, particularly for weak tornadoes while retaining a greater frequency of events for strong tornadoes. Heidke skill scores for strength discrimination were dependent on algorithm, velocity parameter, population threshold, and convective mode, and varied from 0.23 and 0.66. Bootstrapping ...

Forecaster Performance and Workload: Does Radar Update Time Matter?

AbstractImpacts of radar update time on forecasters’ warning decision processes were analyzed in the 2015 Phased Array Radar Innovative Sensing Experiment. Thirty National Weather Service forecasters worked nine archived phased-array radar (PAR) cases in simulated real time. These cases presented nonsevere, severe hail and/or wind, and tornadic events. Forecasters worked each type of event with approximately 5-min (quarter speed), 2-min (half speed), and 1-min (full speed) PAR updates. Warning performance was analyzed with respect to lead time and verification. Combining all cases, forecasters’ median warning lead times when using full-, half-, and quarter-speed PAR updates were 17, 14.5, and 13.6 min, respectively. The use of faster PAR updates also resulted in higher probability of detection and lower false alarm ratio scores. Radar update speed did not impact warning duration or size. Analysis of forecaster performance on a case-by-case basis showed that the impact of PAR update speed varied depending ...

A Method for Extracting Postevent Storm Tracks

AbstractAlthough existing algorithms for storm tracking have been designed to operate in real time, they are also commonly used to do postevent data analysis and research. Real-time algorithms cannot use information on the subsequent positions of a storm because it is not available at the time that associations between frames are made, but postevent analysis is not similarly constrained. Therefore, it should be possible to obtain better tracks for postevent analysis than those that a real-time algorithm is capable of producing. In this paper, a statistical procedure for determining storm tracks from a set of identified storm cells over time is described. It is found that this procedure results in fewer, longer-lived tracks at the potential cost of a small increase in positional error.

Real-Time Applications of the Variational Version of the Local Analysis and Prediction System (vLAPS)

AbstractThe accurate and timely depiction of the state of the atmosphere on multiple scales is critical to enhance forecaster situational awareness and to initialize very short-range numerical forecasts in support of nowcasting activities. The Local Analysis and Prediction System (LAPS) of the Earth System Research Laboratory (ESRL)/Global Systems Division (GSD) is a numerical data assimilation and forecast system designed to serve such very finescale applications. LAPS is used operationally by more than 20 national and international agencies, including the NWS, where it has been operational in the Advanced Weather Interactive Processing System (AWIPS) since 1995.Using computationally efficient and scientifically advanced methods such as a multigrid technique that adds observational information on progressively finer scales in successive iterations, GSD recently introduced a new, variational version of LAPS (vLAPS). Surface and 3D analyses generated by vLAPS were tested in the Hazardous Weather Testbed (H...