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

14 A . 3 Exploring Impacts of Rapid-scan Radar Data on NWS Warning Decision Making

2011

The development of rapid-scan capabilities with S-band phased-array radar (PAR) at the National Weather Radar Testbed in Norman, Oklahoma (Zrnić et al. 2007; Heinselman and Torres 2011) presents new opportunities for advancement of weather sensing. An important component of the technology development process is assessment of operational benefit(s) of new radar capabilities. This has been done for previous radar upgrades, only during the technology transfer process (e.g., JDOP (Burgess 1979) and JPOLE (Scharfenberg et al. 2005)).

Forecaster Performance and Workload: Does Radar Update Time Matter?

Weather and Forecasting, 2017

Impacts 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 va...

Bias in Severe Thunderstorm and Tornado Warnings Issued by the National Weather Service in the Doppler Radar Era: A Spatial-Temporal Evaluation

2006

A climatology of severe thunderstorm (damaging wind and/or hail) and tornadoes in the United States has established the location of the areas of highest frequency of occurrence. This climatology was attained through analysis of a basic data source, that of observed events, which carries many associated biases. Among these biases is the requirement that someone be on hand to witness the event no matter what time of the day or night, the assumption that the observer had sufficient visibility to see the event clearly, and whether there was something available on location to damage. In this study I use an alternate database consisting of the number of county severe thunderstorm warnings and tornado warnings issued by the National Weather Service, primarily for the 1995-2004 time window, between the Rocky Mountains and Appalachian Mountains. Because this alternative climatology is based upon the much improved technology available using Doppler radar, it is believed to have fewer and more...

Assessing the Impact of Non-Conventional Radar and Surface Observations on High-Resolution Analyses and Forecasts of a Severe Hailstorm

2021

A 2009 National Research Council study recommended that new mesoscale observing networks be integrated with existing networks to form a nationwide “network of networks”. The report also recommended that research testbeds be established, such as the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) DFW Testbed, to ascertain the potential benefit of proposed observing systems. In this work, we use various conventional and non-conventional observing systems from the DFW Testbed in a series of observing system experiments (OSEs). Of special interest are radar data from Terminal Doppler Weather Radars and CASA X-band radars, as well as novel surface observations. The Advanced Regional Prediction System (ARPS) model is used to perform OSEs that are designed to assess the impact of these observing systems. A three-dimensional variational analysis system and companion complex cloud analysis are used to produce analysis increments, which are assimilated in ARPS using Increme...

Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar

Weather and Forecasting, 2008

A key advantage of the National Weather Radar Testbed Phased Array Radar (PAR) is the capability to adaptively scan storms at higher temporal resolution than is possible with the Weather Surveillance Radar-1988 Doppler (WSR-88D): 1 min or less versus 4.1 min, respectively. High temporal resolution volumetric radar data are a necessity for rapid identification and confirmation of weather phenomena that can develop within minutes. The purpose of this paper is to demonstrate the PAR's ability to collect rapid-scan volumetric data that provide more detailed depictions of quickly evolving storm structures than the WSR-88D. Scientific advantages of higher temporal resolution PAR data are examined for three convective storms that occurred during the spring and summer of 2006, including a reintensifying supercell, a microburst, and a hailstorm. The analysis of the reintensifying supercell (58-s updates) illustrates the capability to diagnose the detailed evolution of developing and/or intensifying areas of 1) low-altitude divergence and rotation and 2) rotation through the depth of the storm. The fuller sampling of the microburst's storm life cycle (34-s updates) depicts precursors to the strong surface outflow that are essentially indiscernible in the WSR-88D data. Furthermore, the 34-s scans provide a more precise sampling of peak outflow. The more frequent sampling of the hailstorm (26-s updates) illustrates the opportunity to analyze storm structures indicative of rapid intensification, the development of hail aloft, and the onset of the downdraft near the surface.

Rainfall Estimates or Tornado Detection?: An Assessment Based on the Needs of Emergency Managers

2005

The following research brief uses data obtained from twenty six (n=26) interviews with emergency managers, National Weather Service (NWS) forecasters, and amateur radio operators (HAM) to determine whether rainfall estimation or tornado detection would more effectively address the needs of the emergency management community in Oklahoma. This study was conducted as part of a broader project on end-user integration, which intends to incorporate the needs and recommendations of end users into the design of radar technology currently under development by the Engineering Center for the Collaborative Adaptive Sensing of the Atmosphere (CASA). In the course of our analysis, we discovered that a majority of emergency managers require tornado detection due to the specific needs of Oklahoma communities, as well as their experiences with severe weather. We identified three reasons for this decision. First, tornados are less predictable than floods. Second, mitigation strategies, such as rain gauges and retention ponds, have significantly reduced the threat of flooding in most regions. Finally, failed tornado warnings vis-à-vis flood warnings seem to pose a greater threat to professional credibility and legitimacy. Overall, these findings indicate that emergency managers consider a wide range of factors when making decisions related to severe weather. While much is revealed about the decision-making process, the reasons for which emergency managers chose tornado detection over rainfall estimation were, in some cases, based on incomplete or inaccurate information. Most strikingly, for example, is that according to epidemiological statistics, flooding appears to be a greater threat to life than tornados. Moreover, current flood mitigation practices do not address the fact that a) floods produce long-term and diffuse effects (e.g., insurance costs), and b) mitigation techniques may decrease the level of individual preparedness, putting a population at risk of flash and/or major flooding. It is the recommendation of emergency managers that radar resources should primarily be allocated to tornado detection. It should, however, be remembered that flooding may continue to constitute a major threat to these communities.