The Oklahoma Weather Alert Remote Notification ( OK-WARN) program provides deaf and hard-of-hearing Oklahomans access to emergency severe weather information via alphanumeric pagers and/or E-mail addresses. New concepts of making dual-Doppler measurements using the WSR-88D with the airborne Doppler were first tested in 1989 and are now used routinely. The first direct measurements of a tornado recorded with an airborne Doppler radar were made by NSSL. NSSL has used an airborne Doppler radar (installed on NOAA's P-3 research aircraft) to study storms. The data was used to map the structure of a tornadic storm at several altitudes. The radars were located about 40 miles from each other and were able to record data on the same storm but from two different perspectives. NSSL made the first observations of a tornadic storm with two Doppler radars (called dual-Doppler). The Department of Commerce recognized NSSL's contribution to the NEXRAD program and to our Nation by awarding a Gold Medal to NSSL. These developments helped spur deployment of the WSR-88D NEXRAD radar network. This led to an NSSL scientist's discovery of the Tornadic Vortex Signature in radar velocity data in the 1970s. NSSL built the first real-time displays of Doppler velocity data. In this display, the circle is a mesocyclone, and the triangle is the TVS. NSSL's second generation Warning Decision Support System, WDSS-II,was an advanced algorithm development and visualization platform that accepted data from multiple sources and organizes it in ways that conveyed critical severe weather information to warning meteorologists. Today, circulation maps are available as part of the multi-radar, multi-sensor (MRMS) system developed at NSSL. Emergency responders and damage surveyors also used On-Demand to produce high-resolution street maps of potentially damaged areas so they can more effectively begin rescue and recovery efforts. NWS forecasters could quickly review warnings and check their accuracy with this system. NSSL's On-Demand web-based tool helped confirm when and where tornadoes occurred by mapping circulations on satellite images. The NTDA is currently being tested in NOAA’s Hazardous Weather Testbed on its performance and how NWS forecasters like the look and feel of the product. All of these factors are then combined by the NTDA to yield a probability of a tornado presence. The algorithm takes into account multiple storm aspects, including information available from dual-polarization radar, and reviews the statistics related to each evaluated element. The NTDA uses machine learning to evaluate storm criteria and calculates the probability of whether a tornado is present with each detection. The NTDA provides an operations update to the Tornado Detection Algorithm, also developed at NSSL, which is currently in use. Researchers at NSSL are developing the New Tornado Detection Algorithm, or NTDA, to help NWS forecasters better detect tornadoes and hail. This helps us understand atmospheric processes to help improve forecasts of significant weather events. NSSL uses a mobile Doppler radar to position close to tornadic storms to scan the entire lifecycle of a tornado. Phased array technology can scan an entire storm in less than one minute, allowing forecasters to see signs of developing tornadoes well ahead of current radar technology. What we do: NSSL engineers and scientists have adapted phased array technology, formerly used on Navy ships for surveillance, for use in weather forecasting. The mesocyclone is usually 2-6 miles in diameter, and is much larger than the tornado that may develop within it. When a Doppler radar detects a large rotating updraft that occurs inside a supercell, it is called a mesocyclone. A storm with a tornado observed by radar has certain distinguishing features and forecasters are trained to recognize them. Storm spotters can be emergency managers or even local people with a keen interest in severe weather who have taken formal storm spotter training in their community.Ĭomputer programs, called algorithms, analyze Doppler radar data and display it in ways that make it easier for forecasters to identify dangerous weather. Storm spotters have been trained to recognize tornado conditions and report what they see to the National Weather Service. Some of these are visual cues, like the rear-flank downdraft, and others are particular patterns in radar images, like the tornadic vortex signature (TVS). Forecasters and storm spotters have learned to recognize certain thunderstorm features and structure that make tornado formation more likely.
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