![]() ![]() It uses machine learning to evaluate storm criteria and calculates the probability of whether a tornado is present with each detection. National Weather Service forecasters currently use a Tornado Detection Algorithm which was also developed at NSSL, but as with all technology, it needed an update. Researchers at NSSL are developing the New Tornado Detection Algorithm, or NTDA, to help NWS forecasters better detect tornadoes and hail. Phased array radar has strong potential to aid the NWS in the forecast and warning decision process by providing new radar data more quickly. Researchers are hoping to collect more high-resolution data on developing tornadoes both in QLCS's and supercells to look for clues in radar data that a tornado is forming. ![]() NSSL has a research phased array radar that also incorporates dual polarization technology, and can scan the entire sky for severe weather in less than a minute, five times faster than current weather radars. NSSL researchers discovered dual-polarization radars can detect debris from a tornado, helping forecasters pinpoint its location even at night or if it is wrapped in rain. The national network of weather radars now use dual-polarization technology, and NSSL continues to be a leader and major contributor to its ongoing scientific and engineering development. NSSL researchers are looking for ways to detect non-supercell tornadoes more effectively. QLCS tornadoes frequently occur during the late night and early morning hours when it can be more difficult to stay weather aware of severe hazards. However, nearly 20% of all tornadoes are associated with lines of strong thunderstorms called “quasi-linear convective systems” (QLCS). Most tornadoes come from rotating thunderstorms, called supercells. We use this model to study what changes in the environment cause a thunderstorm to produce a tornado, and how the tornado and storm behaves as it encounters different weather conditions. NSSL researchers have created a computer model that simulates a tornado-producing thunderstorm in 3-D. We continue to study the vast amounts of data collected from projects like this to learn what specific ingredients thunderstorms need to form a tornado, what causes it to die, and why some rotating thunderstorms produce tornadoes and others do not. The goal is to study and better observe features near the ground that are thought to play a key role in tornado formation. Most recently, the TORUS project set out to use a variety of tools from several organizations in order to study this phenomena, including the use of UAVs. As such, NSSL routinely participates in field work designed to better understand them. One of NSSL’s core missions is to understand severe weather and the hazards that accompany it, such as tornadoes. NSSL's tornado research targets ways to better understand how they form, and use that understanding to improve tornado forecasts and warnings to help save lives. typically has more tornadoes than anywhere else in the world, though they can occur almost anywhere. They are rare, deadly, and difficult to predict, and they can deal out millions or even billions of dollars in property damage per year.
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