![]() ![]() ![]() This lightning prediction AI tool demonstrates a new way to use satellite data to help keep us informed and stay safe.Ĭheck out the experimental near real-time probability of lightning data. Not only is the satellite data useful for depicting current weather conditions, but it is also critical for predicting hazardous conditions in the future. The advanced data provided by GOES-16 (GOES East) and GOES-17 (GOES West) allow for new and innovative applications. “As John’s work shows, machine learning is opening the door to new satellite applications that directly address major societal challenges in ways that were not previously possible,” said Pavolonis. Mike Pavolonis, the NOAA scientist who leads the ProbSevere project, praised the new application. This tool was developed by John Cintineo at the University of Wisconsin/Cooperative Institute for Meteorological Satellite Studies (CIMSS), as part of ProbSevere, a statistical model that predicts the probability that a storm will produce severe weather in the near-term. Probability of lightning occurring in the next 60 minutes plotted for the coast of South Carolina on July 14, 2021, along with GLM flash-extent density data when lightning was detected. EST, the AI lightning prediction tool indicated a greater than 25% chance of lightning occurring in the next hour. on July 7, 2021, shows a lightning probability signal occurring 50 minutes before the first instance of lightning.Īt 1:56 p.m. Clear predictive signals often emerge before rain forms, ahead of weather radar signals. The AI tool can accurately predict lightning up to 60 minutes before the first observation of lightning flashes. Hail falls in a Berkeley backyard shortly after 9 a.m. It also indicates where lightning remains a threat in storms with intermittent lightning activity and helps determine when the threat from lightning is diminishing. Berkeley may be allowed some respite from the rain on Thursday, as only a tenth of an inch of rain is forecast to fall that day, but for the most part, continue to expect rain and gusts of wind of up to 50 mph throughout the week. This AI model skillfully and reliably predicts where lightning is most likely to occur even before precipitation forms. To accomplish this, a sophisticated machine-learning algorithm was trained, using GLM data, to recognize complex patterns in GOES-R Advanced Baseline Imager (ABI) imagery that often precede lightning activity detected by GLM. Scientists are using artificial intelligence (AI) to predict where GLM will observe lightning in the future. This version is ad-supported and is similar to apps such as Blitzortung, Lightning Cast, Spark and Live Lightning.Lightning captured in Temescal Valley, California. If you want the most efficient way of keeping up lightning strikes and thunderstorms, then My Lightning Tracker is the right lightning network for you. Full support for the latest iPhone and iPad models. Monitor the weather radar to track what could be coming. Share a strike with your friends so that they can see where the thunder & lightning is occurring too! Receive a lightning alarm when a storm is nearby so that you can monitor it live. Lightning Counters Strike Circles Detector Links Animation Speed Detectors Sound This website is for entertainment purposes only. The colors represent the age from now (white) to past (dark red) in 20 minutes time ranges. View more detailed information about where the thunderstorm is occurring on a map. Contact: V.3.52 This map shows lightning strikes in real time from. See a history of hotspots where lightning strikes occur most often! The 2021 lightning storms in the Bay Area and Southern California lasted less than 12 hours, compared to two days of strikes that triggered the August 2020. Detects and displays lightning strikes all around the world! You can also receive notifications whenever strikes are detected in your area. With a sleek modern design, you can watch thunderstorms as they occur. My Lightning Tracker is the best app for monitoring lightning strikes all around the world in close to real-time. ![]()
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