A cyclone makes landfall across the California coast on November 19, 2024. TACLS will help give communities more time to prepare for impending severe weather.<\/div>\n
Credit: NASA<\/div>\n<\/figcaption><\/div>\n<\/div>\n<\/div>\n
This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans. <\/p>\n
The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth.<\/p>\n
The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it\u2019s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts. <\/p>\n
If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning.<\/p>\n
Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL\u2019s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA\u2019s Mars missions. <\/p>\n
The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning.<\/p>\n
Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch.<\/p>\n
For\u00a0additional\u00a0details, see the\u00a0entry for this project on NASA TechPort.<\/a>\u00a0<\/p>\nProject Lead:<\/strong>\u00a0<\/strong>Dr. Yehuda Bock, University of California, San Diego.\u00a0<\/p>\nSponsoring Organization(s):\u00a0<\/strong>NASA\u2019s Earth Science Technology\u00a0Office Advanced\u00a0Information Systems Technology Program; JPL; NOAA;\u00a0National Weather Service.\u00a0<\/p>\nPowered by WPeMatico<\/a><\/small><\/p>\nGet The Details…<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"The Transient Artifact and Continuous Learning System (TACLS)\u00a0leverages\u00a0data from\u00a0continuously operating\u00a0satellite\u00a0networks coupled with machine learning models\u00a0to help meteorologists at\u00a0the\u00a0National Weather Service forecast flash floods more efficiently. Continue reading → <\/span><\/a><\/p>\n