In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and several fields from the Rapid Refresh (RAP) 13 km numerical weather prediction model to create precipitation and echo top fields similar to those provided by existing Federal Aviation Administration (FAA) weather systems. Preprocessing and feature extraction methods are described to construct inputs for model training. A variety of machine learning algorithms are investigated to identify which provides the most accuracy. Output from the machine learning model is blended with existing radar mosaics to create weather radar-like analyses that extend into offshore regions. The resulting fields are validated using land radars and satellite precipitation measurements provided by the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) core observatory satellite. This capability is initially being developed for the Miami Oceanic airspace with the goal of providing improved situational awareness for offshore air traffic control.
Offshore Precipitation Capability
2016
39 pages
Report
No indication
English
Guidance Systems , Space Technology , Oceanographic Vessels, Instruments, & Platforms , Aeronautics & Aerodynamics , Avionics , Common Carrier & Satellite , Offshore activities , Precipitation characteristics , Geostationary satellite , Global lightning , OBSERVATORY SATELLITES , Rapid Refresh (RAP) , Algorithms , Offshore regions , Offshore Precipitation Capability (OPC) , Geostationary Operational Environmental Satellite (GOES) , Federal Aviation Administration (FAA)
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