Creating Synthetic Radar Imagery Using Convolutional Neural Networks
Name
2019-02-15-Bulletin-Creating Synthetic Radar.pdf
Size
1.22 MB
Format
Adobe PDF
Checksum (MD5)
b70349223ef1225670cc8859b8c67515
Date Issued
February 15, 2019
Publisher
MIT Lincoln Laboratory
Series/Report no.
The Bulletin;
Abstract
Weather radar can track the location and intensity of storms and is useful for managing transportation around hazardous weather. Air traffic controllers, for example, rely on weather radar to track storms that could impact aircraft and flight schedules. Although land-based radar is sufficient to cover most continental air space, many offshore and oceanic controllers do not have sufficient access to the weather information that they need for proper air traffic management. Researchers at MIT Lincoln Laboratory developed the Offshore Precipitation Capability (OPC), a system that creates a radar-like depiction of precipitation — known as synthetic radar — by combining data from multiple nonradar sources, and implementing convolutional neural networks into the OPC system as a means of combining the nonradar data sources to create synthetic radar.
Subjects
Lincoln Laboratory
Supercomputing
LLSC
Artificial Intelligence
Terms of Use
Attribution-NoDerivs 3.0 United States
Persistent DSpace Link
