The lack of aviation weather information over the ocean has motivated the development of the Offshore Precipitation Capability (OPC), which uses a machine learning framework to fuse data from the five satellite channels on the current GOES, global lightning data from the ground-based Earth Network's Total Lightning Network, and several fields from NOAA's Rapid Refresh (RAP) 13 km numerical weather prediction model to create near-real-time radar-like precipitation intensity and echo top heights analyses over the ocean beyond the range of weather radar. OPC output is blended with existing NEXRAD radar mosaics to create a seamless weather radar-like analysis of aviation impacting weather for the benefit of offshore air traffic control.
Since the OPC is designed to operate over the data-sparse oceanic regions, satellite measurements are critical for development and validation of the capability. MIT Lincoln Laboratory currently uses data from the NASA Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar and the Microwave Imager (GMI) in capability development and validation, and is researching methods to add products from Suomi NPP, NASA A-Train, and NOAA 18/19 satellites. In preparation for the GOES-R era, we are also exploring the use of cloud property fields from the GOES-R Algorithm Working Group Cloud Height Algorithm (ACHA), and Geostationary Lightning Mapper (GLM) proxy data from NASA's Pseudo Geostationary Lightning Mapper (PGLM) dataset. This presentation will describe the use and benefits of these datasets to the OPC capability as they relate to providing improved situational awareness and forecasts for offshore air traffic control and management.