This shortfall in aviation weather information over the ocean is motivating the Federal Aviation Administration and the Massachusetts Institute of Technology Lincoln Laboratory to develop the Offshore Precipitation Capability (OPC). The OPC uses a machine learning model to fuse data from the five channels on the current Geostationary Operational Environmental Satellite (GOES) with 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 the radar-like precipitation mosaics over the ocean. This presentation will describe the OPC capability and present examples of the oceanic radar-like precipitation mosaics for use in aviation.
Satellite data is also critical for OPC validation over the data-sparse ocean. Data from the NASA Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar and the Microwave Imager (GMI) is currently being used in OPC validation. In preparation for the GOES-R era, we are also exploring channels from Himawari-8, cloud properties from GOES-R Algorithm Working Group Cloud Height Algorithm (ACHA), and lightning proxy data from NASA's Pseudo-Geostationary Lightning Mapper (PGLM) in preparation for GLM. This presentation will describe the use of these data in the development and validation of the OPC capability and the potential use of new generation satellites in the capability.