In this work, machine learning and image processing methods are used to create near-real-time 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 GOES satellite channels, and several fields from NOAA's Rapid Refresh (RAP) 13 km numerical weather prediction model to create products similar to those provided by FAA WARP and CIWS weather systems. NEXRAD radar data available offshore and over ocean (e.g. Puerto Rico) is used to train the machine learning model. OPC output is blended with existing NEXRAD radar mosaics to create a seamless weather radar-like analysis of aviation impacting weather for offshore air traffic control. The seamless radar-like offshore analysis from OPC is extrapolated and combined with the RAP numerical model forecast to create 0-8 hour radar-forward forecasts for offshore air traffic management. Outputs of the analysis and forecast are validated using land radars and satellite precipitation measurements provided by the NASA Global Precipitation Measurement Mission. These capabilities will directly benefit the FAA by providing improved situational awareness and forecasts for offshore air traffic control and management.