The objective of this SBIR on “Machine Learning Downscaling Capability for Environmental Forecasts” is to develop a capability to generate skillful, near real-time environmental forecasts (of the atmosphere, ocean, sea ice, and/or ionosphere) at a much higher spatial horizontal resolution (less than 1 km) and vertical resolution (on the order of 10 m, particularly in the atmospheric boundary layer and/or upper ocean) than current machine learning weather prediction (MLWP) techniques using downscaling or similar methodologies for tactical/local scale applications.