Nowcasting techniques continue to be of great interest for predicting rainfall in the 0-6 hour timeframe. Radar-based techniques offer significant utility; however, the limitations of radar coverage and the dependence on pre-existing rainfall make satellite-based techniques a necessary component of a nowcasting suite. In this vein two of the GOES-R Environmental Data Records (EDR's) address this need: probability of precipitation and rainfall outlook derived from GOES data.

This is a collaborative framework being pursued by personnel at NOAA/NESDIS, the NOAA/NWS Meteorological Development Laboratory (MDL), the NOAA/OAR National Severe Storms Laboratory (NSSL), and the NOAA Cooperative Remote Sensing Science and Technology Center (CREST) at the City College of New York (CCNY) to develop and test a prototype satellite-based nowcasting capability for the New York City metropolitan area and other US selected areas, and to transition that capability into the GOES-R era. In this paper we will describe how this project will be assisting in the algorithm selection process by producing output from existing nowcasting algorithms for use in the Hydrology Algorithm Team inter-comparison, and present some preliminary results of the different nowcasting algorithms using several study cases over US and Europe. Possible improvements of the existing algorithms will be included.