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This study developed a snowfall detection algorithm over ocean, sea ice, and coast for the advanced technology microwave sounder (ATMS) onboard both NPP and NOAA-20 satellites. The detection algorithm was trained from collocated observations between ATMS-NPP and CloudSat cloud profiling radar (CPR) snowfall product. Both brightness temperature (TB) variables and global forecast system (GFS) output variables are evaluated for snowfall detection in this algorithm. Results show that combining TB variables and GFS variables provide the optimal snowfall detection performance. The Heidke skill score (HSS) values are about 0.56 over all three surface types, and the probability of detection (POD) values are 0.76, 0.70, and 0.72 over ocean, sea ice, and coast, respectively. The importance of the GFS variables differs greatly among these three surface types. The detection algorithm primarily depends on TB variables over ocean and HSS only increased by 0.05 by adding GFS variables. In contrast, GFS variables are critically important to snowfall detection over sea ice and coastal regions. Without GFS variables, the HSS values over both sea ice and coastal regions decrease sharply from about 0.56 to about 0.40. Over ocean, we also developed a regional snowfall detection model in each 10° grid box, which greatly outperform the global detection model over certain regions (e.g., sea of Okhotsk and Labrador Sea). Case studies and validation against NOAA-20 observations showed that the snowfall detection algorithm performs well, which will benefit coastal communities by providing information on snowstorms offshore before they transition to land.