Monitoring　and　predicting　highly　localized　weather　events　over　a　very　short-term　period,　typically　ranging　from　minutes　to　a　few　hours,　are　very　important　for　decision　makers　and　public　action.　Nowcasting　these　events　usually　relies　on　radar　observations　through　monitoring　and　extrapolation.　With　advanced　high-resolution　imaging　and　sounding　observations　from　weather　satellites,　nowcasting　can　be　enhanced　by　combining　radar,　satellite,　and　other　data,　while　quantitative　applications　of　those　data　for　nowcasting　are　advanced　through　using　machine　learning　techniques.　Those　applications　include　monitoring　the　location,　impact　area,　intensity,　water　vapor,　atmospheric　instability,　precipitation,　physical　properties,　and　optical　properties　of　the　severe　storm　at　different　stages　(pre-convection,　initiation,　development,　and　decaying),　identification　of　storm　types　(wind,　snow,　hail,　etc.),　and　predicting　the　occurrence　and　evolution　of　the　storm.　Satellite　observations　can　provide　information　on　the　environmental　characteristics　in　the　preconvection　stage　and　are　very　useful　for　situational　awareness　and　storm　warning.　This　paper　provides　an　overview　of　recent　progress　on　quantitative　applications　of　satellite　data　in　nowcasting　and　its　challenges,　and　future　perspectives　are　also　addressed　and　discussed.