Significance　Severe　local　storms,　hail,　squall　lines,　and　tornadoes　significantly　affect　daily　life,　social　activities,　and　economic　development.　Despite　their　importance,　understanding　the　mechanisms　of　severe　storms　and　improving　their　forecasts　remains　a　challenging　task.　Nowcasting　focuses　on　high-impact　weather　(HIW)　events　that　develop　rapidly　and　have　short　durations.　After　half　a　century　of　development,　Fengyun　meteorological　satellites　have　become　a　crucial　component　of　the　global　observation　network.　They　provide　essential　data　for　monitoring　severe　weather,　generating　early　warnings,　and　contributing　to　numerical　weather　forecasting,　climate　projections,　environmental　assessments,　and　predictive　analyses.　Notably,　in　the　past　decade,　the　advent　of　the　new　generation　of　Fengyun　satellites　has　brought　quantitative　products　to　the　forefront　of　operational　use.　We　review　the　latest　advances　in　the　applications　of　Fengyun　meteorological　satellites　in　short-term　weather　nowcasting　and　highlight　the　principal　scientific　and　technical　challenges　that　future　research　endeavors　need　to　address.　Progress　China　has　actively　utilized　the　new　generation　Fengyun　meteorological　satellite　data　to　improve　near　real-time　(NRT)　forecasting　and　nowcasting　capabilities.　The　China　Meteorological　Administration　(CMA)　assimilates　these　observation　data　into　numerical　weather　prediction　(NWP)　models　to　enhance　short-range　and　middle-range　weather　forecasts.　In　addition,　the　National　Satellite　Meteorological　Center　(NSMC)　of　the　CMA　processes　these　data　to　produce　and　distribute　quantitative　information　on　the　atmosphere,　clouds,　and　precipitation.　These　quantitative　products,　delivered　to　users　on　time　through　advanced　communication　and　data　distribution　technologies,　are　crucial　for　NRT　nowcasting　applications　and　have　played　a　significant　role　in　monitoring　and　early　warning　of　HIW　events.　Besides　operational　Fengyun　satellite　products,　progress　has　been　made　in　developing　new　products　and　prediction　models　for　0-6　h　forecasts,　particularly　using　data　from　the　Fengyun-4　series.　1　New　Products　and　Applications　1)　Radar　composite　reflectivity　estimation　(RCRE).　Ground-based　weather　radar　observations　are　commonly　used　to　track　convective　storms;　however,　the　radar　network’s　coverage　is　limited,　especially　in　mountainous　and　marine　areas.　Fengyun-4　satellites　provide　extensive　coverage　and　NRT　observations,　compensating　for　radar’s　limitations.　Since　the　physical　properties　of　clouds　can　be　reflected　in　both　ground-　based　radar　and　satellite　observations,　a　connection　exists　between　the　two.　Using　deep　learning　methods,　Yang　et　al.　developed　the　radar　composite　reflectivity　estimation　(RCRE)　using　Fengyun-　4A　AGRI　observations.　Independent　validation　indicates　that　RCRE　accurately　reproduces　radar　echoes’　position,　shape,　and　intensity.　This　RCRE　product　is　operationally　used　by　the　National　Meteorological　Center　(NMC)　and　provides　synthetic　radar　data　for　nowcasting　applications　where　ground-　based　radar　is　unavailable.　2)　Automatic　recognition　of　convection　clouds.　Monitoring　convective　clouds　from　satellites　is　vital　for　nowcasting.　Traditional　techniques　rely　on　thresholds,　such　as　using　the　240　-　258　K　range　to　identify　convective　clouds　from　11　μm　brightness　temperature　images.　For　rapidly　changing　convective　systems,　these　methods　are　often　regional,　seasonal,　and　weather-　dependent.　To　address　this,　the　K-　means　clustering　method　is　used　to　analyze　cloud　types　over　China　from　AGRI　infrared　band　brightness　temperature　measurements.　This　method　enables　users　to　select　regions　of　interest　and　automatically　identify　convective　systems　and　other　cloud　types　in　NRT,　improving　quantitative　precipitation　estimation　(QPE)　from　satellite　IR　data.　For　instance,　this　product　can　enhance　convective　cloud　precipitation　estimation　and　provide　valuable　information　on　convection　coverage　and　intensity,　especially　in　areas　without　radar　observations.　Figure　1　shows　the　automatic　identification　of　convective　clouds　based　on　Fengyun-　4A　on　July　30,　2023.　Due　to　the　northward　influence　of　the　typhoon’s　peripheral　cloud　system,　the　northern　and　central　parts　of　Shanxi,　Hebei,　Beijing,　and　Tianjin　are　completely　covered　by　large　areas　of　convective　clouds,　with　maximum　hourly　precipitation　exceeding　40　mm/h.　The　convective　clouds　correspond　well　with　the　radar　observations　[Fig.　1(b)].　3)　Cloud-　base　height.　The　cloud　top　height　(CTH)　product　is　well-　established　and　widely　used,　while　cloud　base　height　(CBH)　is　challenging　to　obtain　due　to　weak　signals　in　passive　remote　sensing　observations.　However,　CBH　is　crucial　for　understanding　vertical　atmospheric　motion,　aviation　safety,　and　weather　analysis.　The　physical　method　for　retrieving　CBH　involves　converting　cloud　optical　thickness　into　physical　thickness　and　subtracting　it　from　CTH.　The　uncertainty　of　optical　thickness　is　the　main　error　source　for　CBH　retrieval　using　the　physical　method.　To　overcome　this　limitation,　a　machine　learning　model　trained　on　satellite-　based　lidar　(CALIOP　from　CALIPSO　satellite)　observations,　which　has　good　accuracy　but　limited　coverage,　has　been　used　to　derive　CBH　by　combining　NWP　products　and　Fengyun-　4　AGRI　observations　as　input.　This　algorithm　provides　a　CBH　product　with　the　same　coverage　as　CTH　(AGRI　full　disk).　Independent　validation　shows　an　overall　root　mean　square　error　(RMSE)　of　1.87　km.　This　CBH　product,　along　with　the　traditional　CTH　product,　offers　valuable　information　on　cloud　structure　and　physical　thickness,　enhancing　nowcasting　applications.　2　Prediction　Models　Using　Fengyun-　4　Data　for　Nowcasting　1)　Storm-　warning　in　pre-　convection　environment.　Severe　local　storms　typically　have　three　stages:　pre-　convection,　initiation,　and　development.　Identifying　the　pre-　convection　environment　is　crucial　for　nowcasting　and　providing　warnings　before　radar　observations.　By　integrating　high　spatiotemporal　resolution　AGRI　observations　from　the　Fengyun-　4　series　with　CMA　NWP　products,　key　factors　in　the　pre-　convection　environment　can　be　analyzed.　Li　et　al.　developed　the　storm　warning　in　the　pre-　convection　environment　version　2.0　(SWIPE2.0)　model　for　China　and　surrounding　areas　using　machine　learning　techniques.　This　model　identifies　potential　convective　systems　and　classifies　cloud　clusters　into　strong,　medium,　or　weak　convection.　SWIPE2.0　predicts　storm　occurrence　and　intensity　0　-　2　h　ahead　of　radar　observations　and　is　used　in　NRT　applications　by　the　NMC/CMA.　For　example,　the　SWIPE2.0　model　issued　a　severe　convective　warning　for　a　cloud　mass　located　in　the　western　part　of　Gansu　province　at　14:　30　on　July　10,　2023　(Beijing　time).　At　that　time,　the　ground-based　radar　reflectivity　of　about　20　dBz　or　lower　is　mainly　near　the　provincial　boundary,　while　the　satellite　warning　signals　did　not　correspond　to　ground-　based　radar　signals,　indicating　that　precipitation　had　not　yet　occurred.　At　14:　34,　the　red　severe　convective　warning　signal　still　existed,　and　its　range　expanded　slightly　to　the　southeast.　As　the　cloud　developed　and　moved　towards　the　southeast,　it　produced　precipitation　greater　than　1　mm/h　between　15:　00　and　16:　00,　with　some　local　areas　experiencing　rainfall　exceeding　5　mm/h.　SWIPE2.0　provides　early　warnings　for　local　convection　before　ground-　based　radar　observations.　2)　Satellite　image　extrapolation.　Similar　to　radar　extrapolation,　satellite　image　extrapolation　is　essential　for　short-　term　forecasting　and　applications　such　as　solar　photovoltaic　power　generation.　The　rapid　advancement　of　artificial　intelligence　has　led　to　the　adoption　of　data-　driven　machine　learning　methods　in　satellite　image　extrapolation.　Xia　et　al.　developed　an　hourly　cloud　cover　prediction　algorithm　using　high　spatiotemporal　resolution　geostationary　satellite　images.　This　model　predicts　cloud　images　for　the　next　0　-　4　h　and　estimates　cloud　cover　over　photovoltaic　stations.　Independent　validation　shows　reliable　and　stable　performance　in　the　first　two　hours,　with　an　average　correlation　coefficient　of　nearly　0.9　between　predicted　and　observed　cloud　cover.　Compared　to　previous　methods　of　only　being　able　to　perform　10　-　30　min　of　extrapolation,　the　new　approach　greatly　improves　accuracy　and　forecasting　time,　making　it　valuable　for　regional　short-term　warnings.　Conclusions　and　Prospects　As　a　key　member　of　the　global　observing　system,　the　Fengyun　meteorological　satellite　system　has　significantly　enhanced　observation　capabilities,　short-term　monitoring,　and　early　warning.　However,　challenges　remain　in　applying　Fengyun　satellite　data　for　nowcasting,　particularly　in　achieving　low　latency　and　high-quality　products　with　high　spatiotemporal　resolution.　With　ongoing　advancements　in　Fengyun　satellite　technology,　quantitative　nowcasting　applications　are　entering　a　new　era.　The　future　direction　involves　combining　Fengyun　satellite　quantitative　products,　NWP　products,　ground-based　measurements　including　radar,　and　other　multi-source　data　with　artificial　intelligence　to　improve　the　identification,　monitoring,　and　early　warning　of　severe　weather　events.　©　2024　Chinese　Optical　Society.　All　rights　reserved.