FALLING SNOW IS A KEY COMPONENT FOR THE GLOBAL ATMOSPHERIC HYDROLOGICAL AND ENERGY CYCLES AND ITS RETRIEVAL FROM SPACE-BASED OBSERVATIONS REPRESENTS THE BEST CURRENT CAPABILITY TO EVALUATE IT GLOBALLY. PRECIPITATION RETRIEVAL ALGORITHMS HAVE BEEN DEVELOPED AND REFINED OVER THE FEW LAST YEARS AND THEIR ACCURACY AND RELIABILITY ARE BECOMING INCREASINGLY MORE IMPORTANT FOR EARTH S ENERGY AND RADIATION BUDGETS AS WELL AS FOR HUMAN ACTIVITIES. UNFORTUNATELY MANY ISSUES STILL AFFECT SNOWFALL RETRIEVALS AND ALGORITHMS PERFORMANCES CANNOT BE CONSIDERED ACCURATE AND RELIABLE ENOUGH YET. IN PARTICULAR PREVIOUS WORK SHOWED THAT WITHIN THE GLOBAL PRECIPITATION MEASUREMENT (GPM) MISSION THE GODDARD PROFILING (GPROF) ALGORITHM SNOWFALL RETRIEVAL PERFORMANCES STRONGLY DEPEND ON THE SNOWFALL TYPE. SOME TYPE OF SNOWFALL SUCH AS THE SHALLOW CONVECTIVE ONE ARE DETECTED WITH EXTREME DIFFICULTIES AND IN MOST OF THE CASES COMPLETELY MISSED. MOREOVER SINCE THE RETRIEVAL IS BASED ON PASSIVE MICROWAVE (PMW) DATA THE WELL-KNOWN ISSUES OF SNOW-COVERED SURFACES CONTAMINATING THE SIGNAL ARISE INDUCING FALSE ALARMS IN THE PRECIPITATION ESTIMATES. THE MAIN REASONS FOR GPROF BEHAVIOR HAVE BEEN IDENTIFIED IN THE BAYESIAN NATURE OF GPROF WHICH RELIES ON A-PRIORI DATABASES FOR ITS ESTIMATES. IF THE A-PRIORI DATABASES ARE NOT REPRESENTATIVE ENOUGH FOR A PARTICULAR TYPE OF PRECIPITATION EVENT THE ESTIMATES CANNOT ACTUALLY FIND A PERFECT MATCH AND MISS OR UNDERESTIMATE PRECIPITATION. IS REPRESENTATIVENESS THE PROBLEM OR IS NON-UNIQUENESS I.E. THE SAME TB SIGNATURE MAKE MEAN SOMETHING IN STRATIFORM SNOW BUT ANOTHER IN CONVECTIVE SNOW? SOME OTHER DETAILS OF GPROF (I.E. CHANNEL SENSITIVITY SURFACE CLASSIFICATION ETC.) COULD BE MODIFIED TO IMPROVE THE RETRIEVALS AND A CLASSIFICATION FLAG THAT RECOGNIZES THE SNOWFALL TYPE AND MAKES THE ALGORITHM SWITCHING ITS CHARACTERISTICS WOULD BE PARTICULARLY HELPFUL. MANY EFFORTS HAVE BEEN MADE TO PARTITION RAINFALL BETWEEN CONVECTIVE AND STRATIFORM BUT VERY FEW EFFORTS HAVE BEEN SPENT FOR SNOWFALL AND NONE OF THE METHODS DEVELOPED CAN BE APPLIED TO PMW RETRIEVALS. GIVEN THE IMPORTANCE OF CONVECTIVE SNOWFALL TYPE BOTH GLOBALLY (I.E. NORTHERN ATLANTIC REGION SEA OF JAPAN ANTARCTIC BELT ETC.) AND LOCALLY (I.E. LAKE-EFFECT SNOW OVER THE GREAT LAKES) THIS PROPOSAL AIMS TO PARTITION SNOWFALL TYPE PROVIDING A FLAG ABLE TO ACTIVATE A SPECIFIC AND DEDICATED SETUP OF GPROF. THIS WILL BE ACHIEVED THROUGH DEVELOPMENT OF A NEW DEEP NEURAL NETWORK (DNN) MODEL FOR SNOWFALL CLASS DETECTION. RELYING ON THE AVAILABLE CLOUDSAT DATA COMBINED CLOUDSAT-GPM DATASET AND RELEVANT ANCILLARY PRODUCTS GMI OBSERVATIONS WILL BE LINKED TO A NUMBER OF SNOWFALL CLASSES TO OFFER A COMPLEMENTARY INFORMATION CONTENT TO THE EXISTING PMW ALGORITHMS. IN THIS EFFORT A NEWLY DEVELOPED GMI NON-RAIN RETRIEVAL WITH SURFACE-SPECIFIC EMISSIVITY CONSTRAINTS WILL PROVIDE THE NECESSARY SKILL FOR PRECIPITATION DETECTION. AN EVALUATION EXERCISE WILL BE DONE USING PART OF THE CLOUDSAT DATASET (MAINLY OVER OCEAN) AND GROUND-BASED SENSORS (MULTI-RADAR MULTI-SENSOR DATASET OVER US FINLAND AND GREAT LAKES REGION RADARS).