SNOW IS CRITICALLY IMPORTANT TO HUMAN WELFARE AFFECTING WATER SUPPLY SECURITY ECONOMICS ENERGY AND CLIMATE. UNDERSTANDING SEASONAL SNOW COVER IS ALSO CRITICAL TO UNDERSTANDING THE FATE OF FROZEN GROUND GLACIERS AND SEA ICE. GLOBAL SNOW RESERVES ARE RAPIDLY CHANGING BUT WE CURRENTLY LACK EFFECTIVE MEANS FOR ACCURATELY TRACKING SNOW AMOUNTS AND HOW THEY ARE CHANGING. A KEY MOTIVATING SCIENCE QUESTION IS: WHERE WHY AND BY HOW MUCH ARE GLOBAL STORES OF SEASONAL SNOW CHANGING? THIS SCIENCE QUESTION ISRELEVANT ACROSS SPATIAL (A MOUNTAIN WATERSHED TO THE ENTIRE EARTH) AND TEMPORAL (DAYS TO SEASONS TO CENTURIES) SCALES. TO ANSWER THIS QUESTION WE NEED FREQUENT YET LONG-TERM MEASUREMENTS OF SNOW AT HIGH SPATIAL RESOLUTIONS ACROSS THE GLOBE. TO DATE THIS HAS PROVED CHALLENGING AND SNOW IS THE ONLY COMPONENT OF THE HYDROLOGIC CYCLE THAT DOES NOT YET HAVE A DEDICATED SPACE MISSION. FORTUNATELY RECENT DEVELOPMENTS IN 4 AREAS (MODELING DATA ASSIMILATION LIDAR AND RADAR REMOTE SENSING) IF INTEGRATED INTO A SEAMLESS SYSTEM COMBINED WITH LEGACY NASA OBSERVATIONS (PASSIVE MICROWAVE SNOW SURFACE TEMPERATURE AND SNOW COVERED AREA) COULD DELIVER SNOW DEPTH AND SNOW WATER EQUIVALENT (SWE) DATA AT LOCAL TO GLOBAL SCALES BENEFITING BILLIONS OF PEOPLE.FIRST PHYSICALLY-BASED SNOW MODELING CAN ACCURATELY SIMULATE SNOW DEPTH AND WATER EQUIVALENT IN ANY AREA WITH ACCURATE METEOROLOGICAL FORCING DATA (SNOWFALL WINDS TOPOGRAPHY ENERGY BALANCE) AND GENERALLY CAN REASONABLY SIMULATE SNOW LAYERS SNOW DENSITY AND SNOW ALBEDO AS WELL. THE CAVEAT IS THAT WE GENERALLY DO NOT KNOW HOW MUCH SNOW IS FALLING FROM THE SKY (WHILE GPM HAS GREATLY ENHANCED GLOBAL RAINFALL ESTIMATES SNOW IS STILL ONLY DETECTED AND NOT QUANTIFIED BY THE SYSTEM). THEREFORE DATA ASSIMILATION MUST BE IMPLEMENTED TO UTILIZE SNOW OBSERVATIONS TO UPDATE THE SNOW MODELS WITH HOW MUCH SNOW ACTUALLY ACCUMULATED IN A STORM OR SEQUENCE OF STORMS. NEW DEVELOPMENTS SUGGEST THAT LIDAR (SNOW DEPTH) AND RADAR (SWE) CAN PROVIDE VALUABLE INPUT WHILE LEGACYOBSERVATIONS IN THE VISIBLE (SNOW COVERED AREA) INFRARED (SNOW SURFACE TEMPERATURE) AND MICROWAVE (SWE IN SHALLOW DRY SHOW) WILL ALSO CONSTRAIN MODELS TO THE REALM ON TRUTH. HOWEVER DATA ASSIMILATION RELIES CRITICALLY ON KNOWING THE UNKNOWNS; IN OTHER WORDS IN ACCURATELY IDENTIFYING AND CORRECTING BIASES IN DATA STREAMS AND IN ACCURATELY DEFINING THE RANGE OF UNCERTAINTY IN EACH MEASUREMENTAND EACH MODEL ESTIMATE.TO KNOW THE UNKNOWNS WE PROPOSE MULTI-YEAR FIELD EXPERIMENTS DESIGNED TO RIGOROUSLY ELUCIDATE THE ACCURACY AND UNCERTAINTY ASSOCIATED WITH EACH MODELED AND OBSERVED VARIABLE UNDER A VARIETY OF PHYSICAL CONDITIONS TO CREATE A SET OF ERROR MODELS TO GUIDE DATA ASSIMILATION. TO BEST ACHIEVE THIS GOAL WE PROPOSE A SEQUENCE OF OBSERVING SYSTEM SIMULATION EXPERIMENTS (OSSES). THE FIRSTOSSES WILL ESTIMATE HOW EACH GROUND AND AIRBORNE FIELD CAMPAIGN SHOULD BE CONDUCTED. THEN FOLLOWING IMPROVEMENTS TO BOTH THE PHYSICALLY-BASED SNOW MODELS AND THE DATA ASSIMILATION ERROR MODELS BASED ON OBSERVATIONS THE SUBSEQUENT OSSES WILL DETERMINE WHICH COMBINATION OF INSTRUMENTS AND SAMPLING STRATEGIES SHOULD BE TARGETED FOR A SPACE-BASED OBSERVATION PROGRAM.