OBJECTIVES: NASA S NEXT GENERATION COLD LAND PROCESSES EXPERIMENT(S) (NG-CLPX) WILL COLLECT A WIDE RANGE OF GROUND-BASED AIRBORNE AND SATELLITE-BASED MEASUREMENTS. THESE WILL INCLUDE FIELD OBSERVATIONS OBTAINED WHILE RESEARCHERS TRAVEL ACROSS THE SNOW AND VARIOUS REMOTE SENSING INSTRUMENTS MAKING MEASUREMENTS FROM DIFFERENT GROUND-BASED AIRBORNE AND SATELLITE PLATFORMS. INDIVIDUALLY AND IN THEIR RAW FORM ALL OF THESE MEASUREMENTS WILL HAVE MINIMAL VALUE. BUT TOGETHER THEY CAN BE USED TO SOLVE A WIDE RANGE OF SNOWRELATED PROBLEMS. THIS PROPOSED WORK WILL IMPLEMENT A DATA-MODEL FUSION SYSTEM THAT WILL CONVERT THE EXTENSIVE SUITE OF NG-CLPX RAW NUMBERS INTO VALUE-ADDED PRODUCTS THE SNOW-SCIENCE COMMUNITY CAN USE TO ANSWER INDIVIDUAL-PI AND COMMUNITY-WIDE SCIENCE QUESTIONS. BACKGROUND: SNOW-PROPERTY DISTRIBUTIONS AROUND THE WORLD EXIST AT DIFFERENT SPATIAL AND TEMPORAL RESOLUTIONS FOR WELL-DEFINED PHYSICAL REASONS. BECAUSE OF THIS WE NOW HAVE PHYSICALLY BASED MODELING SYSTEMS THAT WHEN DRIVEN WITH ACCURATE METEOROLOGICAL INPUTS CAN REPRODUCE OBSERVED SNOW-PROPERTY DISTRIBUTIONS. UNFORTUNATELY SIMULATIONS WITH THESE MODELING TOOLS ARE OFTEN LIMITED BY AVAILABLE HIGHRESOLUTION METEOROLOGICAL AND SPATIAL INPUTS; A PROBLEM THAT EXISTS THROUGHOUT THE WORLD S SNOW-COVERED REGIONS AND A PROBLEM THAT IS EXPECTED TO EXIST WELL INTO THE FUTURE. IN ADDITION TO OUR MODELING TOOLS NEW REMOTE SENSING INSTRUMENTS AND APPROACHES ARE ADDING VALUABLE SNOW-PROPERTY DISTRIBUTION AND EVOLUTION INFORMATION THAT CAN BE USED TO ADDRESS SNOW-RELATED SCIENCE QUESTIONS. BUT DEPENDING ON THE INDIVIDUAL SENSOR THE ASSOCIATED REMOTE SENSING INFORMATION ALSO COMES WITH STRENGTHS AND WEAKNESSES. NO SINGLE REMOTE-SENSING INSTRUMENT WILL ANSWER ALL OF OUR SNOW SCIENCE AND MANAGEMENT QUESTIONS AND THE ONLY PATH FORWARD IS TO USE A COMBINATION OF REMOTE SENSING DATA AND MODELING TOOLS. THE FOUNDATION OF THIS PROPOSED WORK IS THAT EACH GROUND OBSERVATION REMOTE SENSING MEASUREMENT AND MODEL RESULT CAN CONTRIBUTE TOWARD UNVEILING THE SNOW PUZZLE AND THAT TOGETHER THEY CAN BE USED TO COMPREHENSIVELY ANSWER OUR SNOW-SCIENCE QUESTIONS. WE CALL THIS DATA-MODEL FUSION. PROPOSED WORK: DATA-MODEL FUSION IS THE PROCESS OF INTEGRATING MULTIPLE OBSERVED DATASETS AND MODEL REPRESENTATIONS OF REAL-WORLD FEATURES (E.G. SNOW WATER EQUIVALENT ALBEDO GRAIN SIZE HARDNESS DENSITY DEPTH THERMAL RESISTANCE LIQUID WATER CONTENT AND SURFACE ROUGHNESS) INTO A CONSISTENT ACCURATE AND USEFUL DEPICTION OF THOSE FEATURES. IN THIS CONTEXT THE STRENGTHS OF DIFFERENT SENSORS OBSERVATIONS AND MODEL REPRESENTATIONS CAN COMPENSATE FOR THE WEAKNESSES OF OTHERS. THIS PROPOSED WORK WILL DEVELOP A MULTI-SENSOR MULTI-DATA AND MULTI-MODEL FUSION SYSTEM THAT WILL MERGE REMOTE SENSING DATA AND MODELING TOOLS TO CREATE VALUE-ADDED SNOW-PROPERTY PRODUCTS THAT CAN BE USED BY THE SNOW-SCIENCE COMMUNITY TO: 1) DEFINE SATELLITE REMOTE SENSING NEEDS REQUIREMENTS AND STRATEGIES; 2) IMPROVE REMOTE SENSING ALGORITHMS AND PROCEDURES; AND 3) ANSWER PRESSING SNOW-SCIENCE QUESTIONS. CONTEXT: THIS WORK IS REQUIRED FOR NASA S NG-CLPX TO SUCCEED. WITHOUT THE ABILITY TO INTEGRATE AND SYNTHESIZE THE WIDE RANGE OF DATASETS GENERATED DURING SUCH AN EXPERIMENT NASA WILL BE UNABLE TO FULFILL ITS GOALS OF ADVANCING REMOTE SENSING TOOLS TO DEVELOP A PREDICTIVE UNDERSTANDING OF THE ROLE THAT SNOW PLAYS IN LAND-ATMOSPHERE INTERACTIONS AND OTHER ASPECTS OF THE CLIMATE SYSTEM. THIS WORK WILL PLAY A KEY ROLE IN NASA S NEXT-GENERATION EFFORT TO CONVERT RAW OBSERVATIONS TO VALUABLE AND USEFUL INFORMATION.