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RECENT YEARS HAVE SEEN MAJOR COMMUNITY-WIDE ADVANCES IN THREE AREAS: 1. METHODS FOR GRAIN SIZE MEASUREMENTS HAVE MATURED SIGNIFICANTLY. MICROSTRUCTURE PROPERTIES SUCH AS SNOW SPECIFIC SURFACE AREA (SSA) WHICH CONTROLS THE SCATTERING OF BOTH ACTIVE AND PASSIVE MICROWAVE RADIATION CAN NOW BE MEASURED IN THE FIELD AND IN THE LABORATORY. 2. ADVANCES IN BACKSCATTER MODELING HAVEBEEN MADE; THESE MODELS ARE INCREASINGLY PARAMETERIZED BY MEASURABLE QUANTITIES. AND THIRD SIGNIFICANT FIELD AND AIRBORNE DATASETS OF RADAR BACKSCATTER AND SNOW PROPERTIES HAVE BEEN COLLECTED. PHYSICALLY-BASED SWE RETRIEVAL ALGORITHMS ALLOWING FOR MULTIPLE SNOWPACK LAYERS ARE NOW POSSIBLE.NEXT-GENERATION NASA COLD LANDS PROCESSES EXPERIMENT (NGCLPX) FIELD ACTIVITIES TO OCCUR IN WINTERS OF 2019 2020 AND 2021 ALONG WITH THE 2017 SNOWEX EXPERIMENT ARE EXPECTED TO INCLUDE RADAR (AND PERHAPS ALSO PASSIVE MICROWAVE) MEASUREMENTS.WE PROPOSE UTILIZATION OF THE NEWLY-DEVELOPED BAYESIAN SNOW WATER EQUIVALENT ESTIMATION (BASE) ALGORITHM AS PART OF THESE EXPERIMENTS. THE BASE ALGORITHM IS FLEXIBLE RELYING ON RELATIVELY MODULAR PHYSICALLY-BASED RADIATIVE TRANSFER MODELS. BASE PROVIDES A QUANTITATIVE ESTIMATION OF THE SWE. A MAJOR ADVANTAGE OF THE SCHEME IS THAT IT ESTIMATES SWE ALONG WITH SSA AND SNOWPACK LAYERING SIMULTANEOUSLY; THUS EACH PART OF THE RETRIEVAL CAN BE COMPARED WITH FIELD OBSERVATIONS.WE PROPOSE TO SUPPORT NGCLPX AS FOLLOWS: 1) AID IN PLANNING AND PREPARATION FOR THE NGCLPX FIELD ACTIVITIES IN ORDER TO REPRESENT THE PERSPECTIVE OF THE MICROWAVE RETRIEVAL ALGORITHMS AND HELP TO GUIDE AND CONTRIBUTE TO WRITING THE SCIENCE PLAN AND IMPLEMENTATION PLAN; 2) UTILIZE THE SNOWEX AND NGCLPX DATA ALONG WITH EXISTING DATASETS AND WORKING TOGETHER WITH THE REST OF THE SNOWEXAND NGCLPX TEAM TO MAKE A MAJOR CONTRIBUTION TO DEFINING EXPECTED SWE ACCURACY BASED ON WELL-DOCUMENTED RADAR RETRIEVAL ALGORITHMS; 3) ILLUSTRATE EXTENSION OF THE COMPUTATIONALLY-EXPENSIVE BASE ALGORITHM TO FUTURE SPACEBORNE APPLICATIONS BY CONSTRUCTING A SIMPLE ARTIFICIAL NEURAL NETWORK EMULATOR OF THE ALGORITHM.

$514,776FY2020National Aeronautics and Space AdministrationNASA

Ohio State University, The, Columbus OH

Investigators

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RECENT YEARS HAVE SEEN MAJOR COMMUNITY-WIDE ADVANCES IN THREE AREAS: 1. METHODS FOR GRAIN SIZE MEASUREMENTS HAVE MATURED SIGNIFICANTLY. MICROSTRUCTURE PROPERTIES SUCH AS SNOW SPECIFIC SURFACE AREA (SSA) WHICH CONTROLS THE SCATTERING OF BOTH ACTIVE AND PASSIVE MICROWAVE RADIATION CAN NOW BE MEASURED IN THE FIELD AND IN THE LABORATORY. 2. ADVANCES IN BACKSCATTER MODELING HAVEBEEN MADE; THESE MODELS ARE INCREASINGLY PARAMETERIZED BY MEASURABLE QUANTITIES. AND THIRD SIGNIFICANT FIELD AND AIRBORNE DATASETS OF RADAR BACKSCATTER AND SNOW PROPERTIES HAVE BEEN COLLECTED. PHYSICALLY-BASED SWE RETRIEVAL ALGORITHMS ALLOWING FOR MULTIPLE SNOWPACK LAYERS ARE NOW POSSIBLE.NEXT-GENERATION NASA COLD LANDS PROCESSES EXPERIMENT (NGCLPX) FIELD ACTIVITIES TO OCCUR IN WINTERS OF 2019 2020 AND 2021 ALONG WITH THE 2017 SNOWEX EXPERIMENT ARE EXPECTED TO INCLUDE RADAR (AND PERHAPS ALSO PASSIVE MICROWAVE) MEASUREMENTS.WE PROPOSE UTILIZATION OF THE NEWLY-DEVELOPED BAYESIAN SNOW WATER EQUIVALENT ESTIMATION (BASE) ALGORITHM AS PART OF THESE EXPERIMENTS. THE BASE ALGORITHM IS FLEXIBLE RELYING ON RELATIVELY MODULAR PHYSICALLY-BASED RADIATIVE TRANSFER MODELS. BASE PROVIDES A QUANTITATIVE ESTIMATION OF THE SWE. A MAJOR ADVANTAGE OF THE SCHEME IS THAT IT ESTIMATES SWE ALONG WITH SSA AND SNOWPACK LAYERING SIMULTANEOUSLY; THUS EACH PART OF THE RETRIEVAL CAN BE COMPARED WITH FIELD OBSERVATIONS.WE PROPOSE TO SUPPORT NGCLPX AS FOLLOWS: 1) AID IN PLANNING AND PREPARATION FOR THE NGCLPX FIELD ACTIVITIES IN ORDER TO REPRESENT THE PERSPECTIVE OF THE MICROWAVE RETRIEVAL ALGORITHMS AND HELP TO GUIDE AND CONTRIBUTE TO WRITING THE SCIENCE PLAN AND IMPLEMENTATION PLAN; 2) UTILIZE THE SNOWEX AND NGCLPX DATA ALONG WITH EXISTING DATASETS AND WORKING TOGETHER WITH THE REST OF THE SNOWEXAND NGCLPX TEAM TO MAKE A MAJOR CONTRIBUTION TO DEFINING EXPECTED SWE ACCURACY BASED ON WELL-DOCUMENTED RADAR RETRIEVAL ALGORITHMS; 3) ILLUSTRATE EXTENSION OF THE COMPUTATIONALLY-EXPENSIVE BASE ALGORITHM TO FUTURE SPACEBORNE APPLICATIONS BY CONSTRUCTING A SIMPLE ARTIFICIAL NEURAL NETWORK EMULATOR OF THE ALGORITHM. · GrantIndex