TU1.R17.5

ASSESSING THE REPRESENTATION OF WIND, TERRAIN, AND VEGETATION EFFECTS ON SNOW DENSITY DISTRIBUTED BY LEARNED REGRESSION MODELING

Tate Meehan, Eli Deeb, Cold Regions Research and Engineering Laboratory, United States; H.P. Marshall, Boise State University, United States; Shad O'Neel, Cold Regions Research and Engineering Laboratory, United States
Session:
TU1.R17: Microwave Modeling and Observation of Snow Oral
Track:
Cryosphere
Location:
Room 212/214
Presentation Time:
Tue, 18 Jul, 09:18 - 09:30 Pacific Time (UTC -8)
Session Co-Chairs:
Michael Durand, Ohio State University and Leung Tsang, University of Michigan
Session Manager:
Soumitra Sakhalkar
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Session TU1.R17
TU1.R17.1: FULL WAVE SIMULATIONS OF EFFECTIVE PERMITTIVITY AND RADAR SCATTERING OF SNOW AND FIRN AT L AND C-BAND
Zhenming Huang, Firoz Borah, Haokui Xu, Leung Tsang, University of Michigan, United States; Edward Kim, NASA, United States
TU1.R17.2: PHYSICAL-STATISTICAL RETRIEVAL OF SWE FROM SNOWSAR
Siddharth Singh, University of Illinois Urbana-Champaign, United States; Michael Durand, Ohio State University, United States; Edward Kim, NASA Goddard Space Flight Center, United States; Jinmei Pan, Chinese Academy of Sciences, China; Do Hyuk Kang, NOAA Weather Program Office, United States; Ana Barros, University of Illinois Urbana-Champiagn, United States
TU1.R17.3: SNOW DENSITY AND GROUND PERMITTIVITY RETRIEVAL PROBLEM WITH L-BAND SATELLITE RADIOMETER OBSERVATIONS – CASE STUDY FROM SODANKYLÄ, FINLAND
Manu Holmberg, Juha Lemmetyinen, Finnish Meteorological Institute, Finland; Mike Schwank, Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland; Anna Kontu, Kimmo Rautiainen, Finnish Meteorological Institute, Finland
TU1.R17.4: HIGH-ALTITUDE MEASUREMENTS OF SNOW THICKNESS USING ULTRA-WIDEBAND MICROWAVE RADAR
Hara Talasila, Fernando Rodriguez-Morales, John Paden, Carl Leuschen, Shravan Kaundinya, University of Kansas, United States
TU1.R17.5: ASSESSING THE REPRESENTATION OF WIND, TERRAIN, AND VEGETATION EFFECTS ON SNOW DENSITY DISTRIBUTED BY LEARNED REGRESSION MODELING
Tate Meehan, Eli Deeb, Cold Regions Research and Engineering Laboratory, United States; H.P. Marshall, Boise State University, United States; Shad O'Neel, Cold Regions Research and Engineering Laboratory, United States
Resources
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