F5.5
A Novel Transformer-based Deep Learning Model for Satellite Precipitation Enhancement
Liping Wang, Haonan Chen, Colorado State University, United States
Session:
F5: Atmospheric Remote Sensing Lecture
Track:
Commission F: Wave Propagation and Remote Sensing
Location:
Flatirons 4
Presentation Time:
Thu, 8 Jan, 16:40 - 17:00 MT (UTC -7)
Session Co-Chairs:
William Blackwell, MIT Lincoln Laboratory and Steven Reising, Colorado State University
Session F5
F5.1: Recent Progress in Smallsat Constellations, Beam-Steering Arrays, and Cognitive Sensing for Microwave Imaging and Sounding of the Atmosphere
William Blackwell, MIT Lincoln Laboratory, United States
F5.2: Sensing Earth’s Planetary Boundary Layer using Next-Generation Hyperspectral Microwave Sounders: High-frequency Microwave and Millimeter-wave Radiometer HD
Steven Reising, Colorado State University, United States; Ahmed Mohamed, Omkar Pradhan, Akim Babenko, Shannon T. Brown, Alan B. Tanner, Pekka Kangaslahti, Jet Propulsion Laboratory, United States; Renish Thomas, Sharmin Farzana, Malisa Abedin, Adam Whitney, Colorado State University, United States
F5.3: Tropospheric wind measurements with AMISR-14: Experimental setup and preliminary results
Alexander Massoud, Fabiano Rodrigues, The University of Texas at Dallas, United States; Jorge Chau, Leibniz Institute of Atmospheric Physics, Germany; Karim Kuyeng, Danny Scipión, Radio Observatorio de Jicamarca, Peru
F5.4: Characterizing the Atmospheric Channel during a Long Term Troposcatter Experiment
Jacob DeFilippis, Katherine Adams, Edward Bertot, Sunil Ramlall, Jia-Chi Chieh, NIWC Pacific, United States
F5.5: A Novel Transformer-based Deep Learning Model for Satellite Precipitation Enhancement
Liping Wang, Haonan Chen, Colorado State University, United States
F5.6: The Impact and Sources of Radio Frequency Interference on GNSS Signals
Endawoke Yizengaw, The Aerospace Corporation, United States
Contacts