FR1.P2.3
A DEEP LEARNING-BASED APPROACH FOR ESTIMATING THIN CLOUD TOP-OF-ATMOSPHERE REFLECTANCE
Hao Fu, Jiang Qian, Haitao Lyu, Yong Wang, University of Electronic Science and Technology of China, China
Session:
FR1.P2: Precipitation and Clouds I Oral
Track:
Atmosphere Applications
Location:
Room P2
Presentation Time:
Friday, 8 August, 08:30 - 08:45
Session Chair:
David Schvartzman , The University of Oklahoma
Presentation
Discussion
Resources
No resources available.
Session FR1.P2
FR1.P2.1: Multi-Domain Precipitation Retrievals Using Machine Learning and Satellite Observations from the GOES-R Series
Yifan Yang, Haonan Chen, Mahmood Azimi-Sadjadi, Colorado State Univeristy, United States
FR1.P2.2: The evaluation of FengYun-3 cloud products
Jian liu, Xi Wang, Zhaojun Zheng, National Satellite Meteorological Center, China Meteorological Administration, China
FR1.P2.3: A DEEP LEARNING-BASED APPROACH FOR ESTIMATING THIN CLOUD TOP-OF-ATMOSPHERE REFLECTANCE
Hao Fu, Jiang Qian, Haitao Lyu, Yong Wang, University of Electronic Science and Technology of China, China
FR1.P2.4: Micro Rain Radar based Virga Detection Algorithm
Lekhraj Saini, Saurabh Das, Indian Institute of Technology Indore, India; Nuncio Murukesh, National Center for Polar and Research, Ministery of Earcth Sciences, Govt. of India, India
FR1.P2.5: Weather Radar Beam Blockage Correction Using Deep Learning for Improved Precipitation Remote Sensing
Songjian Tan, Haonan Chen, Colorado State University, United States
Resources
No resources available.