FR1.R19.1
Improving Operational Weather Radar Rainfall Mapping Using A Deep Learning Technique
Haonan Chen, Colorado State University, United States; Luyao Sun, Sun Yat-Sen University, China
Session:
FR1.R19: Artificial Intelligence for Active and Passive Remote Sensing of Precipitation Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Fri, 14 Aug, 08:30 - 08:45
Presentation
Discussion
Resources
No resources available.
Session FR1.R19
FR1.R19.1: Improving Operational Weather Radar Rainfall Mapping Using A Deep Learning Technique
Haonan Chen, Colorado State University, United States; Luyao Sun, Sun Yat-Sen University, China
FR1.R19.2: RAINFALL RETRIEVAL FROM CROSS TRACK PASSIVE MICROWAVE OBSERVATIONS USING A TRANSFORMER-BASED DEEP LEARNING FRAMEWORK
Mahyar Garshasbi, Buddha Subedi, Ardeshir Ebtehaj, University of Minnesota, United States
FR1.R19.3: GPTCAST-BASED RADAR NOWCASTING FOR HAILSTORM PREDICTION IN COLORADO AND WYOMING
V. Chandrasekar, Vishnu Charan Venkatesh, Chandrasekar Radhakrishnan, Sounak Biswas, Renzo Bechini, Colorado State University, United States
FR1.R19.4: Continual Learning for Multi-Domain Precipitation Retrievals Using Satellite Observations from the GOES-R Series
Yifan Yang, Haonan Chen, Colorado State University, United States
FR1.R19.5: The Effects of Ice Habit Models on Passive Microwave Snowfall Rate Retrievals
Yulan Hong, CISESS/Earth System Science Interdisciplinary Center, University of Maryland, United States; Huan Meng, NOAA/NESDIS/Center for Satellite Applications and Research (STAR), United States; Yongzhen Fan, Jun Dong, CISESS/Earth System Science Interdisciplinary Center, University of Maryland, United States; Tong Ren, Ping Yang, Texas A&M University, United States
Contacts