MOP.P28.3

IN-CLOUD WIND AND RAIN OBSERVATION AT TIBET USING AIRBORNE DOPPLER SCATTORMETER

Wenbo Guo, Di Zhu, Zijin Zhang, Guoqing Xu, National Space Science Center, Chinese Academy of Sciences, China
Session:
MOP.P28: Clouds and Precipitation: Algorithms, Observations and Instruments Poster
Track:
Atmosphere Applications
Location:
Poster Area 28
Presentation Time:
Mon, 17 Jul, 14:15 - 15:45 Pacific Time (UTC -8)
Session Co-Chairs:
Chandrasekar Radhakrishnan, Colorado State University and kaya kanameru, NICT, JAPAN
Session Managers:
Al Adil Al Hinai and Ivan Arias and Shubham Awasthi and Ayoti Banerjee
Presentation
Not logged in.
Discussion
Not logged in.
Resources
No resources available.
Session MOP.P28
MOP.P28.1: PRECIPITATION JUDGEMENT OF THE DUAL-FREQUENCY PRECIPITATION RADAR IN GPM VERSION 7
Kaya Kanemaru, Hiroshi Hanado, National Institute of Information and Communications Technology, Japan
MOP.P28.2: EVALUATION OF ERA5, AND SPACE-BASED PRECIPITATION ESTIMATION OVER AWASH RIVER BASIN, ETHIOPIA.
Tsegaye Demsis Lemma, Ethiopian Space Science and Geospatial Institute (ESSGI), Ethiopia; Paolo Gamba, University of Pavia, Italy; Gizachew Kabate Wodajo, Ethiopian Space Science and Geospatial Institute, Ethiopia
MOP.P28.3: IN-CLOUD WIND AND RAIN OBSERVATION AT TIBET USING AIRBORNE DOPPLER SCATTORMETER
Wenbo Guo, Di Zhu, Zijin Zhang, Guoqing Xu, National Space Science Center, Chinese Academy of Sciences, China
MOP.P28.4: TOWARDS BIAS CORRECTION OF SATELLITE PRECIPITATION RETRIEVALS IN COMPLEX REGIONS WITH DEEP LEARNING: A CASE STUDY OVER TAIWAN
Liping Wang, Haonan Chen, Colorado State University, United States; Yun-lan Chen, Central Weather Bureau, Taipei, Taiwan, Taiwan; Pingping Xie, National Oceanic and Atmospheric Administration (NOAA), United States; Chia-Rong Chen, Central Weather Bureau, Taipei, Taiwan, United States; Tony Liao, National Oceanic and Atmospheric Administration (NOAA), United States
MOP.P28.5: MSF: One Lightweight Deep-learning Nowcasting Method with Attention Mechanism using Dual-Polarization Radar Observations
Kexin Zhu, Haonan Chen, Colorado State University, United States; Lei Han, Ocean University of China, China
MOP.P28.6: EVALUATING REFLECTIVITY QUALITY OF THE NEW AIRSHIP-BORNE WEATHER RADAR USING THE S-BAND GROUND-BASED WEATHER RADAR IN CHINA
Shuo Han, Xichao Dong, Beijing Institute of Technology, China; Xinpeng Chen, Beijing Institude of Technology, China; Fang Liu, Chongqing Meteorological Information Center, China; Lin Zhu, Institute of Technology Chongqing Innovation Center, China
MOP.P28.7: DETECTING SNOWFALL EVENTS USING SATELLITE-BASED DATA SOURCES
Kerttu Kouki, Emmihenna Jääskeläinen, Aku Riihelä, Finnish Meteorological Institute, Finland
MOP.P28.8: VERTICAL MULTI-LAYER CLOUD MASK RECONSTRUCTION WITH PHYSICS-INFORMED DEEP NEURAL NETWORKS
Yiding Wang, Leah Ding, American University, United States; Jie Gong, Dong Wu, NASA Goddard Space Flight Center, United States
MOP.P28.9: Thin-cirrus detection from Artificial Neural Network and IASI-NG
Elisabetta Ricciardelli, Francesco Di Paola, Domenico Cimini, Salvatore Larosa, National Research Council of Italy, Italy; Guido Masiello, Pietro Mastro, Carmine Serio, University of Basilicata, Italy; Tim Hultberg, Thomas August, European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Germany; Filomena Romano, National Research Council of Italy, Italy
MOP.P28.10: THE INITIAL CAPABILITY OF X-BAND POLARIMETRIC RADAR TO GAP-FILL A S-BAND AND C-BAND RADAR NETWORK FOR CONVECTIVE RAINBANDS IN EASTERN CHINA
Yabin Gou, Miao Zhou, Wanlin Kong, Hangzhou Meteorological Bureau, China; Haonan Chen, Colorado University, China
MOP.P28.11: A MACHINE LEARNING MODEL FOR RADAR QUANTITATIVE PRECIPITATION ESTIMATION IN BEIJING, CHINA
Ruiyang Zhou, Aofan Gong, Guangheng Ni, Tsinghua University, China
MOP.P28.12: CLOUDSEGNET: A DEEP LEARNING BASED SEGMENTATION METHOD FOR CLOUD DETECTION IN MULTISPECTRAL SATELLITE IMAGERY
Manoj Kaushik, ANAGHA S SARMA, Rama Rao Nidamanuri, Indian Institute of Space Science and Technology, India
Resources
No resources available.