H132
Space-Based Precipitation Observations: Innovations for Science and Applications
Session ID#: 249681
Session Description:
Global precipitation observations are vital for investigating processes that impact extreme weather, hydrologic forecasts, and infrastructure. The TRMM and GPM missions have set standards for remote sensing of global precipitation and applications, and future international agency and commercial missions will further advance our observational record and understanding of physical processes. Advancing precipitation science requires complementary cloud-precipitation observations from spaceborne and suborbital platforms as well as model-observation synergy to document detailed interaction processes, small spatial scales, and poorly resolved short temporal components. Emerging concerns in global precipitation observations include increasing reliance on high-frequency microwave channels in planned satellite missions and the undetermined contribution of smallsats to an ever-evolving precipitation constellation. This session invites innovative contributions in precipitation science and applications with emphasis on the use of space-based observations, including missions, instrumentation, algorithms, quantitative precipitation estimates, uncertainty characterization, and validation, extending to interdisciplinary work on extremes, processes, and models.
Co-Sponsor(s):
Index Terms:
1854 Precipitation [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
Primary Convener: Sarah Ringerud, NASA Goddard Space Flight Center, Greenbelt, United States
Conveners: George John Huffman, NASA Goddard Space Flight Center, Greenbelt, United States, Yagmur Derin, University of Wisconsin–Madison, Madison, United States and Daniel Watters, University of Oklahoma, Advanced Radar Research Center, Norman, United States
Transfer Learning of Global Precipitation from Reanalysis Data to Satellite Observations (1850100)
Buddha Subedi1, Mahyar Garshasbi1, Ardeshir Ebtehaj1, Linda Bogerd2,3, Lisa Milani2,3, Nobuyuki Utsumi4 and George John Huffman2, (1)Saint Anthony Falls Laboratory, Department of Civil, Environmental and Geo-Engineering, University of Minnesota, Minneapolis, United States, (2)NASA Goddard Space Flight Center, Greenbelt, United States, (3)University of Maryland College Park, Earth System Science Interdisciplinary Center (ESSIC), College Park, United States, (4)Kyoto Universty of Advanced Science, Kyoto, Japan
A Novel Hybrid CNN-LSTM Approach to Dynamically Parameterize the Soil Water Balance for Improved and Self-Calibration of Global Rainfall Estimation (1851317)
Mohammad Saeedi1, Ziyue Zhu1, Hyunglok Kim2, John D Bolten3, Michael H Cosh4 and Venkataraman (Venkat) Lakshmi1, (1)University of Virginia, Civil and Environmental Engineering, Charlottesville, United States, (2)Gwangju Institute of Science and Technology (GIST), School of Environment and Energy Engineering, Gwangju, South Korea, (3)Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, United States, (4)U. S. Department of Agriculture, Beltsville, United States
IMERG V08 and Thoughts for the Next Generation of Global Precipitation Products (1864714)
Robert Joyce1, George John Huffman2, David T Bolvin3, Eric Nelkin3 and Jackson Tan4, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, United States, (3)Science Systems and Applications, Inc., Lanham, United States, (4)Monash University, Melbourne, Australia
Snowfall Regime Classification: Application of a Machine Learning Classifier to Passive Microwave Observations. (1892339)
Lisa Milani, University of Maryland College Park, Earth System Science Interdisciplinary Center (ESSIC), College Park, United States; NASA Goddard Space Flight Center, Greenbelt, United States and Dr. Veljko Petković, University of Maryland, College Park, ESSIC/CISESS, College Park, United States
Characterizing vertical motion in global precipitation using EarthCARE–GPM coincidence dataset (1904451)
Shunsuke Aoki1, Takuji Kubota1 and Joe J Turk2, (1)Japan Aerospace Exploration Agency, Earth Observation Research Center, Tsukuba, Japan, (2)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Point Rainfall Extremes from an Ensemble of Coarse-scale Satellite and Reanalysis Precipitation Estimates: Application and Testing in Northeastern Italy (1908538)
César Arturo Sánchez Peña, University of Padova, Padova, Italy, Francesco Marra, Department of Geoscience, University of Padova, Italy, Legnaro (PD), Italy and Marco Marani, University of Padova, Department of Civil, Environmental and Architectural Engineering, Padova, Italy
Harmonizing Global Geostationary Imagery with Deep Learning for Near Real-Time Precipitation Tracking (1919167)
Yeji Choi1, Hyungon Ryu2, Kwang-Min Myung1, Seongryeong Choi1, Jiu Park1 and Yong-Jae Moon3, (1)DI Lab Inc., Seoul, South Korea, (2)NVIDIA, Seoul, South Korea, (3)School of Space Research, Kyung Hee University, Yongin, South Korea
PUnet-CDR: A Deep Learning Framework for Reconstructing High-Resolution Global Precipitation Climate Data Records (1930935)
Phu Nguyen1, Vu Dao2, Tu Ung3, Mohammad bolboli Zadeh2, Claudia Jimenez Arellano4, Nazak Rouzegari2, Kuo-lin Hsu4, Amir AghaKouchak4, Soroosh Sorooshian4 and F Martin Ralph5, (1)University of California Irvine, Irvine, CA, United States, (2)University of California Irvine, Civil and Environmental Engineering, Irvine, CA, United States, (3)University of California Irvine, Civil and Environmental Engineering, Irvine, United States, (4)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States, (5)Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, UC San Diego, La Jolla, United States
A New Approach to Identifying and Analyzing Precipitation Events and Their Typical Lifecycles over Conterminous United States (1933675)
Siyu Zhu1, Guoqiang Tang2, Songkun Yan1, Yu Du3, Yue Xu4, Mofan Zhang5, Mengye Chen6, Huan Li7 and Yang Hong1, (1)University of Oklahoma, School of Civil Engineering and Environmental Science, Norman, United States, (2)Wuhan University, School of Water Resources and Hydropower Engineering, Wuhan, China, (3)Sun Yat-Sen University, School of Atmospheric Sciences, Zhuhai, China, (4)Peking University, Beijing, China, (5)Stanford University, Stanford, CA, United States, (6)The University of Oklahoma, Center for Analysis and Prediction of Storms, Norman, United States, (7)Tsinghua University, Department of Hydraulic Engineering, Beijing, China
Enhance satellite-based precipitation product through diffusion models in ungauged regions (1938338)
Kaidi Peng1,2, Hayato Ushijima Mwesigwa1, Ting-Yu Dai3 and Jackson Tan4, (1)Fujitsu Research of America, San Jose, United States, (2)University of Wisconsin Madison, Madison, United States, (3)University of Texas at Austin, Austin, TX, United States, (4)University of Maryland Baltimore County, Baltimore, MD, United States
The Newly Released GPCP V3.3 Daily and Monthly Products and the Future Plans (1940634)
Ali Behrangi1, George John Huffman2, Robert F Adler3, Yang Song, PhD1, Kwabena Kingsley Kumah4, David T Bolvin5, Eric Nelkin5 and Guojun Gu6, (1)University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (2)NASA Goddard Space Flight Center, Greenbelt, United States, (3)University of Maryland, College Park, MD, United States, (4)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, United States, (5)Science Systems and Applications, Inc., Lanham, United States, (6)ESSIC, University of Maryland College Park, College Park, MD, United States
Rmerger: An Open-Source Framework for Multi-Source Precipitation Data Merging (1941828)
Suraj Shah1, Yi Liu2, Seokhyeon Kim3 and Ashish Sharma1, (1)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (2)University of New South Wales, School of Civil and Environmental Engineering, Sydney, Australia, (3)Kyung Hee University, Department of Civil Engineering, Yongin, South Korea
Validation of the Global Precipitation Measurement Mission (GPM) Data: Issues and Insights (1954414)
Yuri Gorokhovich1, Shawn Blackman1, Caleb Adjei2, Vivian Chavez1, Ram Ray3 and Gebrekidan Tefera3, (1)CUNY Lehman College, Earth, Environmental and Geospatial Sciences, Bronx, United States, (2)CUNY Lehman College, Computer Science, Bronx, United States, (3)Prairie View A&M University, Cooperative Agricultural Research Center, College of Agriculture, Food and Natural Resources, Prairie View, United States
Evaluating Passive Microwave Precipitation over Complex Terrain: Insights from Multi-Frequency, Multi-Sensor Satellite-based Observations (Invited) (1988315)
Malarvizhi Arulraj1, Dr. Veljko Petković2, Lei Ji3, Saisidharth Seyyadri3, Huan Meng4 and Ralph Ferraro3, (1)University of Maryland, Earth System Science Interdisciplinary Center (ESSIC) / CISESS, College Park, MD, United States, (2)University of Maryland, Earth System Science Interdisciplinary Center (ESSIC), College Park, United States, (3)University of Maryland College Park, College Park, United States, (4)Natl Oceanic & Atmospheric Adm, College Park, United States
University of Arizona – High-latitude Infrared-based Precipitation Analysis Version 2 (UA-HIPA) (1992147)
Omid Zandi, University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States, Simon Pfreundschuh, Colorado State University, Fort Collins, United States, Kwabena Kingsley Kumah, University of Arizona, Hydrology and Atmospheric Sciences, Tucson, United States and Ali Behrangi, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, United States
