H084
Precipitation and Hydrometeorological Processes Through the Eyes of Machine Learning and Advanced Statistics
Session ID#: 249397
Session Description:
Precipitation and related hydrological processes are among the most complex manifestations of Earth’s hydrometeorological system. Their observation, understanding, retrieval, and modeling are inherently complex yet crucial for hydrological science and applications. Advanced approaches involving artificial intelligence (AI) and machine learning (ML) are increasingly employed to enhance the monitoring and modeling of these processes. This session welcomes contributions on the observation, understanding, retrieval, and modeling of precipitation and precipitation-induced hydrometeorological processes using advanced statistical and AI/ML approaches. Targeted applications and studies include, but are not limited to, precipitation remote sensing, extremes, associated hydrological responses, monitoring and uncertainty quantification, as well as analyses of precipitation system radiative, hydrometeor, and microphysical properties.
Co-Sponsor(s):
Index Terms:
1854 Precipitation [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
1942 Machine learning [INFORMATICS]
3354 Precipitation [ATMOSPHERIC PROCESSES]
Primary Convener: Dr. Veljko Petković, University of Maryland College Park, College Park, MD, United States
Conveners: Pierre Kirstetter, University of Oklahoma Norman Campus, School of Meteorology and School of Civil Engineering and Environmental Science, Norman, United States and Shruti Upadhyaya, Indian Institute of Technology Hyderabad, Department of Civil Engineering, Hyderabad, India
Student/Early Career Convener: Shruti Upadhyaya, Indian Institute of Technology Hyderabad, Department of Civil Engineering, Hyderabad, India
Evaluation and Improvement of Precipitation Products in Arizona Supporting Water Resources Planning and Management (1865581)
Binaya Parajuli, Arizona State University, Tempe, AZ, United States, Giuseppe Mascaro, Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, AZ, United States and Abdul Moiz, Scripps Institution of Oceanography, San Diego, United States
Advancing Satellite-Based Precipitation Phase Classification with Deep and Bayesian Neural Networks (1867934)
Dr. Veljko Petković1, Marko Orescanin2, Barret Manfre2, Dalton Duvio3, Lei Ji4, Ralph Ferraro4 and Huan Meng5, (1)Cooperative Institute for Climate and Satellites University of Maryland, Earth System Science Interdisciplinary Center (ESSIC), College Park, United States, (2)Naval Postgraduate School, Computer Science Department, Monterey, United States, (3)Naval Postgraduate School, Monterey, CA, Computer Science Department, Monterey, United States, (4)University of Maryland College Park, College Park, United States, (5)Natl Oceanic & Atmospheric Adm, College Park, United States
Regional Kilometer-Scale Data-Driven Precipitation Forecasting over the Western United States Using Artificial Intelligence (AI) (1888321)
Nazak Rouzegari1, Vesta Afzali Gorooh2, Phu Nguyen2, Kuo-lin Hsu2, Soroosh Sorooshian2, Agniv Sengupta3 and Luca Delle Monache3, (1)University of California Irvine, Civil and Environmental Engineering, Irvine, CA, United States, (2)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States, (3)Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, UC San Diego, La Jolla, United States
An Incremental-Learning Framework for Artifact Detection in Precipitation Data. (1900494)
Andres Monsalve, University of Texas at El Paso, El Paso, TX, United States, Hernan A Moreno, University of Texas at El Paso, Earth, Environmental and Resource Sciences, El Paso, TX, United States and Christian D Kummerow, Colorado State University, Department of Atmospheric Science, Fort Collins, United States
Explainable transformer-based deep learning improves hourly precipitation estimates in observation-sparse regions (1928994)
Fang Wang, Auburn University, Crop, Soil and Environmental Sciences, Auburn, AL, United States and Di Tian, Auburn University, Department of Crop, Soil & Environmental Sciences, Auburn, United States
A Machine Learning Approach to Quantifying the Error of Airborne Radio Occultation in Atmospheric Rivers (1931033)
Noah Barton, Scripps Institution of Oceanography, La Jolla, United States, Jennifer S Haase, Scripps Institution of Oceanography, UC San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States and Bing Cao, Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States
A Global High-Resolution Convective-Stratiform Precipitation Type Dataset from IR Observations (1956254)
Daniel Watters, University of Oklahoma, Advanced Radar Research Center, Norman, United States, Pierre-Emmanuel Kirstetter, University of Oklahoma, School of Meteorology and School of Civil Engineering and Environmental Science, Norman, United States and Jackson Tan, University of Maryland Baltimore County, Baltimore, MD, United States; NASA Goddard Space Flight Center, Greenbelt, MD, United States
