Primary Convener:
Veljko Petković, University of Maryland College Park

Convener:
Pierre Kirstetter, University of Oklahoma Norman Campus
Shruti Upadhyaya, Indian Institute of Technology Hyderabad

Chair:
Veljko Petković, University of Maryland
Pierre Kirstetter, NOAA/National Severe Storms Laboratory
Shruti Upadhyaya, Indian Institute of Technology Hyderabad

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.

Index Terms
3354 Precipitation
1854 Precipitation
1855 Remote sensing
1942 Machine learning

Cross-Listed:
IN - Informatics
A - Atmospheric Sciences

Suggested Itineraries:
Machine Learning and AI

Co-Organized Sessions:
Atmospheric Sciences

Neighborhoods:
3. Earth Covering