CAREER: Artificial Intelligence for Polarimetric Radar Remote Sensing of Precipitation

Colorado State University, Fort Collins CO

Investigators

Abstract

Polarimetric Doppler radars have been the most important remote sensing instrument for observing clouds and precipitation, serving as cornerstones of the national severe weather monitoring and forecast infrastructure. However, state-of-the-art approaches have only been able to extract part of the precipitation information from the multi-dimensional polarimetric radar data. This research will open new horizons for radar remote sensing of precipitation through developing explainable artificial intelligence (AI) techniques which can extract the rich information from radar data to improve the understanding of complex precipitation processes and atmospheric dynamics in different precipitation environments. The AI methods developed from this project can potentially be applied to the nationwide operational weather radars and many research radars in the United States and worldwide, thus can advance weather, water, and climate science and service. In addition to the training of undergraduate and graduate students, the planned educational activities are spread across multiple high schools in Northern Colorado, with instruction and field trips aimed at introducing high school students to weather observations and AI applications and instilling a desire to pursue STEM careers. The integration of research and educational activities can foster the next generation of scientists who will be familiar with both AI and meteorology fields. To achieve the scientific goals, this project focuses on: 1) investigating physics-guided AI models for hydrometeor identification and precipitation microphysics retrievals from polarimetric radar observations; 2) designing a dense convolutional neural network framework that has high generalization capability for radar-based quantitative precipitation estimation; 3) developing an interpretable AI model for precipitation nowcasting and investigating the controlling factors on storm initiation, growth and decay, which are poorly understood. This research will be accomplished through field data collection, model- and data-centric deep learning, and interpretation of the deep learning results. This research will enhance the ability to estimate and predict severe storms, which will lead to improved situational awareness of extreme weather events, improved decision making, and better public safety. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.