CAREER: Artificial Intelligence for Polarimetric Radar Remote Sensing of Precipitation

职业：用于降水偏振雷达遥感的人工智能

• 批准号: 2239880
• 负责人: Haonan Chen
• 金额: $ 64.56万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239880&HistoricalAwards=false
• 关键词: CAREER; Artificial; Intelligence; Polarimetric; Radar

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.

极化多普勒雷达是观测云和降水最重要的遥感仪器，是国家灾害性天气监测预报基础设施的基石。然而，最先进的方法只能从多维极化雷达数据中提取部分降水信息。本研究将通过开发可解释的人工智能（AI）技术，从雷达数据中提取丰富的信息，提高对不同降水环境下复杂降水过程和大气动力学的理解，为雷达遥感降水开辟新的视野。该项目开发的人工智能方法可以潜在地应用于美国和世界各地的全国性业务气象雷达和许多研究雷达，从而可以推进天气、水和气候科学和服务。除了对本科生和研究生的培训外，计划中的教育活动还分布在科罗拉多州北部的多所高中，通过指导和实地考察，旨在向高中生介绍天气观测和人工智能应用，并灌输追求STEM职业的愿望。研究和教育活动的结合可以培养熟悉人工智能和气象领域的下一代科学家。为实现科学目标，本项目重点研究：1)研究基于物理导向的人工智能模型，用于极化雷达观测水文气象识别和降水微物理反演；2)设计了一种具有高泛化能力的稠密卷积神经网络框架，用于基于雷达的降水定量估计；3)建立可解释的降水近预报人工智能模型，研究目前尚不清楚的风暴发生、生长和衰减的控制因素。本研究将通过现场数据收集、以模型和数据为中心的深度学习以及对深度学习结果的解释来完成。这项研究将提高估计和预测强风暴的能力，从而提高对极端天气事件的态势感知，改进决策，改善公共安全。该项目由地球科学理事会共同资助，旨在支持人工智能/机器学习在地球科学领域的发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。