EAGER: North American Monsoon Prediction Using Causality Informed Machine Learning

EAGER：使用因果关系信息机器学习来预测北美季风

• 批准号: 2313689
• 负责人: Christopher Hennon
• 金额: $ 16.25万
• 依托单位: Embry-Riddle Aeronautical University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313689&HistoricalAwards=false
• 关键词: EAGER; North; American; Monsoon; Prediction

This research seeks better understanding of monsoon thunderstorm activity and precipitation in the Southwest. The project creates an innovative machine learning tool trained using regional numerical weather model output and satellite remote sensing data (the predictors) with respect to known thunderstorm cell locations and intensities detected by radar (the targets). The tool will be designed to extract important fundamental relationships between the predictors and targets that help explain the development and evolution of thunderstorms. After an intense training, validation and testing phase, the relationships will then be leveraged to generate better forecasts of the timing, severity and location of future thunderstorm events in the Southwest. The tool will be shared with the National Weather Service to help forecasters predict thunderstorm-related hazards such as large hail, flash flooding or wildfire ignition. This innovative approach will also provide a framework for improving operational meteorological and geophysical prediction systems and for guiding scientific field studies.The project develops a probabilistic model to predict convective initiation, rain rates, and convective cell tracks during the wet phase of the North American Monsoon (NAM). Predictors of convection (e.g., relative humidity, convective available potential energy, precipitable water) will be collected from dynamic mesoscale model (High Resolution Rapid Refresh, University of Arizona-Weather Research Forecast model) analyses and forecasts and combined with new satellite-derived observations of soil moisture and surface temperature to produce a unique prediction tool. A novel machine learning approach – causality informed learning – will be applied to identify the most suitable predictors for further training in a neural network and to gain insight into the processes governing convective initiation and evolution. Hourly forecasts of precipitation occurrence, nature, and categorical rain rates will be produced operationally to guide forecasters and field research.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.

这项研究旨在更好地了解西南地区的季风雷暴活动和降水。该项目创建了一个创新的机器学习工具，使用区域数值天气模式输出和卫星遥感数据（预测者）进行训练，这些数据与雷达探测到的已知雷暴单元位置和强度（目标）有关。该工具将被设计用于提取预测器和目标之间重要的基本关系，这有助于解释雷暴的发展和演变。经过密集的训练、验证和测试阶段后，这些关系将用于更好地预测西南地区未来雷暴事件的时间、严重程度和位置。该工具将与国家气象局共享，以帮助预报员预测雷暴相关的危害，如大冰雹、山洪暴发或野火点燃。这种创新的方法还将为改进业务气象和地球物理预报系统以及指导科学实地研究提供一个框架。该项目开发了一个概率模型，用于预测北美季风（NAM）湿期对流启动、降雨率和对流单体轨迹。对流预报因子（例如，相对湿度、对流有效势能、可降水量）将从动态中尺度模式（高分辨率快速刷新，亚利桑那大学天气研究预报模式）分析和预报中收集，并结合新的卫星衍生的土壤湿度和地表温度观测，以产生一种独特的预测工具。一种新的机器学习方法——因果关系信息学习——将被应用于识别最合适的预测因子，用于神经网络的进一步训练，并深入了解控制对流开始和进化的过程。每小时预报降水的发生、性质和分类降雨率，以指导预报员和实地研究。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。