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部的季风雷暴活动和降水，该项目创建了一种创新的机器学习工具，该工具使用区域数值天气模型输出和卫星遥感数据（预测变量）根据已知的雷暴单元位置和强度进行训练。该工具旨在提取预测变量和目标之间的重要基本关系，以帮助解释雷暴的发展和演变。经过密集的训练、验证和测试阶段，这些关系将被用来生成更好的结果。的预测该工具将与国家气象局共享，以帮助预报员预测与雷暴相关的灾害，例如大冰雹、山洪或野火。这种创新方法还将提供一个框架。用于改进业务气象和地球物理预测系统并指导科学领域研究。该项目开发了一种概率模型来预测北美季风（NAM）潮湿阶段的对流起始、降雨率和对流单元轨迹。对流预测因子（例如相对湿度、对流可用势能、可降水量）将从动态中尺度模型（高分辨率快速刷新、亚利桑那大学天气研究预测模型）分析和预报中收集，并与新的卫星观测结果相结合土壤湿度和地表温度，以产生一种独特的预测工具——因果关系学习——将用于确定最合适的预测因子，以便在神经网络中进行进一步训练并深入了解该过程。每小时对降水发生、性质和分类降雨率的预测将在操作上反映指导预报员和实地研究。该奖项的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持。审查标准。