EAGER: North American Monsoon Prediction Using Causality Informed Machine Learning

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，利用区域数值天气模式输出和卫星遥感数据(预报器)，就雷达(目标)探测到的已知雷暴单元位置和强度进行了培训。该工具将被设计用于提取预报器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。经过紧张的培训、验证和测试阶段后，这些关系将被用来产生对未来西南部雷暴事件的时间、严重性和地点的更好预测。该工具将与美国国家气象局共享，以帮助天气预报员预测与雷暴相关的灾害，如大冰雹、山洪暴发或野火点火。这一创新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来预测北美季风(NAM)湿润阶段的对流起始、降雨率和对流单体路径。对流预报因子(如相对湿度、对流有效势能、可降水量)将从动态中尺度模式(高分辨率快速更新，亚利桑那大学-天气研究预报模式)的分析和预报中收集，并与新的卫星土壤水分和地表温度观测相结合，产生一个独特的预报工具。一种新的机器学习方法--因果信息学习--将被应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的训练，并深入了解控制对流发起和演化的过程。每小时对降水发生、性质和绝对降雨率的预报将用于指导预报员和实地研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.