CAREER: Score-Based Diffusion Models for Probabilistic Forecasting of Weather and Climate

该项目将开发机器学习方法，以估计不利天气和气候事件的风险。估计高维数据中罕见事件发生的概率是数据科学中的一个基本问题。生成性人工智能的进展将被用来开发新的数据驱动的计算方法来建模风险，并将这些方法应用于天气应用。特别是，这些模型将被应用于预测太阳辐射和降水，这两个应用对夏威夷等热带岛屿尤为重要。估计太阳能发电(坡道事件)快速变化的风险对于管理能源电网是必要的，这些电网正在经历各种可再生能源的快速增长，仅在美国，洪水每年就夺走了数百人的生命和数十亿美元的财产损失。这项研究将与一个教育推广项目相结合，该项目包括一个面向高中生的暑期数据科学课程，以及一个与当地K-12教师分享数据科学教材的研讨会。生成性人工智能方法在文本到图像模型、图像超分辨率和视频预测方面取得了快速发展。关键的发展是用于学习图像和视频等高维数据上的联合概率分布的神经网络模型。这些模型包括基于分数的扩散模型，它解决了以前的生成模型的许多局限性，包括其他基于流的模型、自回归模型、变分自动编码器和生成性对抗网络。这个项目将研究和改进基于分数的扩散模型从有限的训练数据中有效地学习罕见事件的概率的能力。太阳辐照度和降水预报的应用将作为激励案例研究，因为这些问题需要对时空天气数据的联合分布进行建模。实验将利用大气变量数值模拟的现有数据以及卫星和地面传感器网络的观测数据。该项目开发的机器学习方法将通过提供计算速度、时空分辨率和概率预测精度更高的特定位置预测来补充现有的基于物理的数值天气预测模型。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 project will develop machine learning methods for estimating the risk of adverse weather and climate events. Estimating the probability of rare events in high-dimensional data is a fundamental problem in data science. Advances in generative artificial intelligence will be used to develop new data-driven computational methods for modeling risk and apply these methods to weather applications. In particular, these models will be applied to forecasting solar irradiance and precipitation, two applications that are particularly important for tropical islands such as Hawaii. Estimating the risk of rapid changes in solar power generation (ramp events) is necessary for managing energy grids that are seeing a rapid increase in variable renewable sources, and floods claim hundreds of lives and billions in property damage each year in the United States alone. This research will be paired with an educational outreach program that includes a summer data science course for high school students and a workshop to share data science teaching materials with local K-12 teachers.Generative artificial intelligence methods have led to rapid progress in text-to-image models, image super-resolution, and video prediction. The key development is in the neural network models used to learn joint probability distributions over high-dimensional data such as images and video. These include score-based diffusion models, which solve many of the limitations of previous generative models including other flow-based models, autoregressive models, variational autoencoders, and generative adversarial networks. This project will investigate and improve the ability of score-based diffusion models to efficiently learn the probability of rare events from finite training data. Applications to solar irradiance and precipitation forecasting will serve as motivating case-studies, as these problems require modeling the joint distributions over spatiotemporal weather data. Experiments will leverage existing data from numerical simulations of atmospheric variables and observations from satellites and ground-based sensor networks. The machine learning methods developed by this project will complement existing physics-based numerical weather prediction models by providing location-specific forecasts with increased computational speed, spatiotemporal resolution, and probabilistic prediction accuracy.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.