Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting

虽然在过去几十年中，天气预报在几天到大约一周的时间尺度上取得了重大进展，但在亚季节时间尺度上对温度和降水等关键气候变量进行高质量的预报，时间范围在2周到2个月之间，继续挑战业务预报员。在农业生产力、水文和水资源管理、运输和航空系统以及大西洋飓风和中西部龙卷风等极端事件的应急规划等领域，熟练的亚季节时间尺度气候预报将具有巨大的社会价值。尽管亚季节气候预测在科学、社会和经济上都很重要，但在这一问题上的进展却很有限。该项目启动了基于物理学的机器学习的系统研究，重点是推进亚季节气候预测。特别是，该项目正在开发新的机器学习（ML）方法，通过利用有限的观测数据以及大量的动态气候模型输出数据进行亚季节预测。此外，该项目的重点是改进基于ML的动态气候模型本身，特别强调学习适合准确的亚季节预测的模型参数化。该项目中开发的基于物理的机器学习的原理、模型和方法将有利于依赖动态模型的其他科学领域。该项目正在建立一个亚季节预测基准数据集的公共存储库，以吸引更广泛的数据科学界参与，并加快这一关键领域的进展。该项目正在培养新一代跨学科科学家，他们可以跨越计算机科学，统计学和气候科学之间的传统界限。该项目使用两个关键数据来源进行亚季节预测：有限数量的观测数据和来自动态模型模拟的大量输出数据，动态模型模拟基于偏微分方程（PDE）的大型耦合系统捕获物理定律和动态。该项目正在调查以下中心问题：从有限的观测数据和不完善的动力学模型中同时学习以改进次季节预报的最佳方式是什么？该项目正在为基于物理的机器构建一个框架，该框架具有两个相互关联的组件：（1）演绎，其中ML模型在动态模型输出以及有限的观察结果上进行训练，以及（2）归纳，其中ML模型用于改进动态模型。在这两个组成部分中，该项目在学习表示、函数梯度下降、迁移学习、无导数优化和多臂强盗、蒙特卡洛树搜索和块坐标下降方面取得了根本性的进展。在气候方面，该项目正在建立一个理想化的动力气候模型，并深入研究如何使用ML方法学习动力模型的合适参数，以提高亚季节时间尺度的预测精度。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分。该奖项反映了NSF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。

While the past few decades have seen major advances in weather forecasting on time scales of days to about a week, making high quality forecasts of key climate variables such as temperature and precipitation on sub-seasonal time scales, the time range between 2 weeks and 2 months, continues to challenge operational forecasters. Skillful climate forecasts on sub-seasonal time scales would have immense societal value in areas such as agricultural productivity, hydrology and water resource management, transportation and aviation systems, and emergency planning for extreme events such as Atlantic hurricanes and midwestern tornadoes. In spite of the scientific, societal, and financial importance of sub-seasonal climate forecasting, progress on the problem has been limited. The project has initiated a systematic investigation of physics-based machine learning with specific focus on advancing sub-seasonal climate forecasting. In particular, this project is developing novel machine learning (ML) approaches for sub-seasonal forecasting by leveraging both limited observational data as well as vast amounts of dynamical climate model output data. Further, the project is focusing on improving the dynamical climate models themselves based on ML with specific emphasis on learning model parameterizations suitable for accurate sub-seasonal forecasting. The principles, models, and methodology for physics-based machine learning being developed in the project will benefit other scientific domains which rely on dynamical models. The project is establishing a public repository of a benchmark dataset for sub-seasonal forecasting to engage the wider data science community and accelerate progress in this critical area. The project is training a new generation of interdisciplinary scientists who can cross the traditional boundaries between computer science, statistics, and climate science.The project works with two key sources of data for sub-seasonal forecasting: limited amounts of observational data and vast amounts of output data from dynamical model simulations, which capture physical laws and dynamics based on large coupled systems of partial differential equations (PDEs). The project is investigating the following central question: what is the best way to learn simultaneously from limited observational data and imperfect dynamical models for improving sub-seasonal forecasts? The project is building a framework for physics-based machine that has two inter-linked components: (1) deduction, in which ML models are trained on dynamical model outputs as well as limited observations, and (2) induction, in which ML models are used to improve dynamical models. Across the two components, the project is making fundamental advances in learning representations, functional gradient descent, transfer learning, derivative-free optimization and multi-armed bandits, Monte Carlo tree search, and block coordinate descent. On the climate side, the project is building an idealized dynamical climate model and doing an in depth investigation on learning suitable parameterizations for the dynamical model with ML methods to improve forecast accuracy in the sub-seasonal time scales. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.