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

合作研究：基于物理的机器学习用于次季节气候预测

基本信息

批准号：
1934584
负责人：
Pradeep Ravikumar
金额：
$ 29.73万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934584&HistoricalAwards=false
关键词：
Collaborative Research Physics Based Machine

项目摘要

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.

尽管过去几十年来，天气尺度的天气预测在天气尺度上达到了大约一周的重大进展，从而对关键气候变量进行高质量预测，例如温度和季节性时间尺度上的降水量，但在2周和2个月之间的时间范围，继续挑战运营预报员。在农业生产力，水文学和水资源管理，运输和航空系统等领域，对亚地区时间尺度的熟练气候预测将具有巨大的社会价值，以及大西洋飓风和中西部龙卷风等极端活动的紧急计划。尽管下季节气候预测的科学，社会和财务重要性，但问题的进展仍然有限。该项目已经对基于物理的机器学习进行了系统的研究，特别着眼于推进季节性气候预测。特别是，该项目通过利用有限的观察数据以及大量动态气候模型输出数据来开发新的机器学习方法（ML）方法，以进行下季预测。此外，该项目将重点放在基于ML的动态气候模型本身上，并特别强调学习模型参数化，适合于准确的亚季节预测。项目中开发基于物理的机器学习的原理，模型和方法将使其他依赖动态模型的科学领域受益。该项目正在建立一个基准数据集的公共存储库，以进行亚季节预测，以吸引更广泛的数据科学界并加速该关键领域的进展。该项目正在培训新一代的跨学科科学家，他们可以跨越计算机科学，统计和气候科学之间的传统界限。该项目可与两个关键的数据源一起使用，用于次级季节预测的两个关键来源：有限的观测数据和大量的动态模型中的大量输出数据，这些模型仿真，捕获基于大型物理偏差的系统元素的系统（pdepledepations aptial difectialsepations）（pdeplessials aptial dientals）。该项目正在研究以下中心问题：从有限的观察数据和不完美的动态模型中同时学习以改善次季预测的最佳方法是什么？该项目正在为基于物理的机器构建一个框架，该机器具有两个相互关联的组件：（1）推论，其中ML模型在动态模型输出以及有限的观察结果上进行培训，以及（2）使用ML模型来改善动力学模型。在这两个组件中，该项目在学习表示，功能梯度下降，转移学习，无衍生化的优化和多臂匪徒，蒙特卡洛树搜索和块坐标下降方面取得了根本的进步。在气候方面，该项目正在建立一个理想化的动力学气候模型，并对使用ML方法对动态模型进行合适的参数化进行深入研究，以提高亚季节时间尺度的预测准确性。该项目是国家科学基金会利用数据革命（HDR）的大创意活动的一部分。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。