Improving Weather Forecasting through non-Gaussian Data Assimilation with Machine Learning

通过机器学习的非高斯数据同化改进天气预报

• 批准号: 2033405
• 负责人: Steven Fletcher
• 金额: $ 58.25万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033405&HistoricalAwards=false
• 关键词: Improving; Weather; Forecasting; through; non

The research project is to advance techniques for using a mathematical discipline to optimally combine theory with observations to improve the accuracy of weather forecasts. The team will use different forms of machine learning mechanism to detect changes in the behavior of moisture fields in the atmosphere such that the new techniques are able to change parts of the weather prediction scheme to better capture these fields in different locations and at different times. To achieve the research goal, a large amount of observations and model results is required to train computers to detect moisture changes. The research will investigate how much data are needed to reliably detect changes through the machine learning techniques. As part of this research project, the research team will develop a website for the research community to view atmospheric moisture changes in the past 24 hours. This research will also test how well a new component of the weather prediction scheme works when the machine learning techniques have detected moisture changes from its normal behavior. The project will also involve training a new scientist to learn the latest research method. The research team will investigate the ability of machine learning techniques to detect changes away from Gaussian behavior for the moisture fields and to be capable to switch the cost function in variational data assimilation between Gaussian and non-Gaussian. The scheme is important to ensure that the model-observation errors are being model consistently. The error changes are commonly assumed to be toward lognormal; recent work has indicated that the behavior of moisture fields has another probability density function—the reverse lognormal. This distribution has a right skewness and enables analysis to increase the moisture state if the background is too dry. Using the proper type of error distribution schemes will aid not only cloud prediction but also cloud retention in forecast models after the data assimilation scheme has finished. This team will also investigate a new ensemble smoother, as well as non-Gaussian versions of the Maximum Likelihood Ensemble Filter as a more consistent ensemble filter for hybrid data assimilation schemes, especially for the lognormal and reverse lognormal behavior. In addition, the skewness of the moisture field at different locations and heights will be displayed at the team’s website for the general public and forecasters to view how the moisture distribution is changing.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.

该研究项目是为了推进使用数学学科的技术，以最佳的理论与观测值结合，以提高天气预报的准确性。该团队将使用不同形式的机器学习机制来检测大气中水分场行为的变化，从而使新技术能够更改天气预测方案的一部分，以更好地在不同位置和不同时间捕获这些领域。为了实现研究目标，需要大量观察结果和模型结果才能训练计算机以检测水分的变化。该研究将研究需要多少数据来可靠地通过机器学习技术检测变化。作为该研究项目的一部分，研究团队将为研究界开发一个网站，以查看过去24小时内大气水分的变化。这项研究还将测试当机器学习技术检测到水分从其正常行为变化时，天气预测方案的新组成部分的工作状况如何。该项目还将涉及培训新科学家学习最新的研究方法。研究团队将研究机器学习技术检测到水分领域高斯行为的变化的能力，并能够在高斯和非高斯之间的变异数据同化中切换成本函数。该方案对于确保模型观察误差始终如一地模型很重要。误差变化通常被认为是对数正态的。最近的工作表明，水分场的行为具有另一个概率密度函数 - 反向对数正态。该分布具有正确的偏度，如果背景太干，可以分析可以增加水分状态。在数据同化方案完成后，使用适当的错误分布方案不仅有助于云预测，还可以帮助预测模型中的云保留。该团队还将调查最大似然集合过滤器的新的合奏更加顺畅，以及非高斯版本，作为混合数据同化方案的更一致的集合过滤器，尤其是对于数字正态和反向logNormoral的行为。此外，在团队的网站上，将向公众和预报员展示水分场在不同位置和高度的偏度，以查看水分分布如何发生变化。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查审查标准来通过评估来诚实地诚实地支持。

项目成果

Lognormal and Mixed Gaussian–Lognormal Kalman Filters

对数正态和混合高斯 - 对数正态卡尔曼滤波器

• DOI: 10.1175/mwr-d-22-0072.1
• 发表时间: 2023
• 期刊: Monthly Weather Review
• 影响因子: 3.2
• 作者: Fletcher, Steven J.;Zupanski, Milija;Goodliff, Michael R.;Kliewer, Anton J.;Jones, Andrew S.;Forsythe, John M.;Wu, Ting-Chi;Hossen, Md. Jakir;Van Loon, Senne
• 通讯作者: Van Loon, Senne

Non‐Gaussian Detection Using Machine Learning With Data Assimilation Applications

使用机器学习和数据同化应用进行非高斯检测

• DOI: 10.1029/2021ea001908
• 发表时间: 2022
• 期刊: Earth and Space Science
• 影响因子: 3.1
• 作者: Goodliff, Michael R.;Fletcher, Steven J.;Kliewer, Anton J.;Jones, Andrew S.;Forsythe, John M.
• 通讯作者: Forsythe, John M.