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.

该研究项目将推进使用数学学科的技术，以最佳的方式结合理论，以使天气预报的Y进行抑制。在不同位置和不同的位置捕获这些领域的天气预测。研究团队将在过去24小时内供研究社区查看Atsostheric水分的变化。行为也将培训新的科学家学习最新的研究团队。模型的误差是Beinin模型的一致性，通常认为误差是对数的另一个概率密度函数数据同化方案完成后，该团队还将研究最大似然合奏的新整合版本，因为更浓厚的集合过滤器，以用于混合数据同化。将在网站的团队中展示不同的地点和高度，以供公众和预报员进行水分分配的变化。该奖项反映了NSF'SF'SFly fly Mission，并通过基金会的智力进行了评估，并获得了支持。优点和更广泛的影响审查标准。

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.