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

点击翻译按钮获取中文摘要

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.