资源环境科技发展态势分析平台

An important assumption made in most variational, ensemble, and hybrid-based data assimilation systems is that all minimized errors are Gaussian random variables. A theory developed at the Cooperative Institute for Research in the Atmosphere enables for the Gaussian assumption for the different types of errors to be relaxed to a lognormally distributed random variable. While this is a first step toward using more consistent distributions to model the errors involved in numerical weather/ocean prediction, we still need to be able to identify when we need to assign a lognormal distribution in a mixed Gaussian-lognormal approach. In this paper, we present some machine learning techniques and experiments with the Lorenz 63 model. Using these machine learning techniques, we show detection of non-Gaussian distributions can be done using two methods: a support vector machine and a neural network. This is done by training past data to classify (1) differences with the distribution statistics (means and modes) and (2) the skewness of the probability density function.