Non-Gaussian Detection using Machine Learning for Data Assimilation Applications
Abstract
An important assumption that is made in most variational, ensemble and hybrid based data assimilation systems is that all errors that are minimised are Gaussian random variables. There has been theory developed at CIRA that 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 towards 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 presentation, we present some machine learning techniques that have been used on the Lorenz 63 model to identify changes to the probability distribution functions. This distribution is then used to determine which version of the 3D/4DVar (Gaussian or Log-Normal based) should be used for assimilation to increase prediction accuracy.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMNG21B0922G
- Keywords:
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- 3315 Data assimilation;
- ATMOSPHERIC PROCESSES;
- 0555 Neural networks;
- fuzzy logic;
- machine learning;
- COMPUTATIONAL GEOPHYSICS;
- 1640 Remote sensing;
- GLOBAL CHANGE;
- 3275 Uncertainty quantification;
- MATHEMATICAL GEOPHYSICS