Ensemble Kalman Filter with perturbed observations in weather forecasting and data assimilation

TL;DR

This paper discusses the development and application of the Ensemble Kalman Filter with perturbed observations for weather forecasting, demonstrating its advantages over traditional methods through mathematical derivation and simulation on the Lorenz 63 model.

Contribution

It introduces a gradual development of the Ensemble Kalman Filter with perturbed observations, highlighting its benefits for nonlinear systems and computational efficiency.

Findings

Larger ensemble sizes reduce prediction error.

The method extends Kalman filtering to nonlinear systems.

Simulation confirms effectiveness on Lorenz 63 model.

Abstract

Data assimilation provides algorithms for widespread applications in various fields. It is of practical use to deal with a large amount of information in the complex system that is hard to estimate. Weather forecasting is one of the applications, where the prediction of meteorological data are corrected given the observations. Numerous approaches are contained in data assimilation. One specific sequential method is the Kalman Filter. The core is to estimate unknown information with the new data that is measured and the prior data that is predicted. As a matter of fact, there are different improved methods in the Kalman Filter. In this project, the Ensemble Kalman Filter with perturbed observations is considered. It is achieved by Monte Carlo simulation. In this method, the ensemble is involved in the calculation instead of the state vectors. In addition, the measurement with perturbation is viewed as the suitable observation. These changes compared with the Linear Kalman Filter make it more advantageous in that applications are not restricted in linear systems any more and less time is taken when the data are calculated by computers. The thesis seeks to develop the Ensemble Kalman Filter with perturbed observation gradually. With the Mathematical preliminaries including the introduction of dynamical systems, the Linear Kalman Filter is built. Meanwhile, the prediction and analysis processes are derived. After that, we use the analogy thoughts to lead in the non-linear Ensemble Kalman Filter with perturbed observations. Lastly, a classic Lorenz 63 model is illustrated by MATLAB. In the example, we experiment on the number of ensemble members and seek to investigate the relationships between the error of variance and the number of ensemble members. We reach the conclusion that on a limited scale the larger number of ensemble members indicates the smaller error of prediction.