Project: Advanced random field methods in data assimilation for short-term weather prediction

An indispensable feature of weather nowcasting is data assimilation (DA), which includes the newest data, e.g. radar and satellite data, into the numerical weather prediction model. The goal of the project is to introduce qualitatively new DA techniques. These techniques (automatic registration, morphing) have arisen in the theory of pattern recognition. They perform a correction of shape and position of objects by means of a horizontal motion field at the same time as the correction of the values of the physical variables. The methods will be generalized so as to be able to use radar data. This will result in better timing and position of atmospheric fronts or precipitation fields and in an improvement of the forecast. Many DA techniques are computationally demanding. New methods which rely on the theory of random fields and Wavelet Transform will be developed. Expensive computations with large matrices in the Ensemble Kalman Filter analysis will be replaced by cheap wavelet transform calls. The methods also can substantially reduce the number of ensemble members required.

Funded by: GA ČR. GA13-34856S

Duration: 2013-2016

Investigator: Pešice, P.