Background error covariance in numeral weather production

Abstract

Improving of weather forecast quality is a continuous work, as it is an invaluable for society and environment. WRF model have been tuned and tested over Georgia’s territory for years. Nowadays as local meteorological network became denser and many remote ob- servational sources are available data assimilation with variational methods is current challenge. First time in Georgia the process of data assimilation in Numerical weather prediction is developing, the way for forecast initial conditions’ correction. Assessment of the forecast error is one of the first and most important steps in data assimilation. This work presents how forecast error statistics appear in the data assimilation problem through the background error covariance matrix – B, where the variances and correlations associated with model forecasts are estimated. Statistics of B are usually determined for a limited set of vari-ables, called control variables that minimize the error covariance between variables. Results of generation and tuning of back-ground error covariance matrix for five con- trol variables using WRF model over Georgia with desired domain configuration are discussed and presented. The mathematical and physical properties of the covariances are also reviewed.