A Estimation and correction of weather model errors


We develop a method to estimate and correct atmospheric model errors that can be trained using operational 6 hour analysis increments (a proxy for the forecast errors). We separate the errors into bias (including annual and diurnal cycles), and state-dependent errors, that are estimated using SVD of the covariance between state anomalies and forecast errors. Tests with the primitive equation SPEEDY model show that 1) the method works well; 2) when integrating the model with the corrections derived from the 6 hr yields significantly better results than a statistical correction performed a posteriori; 3) the state dependent correction based on SVD are many orders of magnitude better than Leith (1978) and DelSole and Hou (1999) linear regression approach, and are twice as effective in improving the forecast. The method can be easily implemented in an operational setup.
Chris Danforth, Eugenia Kalnay and Takemasa Miyoshi