Kalman filter and correction of the temperatures estimated by PRECIS model

The purpose of this study is to evaluate the accuracy of the estimation the monthly mean temperature simulated by the PRECIS model—scenarios A2 and B2 of the IPCC—for Brazilian regions and to develop a Kalman filter to correct the systematic errors of the model for the months of January to June 2010. With a regionalized model, PRECIS aims to reproduce the main features of the climate in complex terrains. The temperature estimates for January to June 2010 are based on linear regression of PRECIS simulations in each pixel of the domain for two time periods, 1961–1990 and 2070–2100. These initial estimates are adapted to 1142 observing stations by a correction using the vertical temperature gradient of the Standard Atmosphere and the difference between model and real topography. The analysis was performed using monthly observed mean temperature data from meteorological stations, along with 1142 simulated data. The PRECIS model with systematic errors was ameliorated by the application of the filter resulting in an improved mean temperature prediction of 66% above the mean square error for the dry months and above 49% for the wet months, for both scenarios under study. At the half-way point, the improvement was 68% for the A2 scenario and 69% for scenario B2.

► The Kalman filter was effective in the estimated of the average temperature.
► The upward bias for the average temperature in 2010 is more intense.
► Reducing the Kalman filter variation improves the forecast predictions.
► Better results of Kalman filter were obtained in the rainy season.
► The Precis-Br model predictions were improved above 66% for the wet months.