Statistical model for downscaling extreme surface temperatures at the weather station network in the Moscow region

Abstract

Investigation of predictability of extreme meteorological values is an urgent problem of the present time. The purpose of this work is to demonstrate possibilities of reconstructing daily maximum and minimum air temperatures on a city scale using short-range weather forecasts. A statistical model is suggested, with which more than 85% of the natural variability of the extreme temperature at the Moscow weather stations can be reconstructed. A possibility to predict the maximum outliers in the solutions is demonstrated. The necessity to use the procedures of filling up the available gaps in observational data is emphasized. A classification of extreme situations in the atmosphere is suggested, which will help to increase the accuracy of the solution.