Improved Dynamical-statistical Method for Forecasting Monthly Surface Air Temperature

The paper justifies the possibility of improving the dynamical-statistical method of predicting monthly average surface air temperature through the application more advanced hydrodynamic models and statistical methods for operational use by the Hydrometeorological Center of Russia. A technology for monthly forecasting of surface air temperature anomalies is presented. The technology is based on using both the results of an improved 15-day medium-range weather element forecast scheme and the outcome of integrating the SL-AV model over a 16-30 day interval. Quality assessments of the forecasts obtained in real-time mode for 326 stations located in Russia are provided. The advantages of the proposed approach, particularly evident in cases of significant air temperature anomalies, are demonstrated. The obtained results are expected to be used in the technology for issuing long-range forecasts by the Hydrometeorological Center of Russia.