Operative Scheme for the Short-range Complex Forecasting of Surface Air Temperature and Humidity
The statistical scheme is proposed for the forecast of surface air temperature and humidity using operative weather forecasts with 3–5-day lead time from the best forecasting hydrodynamic models as well as the archives of forecasts of these models and observational data from 2800 weather stations of Russia, Eastern Europe, and Central Asia. The output of the scheme includes the forecasts of air temperature for the standard observation moments with the period of 6 hours and extreme temperatures with the lead times of 12–120 hours. The accuracy of temperature and humidity forecasts for the period from July 2014 till June 2017 is much higher than that for the forecasts of original hydrodynamic models. The skill scores for extreme temperature forecasts based on the proposed method are compared with the similar results of the Weather Element Computation (WEC) forecasting scheme and forecasts by weathermen.
KeywordsForecasting model air temperature forecast error lead time extreme temperature dew-point temperature
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- 1.O. A. Aldukhov, A. N. Bagrov, and V. A. Gordin, “Statistical Characteristics of Prognostic Meteorological Fields and Their Use in Objective Analysis,” Meteorol. Gidrol., No. 10 (2002) [Russ. Meteorol. Hydrol., No. 10 (2002)].Google Scholar
- 2.A. N. Bagrov, Ph. L. Bykov, and V. A. Gordin, “Complex Forecast of Surface Meteorological Parameters,” Meteorol. Gidrol., No. 5 (2014) [Russ. Meteorol. Hydrol., No. 5, 39 (2014)].Google Scholar
- 3.A. N. Bagrov, Ph. L. Bykov, and V. A. Gordin, “Operative Scheme for the Short-range Complex Forecasting of Wind,” Meteorol. Gidrol., No. 7 (2018) [Russ. Meteorol. Hydrol., No. 7, 43 (2018)].Google Scholar
- 4.A. N. Bagrov, V. A. Gordin, E. A. Loktionova, and N. Yu. Ochan, “Control and Archiving of Global Surface Air Temperatures at the Hydrometeorological Center of the Russian Federation,” Meteorol. Gidrol., No. 2 (1993) [Russ. Meteorol. Hydrol., No. 2 (1993)].Google Scholar
- 5.Ph. L. Bykov and V. A. Gordin, “Meteorological Fore casting is Useful for Short-term Forecast of Hourly Electricity Consumption for the Subjects of the Russian Federation,” Izv. Akad. Nauk, Energetika, No. 5 (2017) [in Russian].Google Scholar
- 6.R. M. Vil’fand, P. P. Vasil’ev, E. L. Vasil’eva, G. K. Veselova, and I. A. Gorlach, “Me dium-range Fore cast of Air Temperature and of Some Dangerous Phenomena Using the Technique of the Hydrometcenter of Russia,” Meteorol. Gidrol., No. 10 (2010) [Russ. Meteorol. Hydrol., No. 10, 35 (2010)].Google Scholar
- 7.V. A. Gordin, Mathematics, Computer, Weather Forecasting, and Other Scenarios of Mathematical Physics (Fizmatlit, Moscow, 2010, 2013) [in Russian].Google Scholar
- 8.Code for Operative Transmission of Meteorological Observation Data from Roshydromet Weather Station Network (Hydrometcenter of Russia, Moscow, 2013) [in Russian].Google Scholar
- 9.RD 52.27.724-2009. Manual for General Short-range Weather Fore casting (IG-SOTsIN, Obninsk, 2009) [in Russian].Google Scholar
- 10.Ph. L. Bykov and V. A. Gordin, “Calls Forecast for the Moscow Ambulance Service. The Impact of Weather Forecast,” in Numerical Algebra with Applications. Proceedings of Fourth China–Russia Conference (Southern Federal University Publ., Rostov-on-Don, 2015).Google Scholar