Staff members from HSE and the Hydrometeorological Centre of Russia have proposed a new operative scheme for the short-range complex forecasting of wind and possible gusts, surface air temperature, and humidity. The results, i.e., estimates of average forecast errors at different lead times and their comparison with competitors’ results, were published in the journal «Russian Meteorology and Hydrology».
Staff members from HSE and the Hydrometeorological Centre of Russia have proposed a new operative scheme for the short-range complex forecasting of wind and possible gusts, surface air temperature, and humidity. The results, i.e., estimates of average forecast errors at different lead times and their comparison with competitors’ results, were published in the journal «Russian Meteorology and Hydrology».
The authors used several global and regional hydrodynamic weather forecasting schemes: UKMO — United Kingdom Meteorological Office (UK), NCEP — National Center for Environmental Prediction (USA), JMA — Japan Meteorological Agency (Japan), PLAV — Hydrometeorological Center of Russia, Cosmo-Ru7 — Hydrometeorological Center of Russia, Cosmo-Ru13 — Hydrometeorological Center of Russia, and WRF-18 — Hydrometeorological Center of Russia. Methods of time series analysis were applied to the forecasts of these schemes for 2,800 synoptic stations. Machine learning helped researchers to develop a comprehensive forecast to improve the quality of existing schemes.
‘We assimilate results of several schemes — Russian and foreign — and process them together, using real data from thousands of synoptic stations over the years of observations. These large archives help us to receive a certain combination of data and to improve the results, that is, to reduce the error compared to the best of the analysed data,’ says Vladimir Gordin, Professor in the Department of Mathematics at the HSE Faculty of Economic Sciences and one of the study’s authors.
Read more at National Research University Higher School of Economics
