Assessing the success of ensemble long-term meteorological forecasting in the Western Arctic

The article presents quality assessments of using the ensemble approach to produce long-term meteorological forecast in the Western Arctic with a one-month advance. The assessment of retrospective forecasts’ quality of the sea level pressure anomalies field and surface air temperature anomalies has been performed for 2010–2018. The ensemble forecast for the second month was made using two methods. The f irst method is the forecast of the mean field of meteorological parameters for all ensemble members. The second method is the forecast of the mean field made by the best class selected from all ensemble members by the clustering procedure. The best class was selected by comparing macrosynoptic process evolution of the first forecast month of each selected class with the actual observations. In the area considered, which is bounded by coordinates from 20º W to 100º E and from 60º N to 80º N, 108 retrospective forecasts were made. As an independent series, the forecast success for 2018 and 2024 was analyzed using two ensemble forecasting techniques and a synoptic-statistical method (the Wangenheim–Geers macro-circulation method). Three estimates of the forecast quality were obtained — the mean square error, the correlation coefficient between the forecast and actual fields of meteorological parameters, and the coefficient of geometric similarity of the forecast and actual fields of the meteorological parameter. The estimation of quality was made for two parameters — sea level pressure and surface air temperature. The highest quality of forecasts using the best class method is observed in the summer season, and the RMS error of forecasts is minimal at this time. The forecast by the method of all ensemble members is preferable in the winter season. The results show that, in general, the best-class ensemble forecasts are more accurate for forecasting the phase of pressure anomalies, while for forecasting the magnitude of temperature and pressure anomalies, it is preferable to use the forecasts for all ensemble members. For 2018 and 2024, both ensemble forecast methods showed higher forecast quality scores than the synoptic-statistical method.

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