Winter Subseasonal Wind Speed Forecasts for Finland from ECMWF

Otto Hyvärinen,Terhi K Laurila,Natalia Korhonen,Hilppa Gregow,Olle Räty,Andrea Vajda

doi:10.5194/asr-18-127-2021

Abstract

Abstract. The subseasonal forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts) were used to construct weekly mean wind speed forecasts for the spatially aggregated area in Finland. Reforecasts for the winters (November, December and January) of 2016–2017 and 2017–2018 were analysed. The ERA-Interim reanalysis was used as observations and climatological forecasts. We evaluated two types of forecasts, the deterministic forecasts and the probabilistic forecasts. Non-homogeneous Gaussian regression was used to bias-adjust both types of forecasts. The forecasts proved to be skilful until the third week, but the longest skilful lead time depends on the reference data sets and the verification scores used.

Highlights

Wind speed forecasts have many potential users that could benefit from skilful forecasts in different time scales, ranging from hourly to monthly forecasts
The coefficient a, the constant of Eq (2), grows as the lead time increases, while b, the coefficient of the ensemble means, decreases (Fig. 2a). This means that as the lead time increases the non-homogeneous Gaussian regression (NGR) pushes the forecast towards the climatology
If we calculate fictive forecasts of 2–5 m/s as the function of the lead time (Fig. 2b), we see that the forecasts larger than the mean are increasingly reduced as the lead time increases, so they tend to the climatological mean, about 3.4 m/s

Summary

Introduction

Wind speed forecasts have many potential users that could benefit from skilful forecasts in different time scales, ranging from hourly to monthly forecasts. Shortand medium-range forecasts of extreme wind speeds are often utilised in early warnings for severe weather (e.g., Neal et al, 2014; Matsueda and Nakazawa, 2015). In the subseasonal time scale, daily forecasts for a single point are no longer skilful, but by aggregating forecasts either in time or space (or both), the random errors might cancel out, while the possible signal is preserved. This study concentrates on subseasonal wind forecasts in winter, as forecasts for winter in northern Europe are known to be more skilful than forecasts for other seasons (e.g., Lynch et al, 2014).

Methods

Results

Discussion

Conclusion