Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China

Abstract

In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches.

Keywords

• wind forecast
• error decomposition
• bias
• distribution
• sequence