Comparisons of the added value of dynamical downscaling of ECMWF EPS and
NCEP GEFS for wind forecast in the complex terrain of Sichuan and Yunnan
in China
Abstract
Numerical weather prediction (NWP) models are commonly used for
wind power forecasts, but NWP forecasts are uncertain due to
uncertainties in the initial conditions, approximate model physics, and
the chaotic nature of the atmosphere. Ensemble prediction systems (EPS),
which simulate multiple possible futures, thus provide valuable
information about forecast uncertainties. However, the spatial
resolution of global ensemble forecasts from the European Centre for
Medium-range Weather Forecast (ECMWF) and the National Centers for
Environmental Prediction (NCEP) is relatively coarse and insufficient
for many wind power farms built in complex terrain. This work proposes
using the Weather and Research Forecasting model (WRF) to downscale
ECMWF EPS and NCEP global ensemble forecast system (GEFS) to determine
and compare the added values of downscaling different global EPS
forecasts for wind forecasts in the complex terrain of Sichuan and
Yunnan in China. A total of 366 days of day-ahead forecasts (28 to 51
hours) for wind speed at 80 meters are evaluated. The results
demonstrate that the ensemble average of the higher resolution WRF
downscaled forecast is considerably better than that of the global EPS
forecast, and downscaled forecast of ECMWF EPS achieves the best
performance. Also, a selective ensemble average (SEA) method is proposed
and applied for the ultra-short (10 to 13 hours) forecast. Verification
results demonstrate that the SEA method outperforms the ensemble mean.
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