Abstract
Surface wind speed forecast from an operational WRF Ensemble Prediction System (WEPS) was verified, and the system-bias representations of the WEPS were investigated. Results indicated that error characteristics of the ensemble 10-m wind speed forecast were diurnally variated and clustered with the usage of the planetary boundary layer (PBL) scheme. To correct the error characteristics of the ensemble wind speed forecast, three system-bias representations with decaying average algorithms were studied. One of the three system-bias representations is represented by the forecast error of the ensemble mean (BC01), and others are assembled from each PBC group (BC03) as well as an independent member (BC20). System bias was calculated daily and updated within a 5-month duration, and the verification was conducted in the last month, including 316 gauges around Taiwan. Results show that the mean of the calibrated ensemble (BC03) was significantly improved as the calibrated ensemble (BC20), but both demonstrated insufficient ensemble spread. However, the calibrated ensemble, BC01, with the best dispersion relation could be extracted as a more valuable deterministic forecast via the probability matched mean method (PMM).
Highlights
Despite the increasing horizontal and vertical resolution in numerical weather prediction (NWP) models, the resolving topography and imperfect land surface processes of-ten result in over or under-predicting surface wind
This study proposed three experiments corresponding to three kinds of system-bias representation with a decaying average algorithm to calibrate the original ensemble
The 3-km 20-member Weather Research and Forecast (WRF) Ensemble Prediction System (WEPS) system consists of initial, lateral boundary, and model perturbations, including multi-physics and two stochastic perturbation schemes (SKEB and Stochastic Perturbation of Physics Tendencies (SPPT))
Summary
Despite the increasing horizontal and vertical resolution in numerical weather prediction (NWP) models, the resolving topography and imperfect land surface processes of-ten result in over or under-predicting surface wind. The diurnal variation of the surface wind speed forecast is highly related to the configuration of land surface and planetary boundary layer (PBL) processes in NWP models [4,5,6]. The first operational WEPS version considered the model uncertainties using a multi-physics approach [11], and an initial condition perturbation was further included to improve the dispersion relation of WEPS. Li et al [9] demonstrated a positive impact in model quantitative precipitation forecasts (QPF) when multi-physics StochasticKinetic Energy Backscatter (SKEB) Scheme [14] and Stochastic Perturbation of Physics Tendencies (SPPT) [15,16] were included, in addition to initial and boundary condition perturbations.
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