**4. Summary and Conclusions**

In this paper, statistical verification of a mesoscale ensemble numerical weather prediction system was conducted for hub-height wind prediction at 411 wind turbines representing ~130 wind farms. The ensemble system contains 39 forecast members, and is divided into 3 groups driven by the US GFS and GEOS and the Canadian GEM global weather model forecasts. Each group contains the same set of 13 physical perturbations. The verification period was from 1 March to 15 April 2020. This paper analyzes the error characteristics of the mean wind forecasts of the three ensemble groups and compares their performance. The error statistics (CC, BIAS, and MAE) of the wind forecasts—including the diurnal variability, differences in seven geographical regions, dependence on wind speed regimes, and growth by forecast time—are analyzed. The main conclusions of this study are as follows.


#### **5. Discussion**

It should be noted that the performance of numerical weather models is highly reliant on model resolution [18], regional climate [19], topography [20], underlying land-surface and soil properties [19], weather measurements [21] and data assimilation schemes for model initiation [23], as well as the lateral boundary conditions for limitedarea models [24]. The ensemble forecasts overestimate wind speeds. Similar results have also been reported in several previous studies using the WRF model over different global regions [24,36,38]. Although several studies have tried to identify the physical reasons for this, they have not reached a consensus on the issue. From the dynamical point of view, the height and roughness of the subsurface may not be sufficiently considered in the WRF model [69,70], and from the thermodynamic point of view, the WRF model may misestimate the cloudiness, making it difficult to predict the long- and shortwave radiation accurately, and resulting in the misestimation of near-surface wind speed [71–73]. Our results show that, driven by different global model forecasts, the BIAS properties of the WRF forecasts differ, but the overall BIAS trends are the same for all subregions in the studied domain.

This study focused on the wind forecast error characteristics of the Inner Mongolia mesoscale ensemble forecasting system with respect to the impact of the ensemble members driven by different global numerical weather prediction model forecasts. Our findings provide a basis for developing a statistical post-processing of the ensemble forecasts to improve wind and power forecasting for the wind farms, and for further improvement of the forecast capability of the WRF models in the future.

However, the present study was based on only 45 days of wind prediction data in the spring of 2020, making it insufficient to describe the year-round forecast error pattern. We are collecting more data to expand this work to a full-year period, and studying the seasonal variation patterns of wind forecast error statistics. Furthermore, this ensemble forecast system contains 10 perturbed members of the varying atmospheric boundary layer parameterization scheme. We are currently analyzing and comparing the error characteristics of the wind forecasts using these different atmospheric boundary layer parameterization schemes; the results will be reported in a separate paper.

**Author Contributions:** Conceptualization, J.S., Y.L. (Yubao Liu) and X.F.; methodology, Y.L. (Yubao Liu) and Y.L. (Yang Li); software, J.S.; validation, L.S. and X.F.; formal analysis, J.S.; investigation and resources, Y.L. (Yuewei Liu); data curation, J.S., L.S. and G.R.; writing—original draft preparation, J.S.; writing—review and editing, Y.L. (Yubao Liu) and Y.L. (Yang Li); visualization, J.S. and Y.L. (Yang Li); funding acquisition, Y.L. (Yubao Liu) and X.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Jibei Electric Power Company (Grant #520120210003), the Inner Mongolia Electric Power Company, and the Northwest Region Weather Modification Capability Development Program, RQC19081 and RQC-19176.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We give thanks to the Inner Mongolia Meteorological Bureau, the US National Center for Atmospheric Research (NCAR), and Nanjing University of Information Science and Technology (NUIST) for data and computing support.

**Conflicts of Interest:** The authors declare no conflict of interest.
