Improving the Near-Surface Wind Forecast around the Turpan Basin of the Northwest China by Using the WRF_TopoWind Model

Abstract

Wind energy is a type of renewable and clean energy which has attracted more and more attention all over the world. The Northwest China is a region with the most abundant wind energy not only in China, but also in the whole world. To achieve the goal of carbon neutralization, there is an urgent need to make full use of wind energy in Northwest China and to improve the efficiency of wind power generation systems in this region. As forecast accuracy of the near-surface wind is crucial to wind-generated electricity efficiency, improving the near-surface wind forecast is of great importance. This study conducted the first test to incorporate the subgrid surface drag into the near-surface wind forecast under the complex terrain conditions over Northwest China by using two TopoWind models added by newer versions of the Weather Research and Forecasting (WRF) model. Based on three groups (each group had 28 runs) of forecasts (i.e., Control run, Test 01 and Test 02) started at 12:00 UTC of each day (ran for 48 h) during the period of 1–28 October 2020, it was shown that, overall, both TopoWind models could improve the near-surface wind speed forecasts under the complex terrain conditions over Northwest China, particularly for reducing the errors associated with the forecast of the wind-speed’s magnitude. In addition to wind forecast, the forecasts of sea level pressure and 2-m temperature were also improved. Different geographical features (wind-farm stations located south of the mountain tended to have more accurate forecast) and weather systems were found to be crucial to forecast accuracy. Good forecasts tended to appear when the simulation domain was mainly controlled by the high-pressure systems with the upper-level jet far from it.

Improving the Wind Power Density Forecast in the Middle- and High-Latitude Regions of China by Selecting the Relatively Optimal Planetary Boundary Layer Schemes

“…One of these is that the power generation of wind farms is difficult to predict, as wind fields are characterized by remarkable randomness, diversity, intermittence, and uncontrollability [6][7][8][9][10]. Various methods were developed to forecast the wind at the near-surface level, which can be roughly divided into two types: physical models that are mainly based on the thermodynamic and dynamic mechanisms of the atmosphere [11][12][13], and statistical models [14][15][16][17] that are mainly based on statistical features (this type includes machine learning and deep learning). For the former type, numerical models are effective and widely used tools, which can provide longer-term forecasts of wind speed and related meteorological factors than those of statistical models.…”

“…Therefore, improving the accuracy of the wind forecast in these regions is crucial for China's wind power industry. Due to its relatively high forecasting skills, the WRF model is a widely used numerical model in wind forecasting [12,19,[28][29][30][31]. It provides a series of PBL parameterization schemes [20], which affect the accuracy of wind forecasts remarkably [13].…”