5:15 PM - 6:45 PM
[MZZ43-P05] Assessing the vertical structure of the atmosphere using Unmanned Aerial Vehicles and validating a numerical weather prediction model on the coast of Japan
Keywords: Coastal Effects, offshore wind, Atmospheric Boundary Layer, UAV (Unmanned Aerial Vehicle), numerical weather prediction model
Governments worldwide are driving the adoption of renewable energy to help achieve carbon neutrality. Offshore wind power, in particular, is spreading rapidly because it can generate substantial outputs. For offshore wind projects, an understanding of wind conditions is critically important in every phase, from planning to operation and maintenance. In Japan, due to the considerable depth of the surrounding seas, offshore wind farm installation has begun in nearshore shallow waters, where the land has a significant impact. However, studies focusing on the understanding of coastal effects on wind farms are still scarce in Japan. Numerical weather prediction (NWP) models have been widely used to predict offshore wind conditions. These models can predict wind conditions over large areas; however, the coarse resolution of NWP models cannot adequately assess localized meteorological fields, such as in wind farms and in coastal areas. Therefore, our goal is to find ways to predict wind conditions with high accuracy in coastal areas. To achieve this, we conducted simultaneous observations over both sea and land and evaluated the performance of a weather research and forecasting model (WRF) on the coast of Japan.
To understand coastal effects on wind conditions in Japan, we conducted simultaneous observations of the vertical structure of the atmosphere using UAVs (Unmanned Aerial Vehicles) over the sea and land. Our data showed that when the wind blows from land towards the coast, the sea surface cools the warm air from the land, leading to a stable stratification near the sea surface. This stable stratification is evident before the wind direction shifts from the land breeze to the sea breeze. Moreover, the top of the stable stratification is located at a height of approximately 100 meters above sea level and a distance of 1300 meters from the onshore toward the offshore. This stable structure is likely to cause low-level jets with high wind speeds. Further studies of this effect are required.
Next, our comparative study evaluated the performance of the Weather Research and Forecasting model (WRF) with vertical LiDAR data in the same region. The results indicate that the accuracy of wind direction prediction with WRF is 80%. Moreover, the prediction accuracy for the land breezes decreased by 20% compared with that for the sea breezes. Particularly, the prediction results during the wind direction shift from the land breeze to the sea breeze show lower agreement with the observations than those of any other period. Wind direction data are important, for example, for evaluating turbine wakes in windfarms; therefore, further studies to improve prediction accuracy are required.
To understand coastal effects on wind conditions in Japan, we conducted simultaneous observations of the vertical structure of the atmosphere using UAVs (Unmanned Aerial Vehicles) over the sea and land. Our data showed that when the wind blows from land towards the coast, the sea surface cools the warm air from the land, leading to a stable stratification near the sea surface. This stable stratification is evident before the wind direction shifts from the land breeze to the sea breeze. Moreover, the top of the stable stratification is located at a height of approximately 100 meters above sea level and a distance of 1300 meters from the onshore toward the offshore. This stable structure is likely to cause low-level jets with high wind speeds. Further studies of this effect are required.
Next, our comparative study evaluated the performance of the Weather Research and Forecasting model (WRF) with vertical LiDAR data in the same region. The results indicate that the accuracy of wind direction prediction with WRF is 80%. Moreover, the prediction accuracy for the land breezes decreased by 20% compared with that for the sea breezes. Particularly, the prediction results during the wind direction shift from the land breeze to the sea breeze show lower agreement with the observations than those of any other period. Wind direction data are important, for example, for evaluating turbine wakes in windfarms; therefore, further studies to improve prediction accuracy are required.