Work

Comparison of the performance between three Doppler wind lidars and a novel wind speed correction algorithm

Year: 2025

Type: article

Abstract: . Doppler wind Lidars (DWLs) have been widely used to detect wind vector variations, based on ground monitoring of atmospheric boundary layer and wind shear. This study evaluates the performance between three DWLs and in situ balloon radiosonde. Lidars data comparison focus on the low altitudes (height < 2 km) from July to September 2021 from three producers: MSD (Minshida), CUIT (homemade), and WP (windprofile) Lidars. Comparison of results shows the root mean square errors (RMSE) for wind speed were 1.11 m/s, 4.45 m/s, and 5.15 m/s, while wind direction RMSE were shown at 49.83°, 82.89°, and 84.87°, respectively. The measurement accuracy decreases with the altitude increase (<2 km). Particle mass concentration loading has positive correlation on the Lidar performance, when PM2.5 ranges from 35 to 50 µg/m³, MSD Lidar exhibited the highest wind speed correlation (R² = 0.82) with radiosonde, and the wind direction accuracy observed by the three Lidars is enhanced with the increase of aerosol concentration, indicating that particle loading is the critical factor affecting the wind profile. Lidar performance varied significantly with planetary boundary layer heights (PBLH), three Lidars demonstrated optimal performance at lower altitudes (500–750 m), with the Pearson correlation coefficients (PCCs) of wind speed are 0.97, 0.92, and 0.72, while the wind direction is shown at 0.98, 0.75, and 0.70, respectively. The vertical relationship between cloud base height (CBH) and PBLH had also varied influences on the Lidar measurements. Machine learning was used to remove anomalies and complement the missing values, the random forest (RF) demonstrated superior performance with the Area Under the Curve (AUC) of 0.93(CUIT) and 0.90(WP) in the Receiver Operating Characteristic (ROC) curves. RF-based correction of CUIT enhanced R² from 0.42 to 0.65. The R² between RF-based CUIT and Aeolus satellite is shown as 0.83, indicating that the method effectively improved data, even in circumstances of anomalies. We proposed a new correction algorithm combined with the isolation forest (IF) and RF to handle high-dimensional and incomplete datasets. Our procedure could increase the Lidar measurement quality of the wind.

Source: Atmospheric measurement techniques

DOI: 10.5194/amt-18-4755-2025