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Question

1. What instruments were used in the field campaign?

2. How does WindScanner lidar system reduce precipitation influence?

3. How is the sonic anemometer's orientation determined?

4. How do sonic and cup anemometers compare in wind speed measurements?

Accepted Answer

In Section 2, the field campaign is introduced, and the instruments used are elaborately described. These instruments include sonic anemometers and a ground-based disdrometer. The sonic anemometers measure wind velocity, while the disdrometer is used to retrieve rain characteristics. The disdrometer data is sourced from a nearby meteorological mast, as mentioned in the study by Tilg et al., 2020. The combination of these instruments allows for comprehensive data collection on wind velocity and rain characteristics, which are crucial for the study's analysis and conclusions.

Accepted Answer

The WindScanner lidar system reduces the influence of precipitation on estimated wind velocity by conducting field experiments and using three cw lidars. The lidars employ a dual-prism beam scanner, enabling them to orient the beam in any direction within +-61 * of the adjustable center axis. The lidars are synchronized to scan the same pattern in space simultaneously, focusing on one static point. The lidars are covered with green rain barrels to avoid rain droplets covering the windows. The influence of different probe lengths and elevation angles on the method's performance is investigated. The system measures wind velocity using a sampling frequency of 3 kHz, with Doppler spectra containing 512 frequency bins and a wind speed spectral resolution of 0.183 ms-1. The system identifies and suppresses rare instances where a raindrop resides in the beam, using the in-phase/quadrature-phase (IQ) detection method and considering the maximum downfall speed of raindrops from disdrometer measurements.

Accepted Answer

The orientation of the sonic anemometer is determined using a Leica Total Station. The azimuth angle of the boom, which is the direction offset of the sonic anemometer relative to the North, is measured. In the provided information, the azimuth angle of the boom to the north is 13.2 * in UTM32 zone. Additionally, the tilt angle of the sonic anemometer to the vertical is 1.9 *. These measurements are used to compute the unit vectors when projecting the wind velocity data.

Accepted Answer

The 10-minute averaged wind speeds by sonic and cup anemometers are in good agreement for all heights, including 31 m where lidars were measuring. The slope of a linear regression is 1.008 with a coefficient of determination R2 equal to 0.997, showing that wind speeds measured by sonic anemometers agree well with cup anemometers (with only a 1% difference). The mean absolute difference of wind speed between sonic and cup anemometer at 31 m height is 0.11 ms-1. The same conclusion can be drawn for wind direction, with a mean absolute difference of 1 * between sonic at 44 m and vane at 41 m height.

Accepted Answer

The Met Office defines moderate rain as a precipitation rate between 2.5 mm and 7.6 mm per hour. This range allows for the investigation of the performance of the proposed method to filter away rain signals during precipitation levels. The selected comparison period from 15:12 to 18:11 includes no-rain, light-rain (precipitation rate smaller than 2.5 mmh-1), and moderate-rain minutes, enabling the analysis of the method's effectiveness in different rain intensities.

Accepted Answer

The noise floor in lidar data processing is flattened by dividing every raw spectrum with the background spectrum. The background spectrum is calculated as the median power spectral density per frequency of 180000 Doppler spectra, acquired during a one-minute period. A smaller background is chosen for any pair of frequencies (-f, f) to provide the true background even if the wind velocity is constant over the minute. However, if the wind velocity is around zero, this procedure does not work. In such cases, a background spectrum is calculated for a period where the line-of-sight speed is away from zero. This process helps in reducing the noise and obtaining a clearer spectrum for further analysis.

Accepted Answer

Rain-removal normalization significantly improves lidar spectra by suppressing the narrow Doppler signal caused by raindrops. This process reduces the influence of rain droplets, as seen in the narrow peak closer to the center zero frequency. After normalization, the bias between the reference sonic wind velocity and that of the lidars is reduced, as demonstrated in Table 3. For example, the bias decreases from -1.56 to -0.18 ms-1 at 50 Hz for lidar #3. This indicates that normalization by the spectral peak value effectively suppresses the impact of rain droplets on lidar measurements, enhancing the accuracy of wind velocity data.

Accepted Answer

Rain-suppressing normalization effectively filters out rain signals in lidar wind velocity measurements, especially during heavy rain and with smaller probe lengths. The bias between sonic and lidar estimations is reduced after normalization, indicating more accurate wind velocity estimations. The improvement in normalization performance is observed during moderate rain for lidar #1 with a shorter focus distance and during light rain and moderate rain for lidar #3 with a longer focus distance. The larger probe length of lidar #3 also influences the variation of estimated wind velocity, even though the elevation angle is smaller. The reduction in the integral of the absolute difference of the PDF after normalization further supports the effectiveness of rain-suppressing normalization in lidar wind velocity measurements.

Accepted Answer

Rain intensity affects lidar wind velocity estimation by introducing a bias in the velocity measurements. In the case of lidar #1 with a smaller probe length, the bias around the rain intensity peak is reduced after normalization with the spectral peak. This is achieved by filtering out low and negative velocity caused by raindrops. For lidar #3, the improvement in wind velocity estimation through normalization is highly effective, especially when it starts to rain at 15:29 until 16:48. The wind velocity calculated by the raw lidar data is often underestimated due to the small projection of raindrop velocity, which counteracts the aerosol projection. However, after rain-suppressing normalization, the 1-minute lidar wind velocity matches well with the sonic measurement for lidar #3 with a larger probe length. During light rain, the impact of rain-suppressing normalization on lidar #1 with a short focus distance is minimal due to the rare occurrence of raindrops passing through the laser beam. Overall, rain-suppressing normalization performs well for both large and small probe lengths when it rains.

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Most frequently asked questions

1. What instruments were used in the field campaign?

2. How does WindScanner lidar system reduce precipitation influence?

3. How is the sonic anemometer's orientation determined?

4. How do sonic and cup anemometers compare in wind speed measurements?

5. What precipitation rate defines moderate rain?

6. How is the noise floor flattened in lidar data processing?

7. How does rain-removal normalization affect lidar spectra?

8. How does rain-suppressing normalization affect lidar wind velocity measurements during different rain intensities and probe lengths?

9. How does rain intensity affect lidar wind velocity estimation?

References

Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm

Offshore wind profiling using light detection and ranging measurements

Conically scanning lidar error in complex terrain

A review of estimation of effective wind speed based control of wind turbines

Complex terrain experiments in the New European Wind Atlas.

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