Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements

Abstract

Wind lidar observations are characterized by a Carrier-to-Noise-Ratio that is often used to filter the observations. The choice of the Carrier-to-Noise-Ratio threshold value for the wind lidar observations is found to have an effect on the climatological wind speed distribution in such a way that when the Carrier-to-Noise-Ratio (CNR) threshold value is increased the wind speed distribution is shifted to higher values. Based on one year of observations carried out with a wind lidar from 126 m to 626 m height at the FINO3 (Forschungsplattform in Nord- und Ostsee Nr. 3) research platform in the North Sea, the effect that the choice of the Carrier-to-Noise threshold value has on the climatology of the wind speed and direction as well as the wind power density in relation to wind energy is illustrated and discussed. In the one-year data set considered here it is found that for thresholds larger than −29 dB, the mean wind speed and wind rose measured by the wind lidar become a function of the threshold value, and for values smaller than ~ −29 dB further decrease of the Carrier-to-Noise-Ratio threshold has a minor effect on the estimated mean wind speed and wind rose. The analysis of the data set from the North Sea shows that the limit for the Carrier-to-Noise-Ratio of the observations should be −29 dB or less to obtain a threshold independent estimate of the mean wind speed and wind rose. Alternatively, all valid observations should be used for the analysis. Although this study is specific for the conditions in the North Sea, we suggest that for a representative estimation of the wind resource with wind lidars, the effect of the CNR threshold filtering on the wind distribution should be studied when the recovery rate is less than 100%.

Coastal Wind Measurements Using a Single Scanning LiDAR

“…As shown in previous studies [30], the CNR can serve as a good indicator for evaluating the reliability of LiDAR-generated measurement data. Generally, the fraction of the unreliable contaminated data increased as the CNR value decreased.…”

“…Figure 20a shows the relationships between the 10-minute mean wind speed and TI of the SA and 100s for Case 9. The dark lines indicate the IEC standards [30] for classifying wind turbines based on site-specific turbulence conditions. The number of samples for each bin is shown in Figure 20b.…”

Filtering of pulsed lidars data using spatial information and a clustering algorithm

“…Both performance metrics, the recovery rate of abnormal measurements in the tails of the pdf of reliable observations and its statistical distance to the pdf of filtered non reliable observations, will be assessed for the median-like filter, the clustering filter and also for data filtered with a CNR threshold of -29 dB, following (Gryning and Floors, 2019).…”

“…For long-range scanning lidars, this becomes an important issue due to the lack of references to identify reliable observations, since noise can contaminate large portions of the scanning domain. The most commonly used criteria to retrieve reliable observations is a threshold on values of the Carrier-to-noise ratio, CNR, which, depending on the site conditions, experimental setup and instrument manufacturer, can take values between [−29 dB, −20 dB] and [−8 dB, 0 dB] as lower and upper bounds, respectively (Gryning et al, 2016;Gryning and Floors, 2019). This criteria results in large amounts of data rejected unnecessarily in regions far from the instrument, due to the nature of CNR which decreases rapidly with distance.…”

Departure from <i>K</i>-theory in the planetary boundary layer

“…For both sites, a CNR lower limit of −29 dB is used to filter radial wind speeds at all heights up to 500 m, so only fully available profiles up to this height are selected for the analysis. We choose −29 dB as a threshold since this value gives an unbiased estimation of the wind climatology (Gryning and Floors, 2019) and provides more valid profiles for robust statistics.…”