# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 4 Discussion  
A full year of measurements of the wind profile in a windy climate performed at a marine site in the North Sea has been analyzed. The measurements were carried out with a \(\sim\) 600 m range wind Doppler lidar in combination with a 100 m tall meteorological mast. The analysis was carried out with observations of wind profiles and CNR values from a WLS-70 Doppler Wind lidar from Leosphere. To assess if the findings are specific for the device used here, it is worth discussing if the effects can be observed in observations from other types of lidars and locations. A similar dependence on the CNR threshold was found applying a S200-Leosphere wind lidar in a study [34] of horizontal (Figure 37 in [35]) and vertical profiles of the wind speed that was performed over the North Sea. As part of this activity, vertical profiling of the wind speed was also carried out with a WLS7 version1 wind lidar (make Leosphere) that was operating at the west coast of Denmark from the beginning of November 2015 to the end of February 2016. The factory CNR threshold setting was -25 dB. Figure 10 shows the mean wind speed at 40 m for a number of CNR threshold values as a function of the filtering height, i.e., the height up to which concurrent measurements fulfill the CNR threshold requirement. It can be observed that the mean wind speed increases when the CNR threshold value is increased, which is in agreement with the findings reported here for the observations at FINO3. The figure also illustrates the availability of the measurements as a function of the CNR threshold value are plotted as a function of the filtering height. We note that similar trends as a function of the CNR threshold and filtering height during the same campaign were observed using a WLS7 version 2 wind lidar (not shown). Figure 9: Wind roses for CNR \(>\)–29 dB. Upper left panel is the wind rose at 126 m when filtering the wind lidar measurements at 126 m, upper right shows the concurrent wind rose for the vane measurements at 100 m, lower left panel is the wind rose at 626 m for measurements filtered at 626 m, and lower right panel when the CNR threshold is applied to the profile between 126 m and 626 m.  
A proper mean wind speed estimate should be based on a time-series of wind speed measurements which have data evenly distributed in time, and there should not be extended periods of lacking data. This is especially so if there are variations in the wind conditions, such as missing data from periods with pronounced seasonal variations as are typical for, e. g. the westerlies with high wind speeds during fall, or systematic missing measurements from, for example, night conditions where the wind regime over lands is driven by different physical processes compared to daytime conditions. A typical example is the analysis of the FINO3 data presented here. The resulting mean wind speed is not representative for a yearly average because of the lack of data in July and poor coverage in August and September, therefore, there will be relatively more weight on the measurements in winter and spring, which will cause a bias in the estimation of the real annual mean wind speed. Filtering the observations with a high CNR threshold value favors conditions with high wind speeds resulting in a time-series of unevenly distributed data, and, therefore, will provide a biased estimate of the real mean wind speed for the actual period. It is, therefore, crucial when estimating the mean wind speed over a prescribed period (e.g., annual or seasonal) that the measurements are evenly distributed in time and that measurements are not lacking for extended periods where the wind speed conditions can be climatological different. The error in estimating the mean wind speed over a given period is a function of data availability and data quality.