A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw

A two-year measurement campaign of the ZephIR 300 vertical profiling continuous-wave (CW) focusing wind lidar has been carried out by the Royal Netherlands Meteorological Institute (KNMI) at the Cabauw site. We focus on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213-m tall meteorological mast. We find an overall availability of quality controlled wind lidar data of 97 % to 98 %, where the missing part is mainly due to precipitation events 5 exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. The mean bias in the wind direction is within 2◦, which is on the same order as the combined uncertainty in the alignment of the wind lidars and the mast wind vanes. The well-known 180◦ error in the wind direction output for this type of instrument occurs about 9 % of the time. A correction scheme based on data of an 10 auxiliary wind vane at a height of 10 m is applied, leading to a reduction of the 180◦ error below 2 %. This scheme can be applied in real-time applications in case a nearby, freely exposed, mast with wind direction measurements at a single height is available.


Data availability
The wind lidar was operating 99.4 % of the time, with the most significant downtime July 26-29 2019, due to a full internal 160 storage issue of the wind lidar. In the following we consider data availability with respect to the uptime of the wind lidar. In Fig. 5(a) the overall availability of the QC 10-minute averaged wind data is shown by the filled bars, which ranges between 96.8 % and 98.4 %. The wind speed distribution of the 2 % to 3 % of missing wind lidar data is shown in Fig. 5(b). The lowest wind speed class (<4 m/s) shows the largest reduction in QC data, especially for the upper levels, while for moderate wind speeds (8 m/s-16 m/s) the reduction is the least.

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In Fig. 5(a) also the availability under "fair weather" conditions are given (open bars), which are very close to 100 %. Fair weather is defined here as no precipitation, visibility at 2 m in terms of meteorological optical range (MOR) more than 5 km and first cloud base height more than 1 km, which accounts for 58 % of the data. We will now take a closer look at the possible meteorological conditions that cause the decrease in QC data. Note that the meteorological data considered here are also 10 minute averaged.

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In Fig. 6(a) and (b) the QC data availability is shown for different classes of precipitation intensity (as measured by the rain gauge) and presence of fog or low clouds (based on visibility measurements in the mast), respectively. The occurrences of the classes are given by the percentages between brackets. The different measuring heights are indicated by the colors. We notice that light precipitation, up to 0.1 mm/h, hardly affect the QC data availability. Only from an intensity of 1 mm/h onwards we observe a significantly reduction, but mostly for the lower measuring heights. This is related to the height-dependent probe 175 length. At the lower levels (short probe lengths) individual hydrometers can cause huge fluctuations in the return signal strength, which can have a detrimental impact on the wind retrieval. At the upper levels (long probe lengths) individual hydrometers are not resolved.
It is well known that low clouds and fog can limit the wind lidar performance due to the attenuation of the laser light. Here we have defined the fog/low clouds classes on basis on the visibility measurements in the A-and B-mast, which are performed 180 at 7 levels from 2 m to 200 m (see Table 2). Events with precipitation are filtered out. The presence of fog (or clouds) at a certain level is triggered by visibility (MOR) less than 1 km. The (mutually exclusive) classes are: thick fog: fog at all levels; shallow fog: fog up to 80 m height (but not at all of the higher levels); low clouds: fog at 140 m and 200 m (but not at all of the lower levels);

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broken: fog at at least one level (but not fitting in one of the previous classes); none: no fog at any level.
We observe that fog in the lower 100 m (thick and shallow fog) has a detrimental impact on the QC data availability of the upper measuring levels. As fog is typically correlated with low wind speeds, this also explain the relative large reduction of QC data for low wind speeds, as shown in Fig. 5(b). Interestingly, QC data availability at the moderate levels remain high, 190 even under thick fog conditions. Clouds above 100 m do not have much impact. The reason why the low cloud class has more QC data for the upper measuring levels than the broken class might be due to enhanced backscatter from the cloud base compensating the attenuation below the clouds.  For lower wind speeds (2-4 m/s) the slope deviates more from 1 and the correlation is smaller compared to the 4-16 m/s class. Here the mean bias is slightly positive, between 0.03 and 0.13 m/s. A positive bias for low wind speeds has been reported 240 (Courtney et al., 2008), which was related to the inability of the homodyne wind lidar to measure zero Doppler-shift. However, we have found that the wind lidar reported wind speeds down to 0.6 m/s, much lower the minimum wind speed in this class. We note that the accuracy of the cup anemometer is 0.1 m/s in this wind speed region, which is on the same order as the observed mean bias. gradual deviation with increasing wind speed and cannot explain these large differences between the wind lidar and the mast measurements.

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The co-located meteorological observations allow to verify the QC wind lidar data for different weather conditions. In Fig. 9 results of linear regression and the biases are shown for "fair weather", "precipitation" and "fog/low clouds" conditions. "Fair weather" is defined as above (no precipitation, MOR>5 km at 2 m and first cloud base height more than 1 km), for "precipitation" a threshold of 0.1 mm/h is taken, and "fog/low clouds" requires at least one mast level with MOR<1 km, while precipitation events are filtered out. Here wind speed is bounded to the 4-16 m/s range for a more fair comparison, recognizing 255 that different weather conditions may be connected to different typical wind speeds.
The fair weather condition gives overall the best results, while the possible impact of precipitation or fog/low clouds on the data quality is small. Most notable is a more negative mean bias at most measuring heights, up to -0.3 m/s at 200 m for fog/low clouds.

Wind direction 260
The wind direction data of the wind lidar and the mast measurements are compared for five heights. Scatterplots are presented in Fig. 10. The selected wind sector is between 200 • and 250 • , as measured at 10 m. The mean bias and standard deviation are  Author contributions. SK was responsible for the wind lidar measurements, performed the data analysis and wrote the original draft. FB was responsible for the mast wind measurements and post-processing. All co-authors contributed to refining the manuscript text.