The lidar used here is a HALO Photonics Stream Line scanning micro-pulsed Doppler lidar (http://halo-photonics.com/; ) that was located on the roof of a container above the laboratory space on the foredeck of Oden (Fig. , left) at a height of 12 ma.s.l. The instrument operates at a wavelength of 1.55 µm and uses cloud droplets and aerosols as a tracer for air motion. Cloud layers with significant droplet concentrations produce full signal attenuation, prohibiting the acquisition of data above. Such liquid layers were frequently found near 400–500 ma.s.l. in this Arctic marine environment. The lidar's range resolution is 18 m, with 533 range gates and a first measurement at 18 m; the Doppler velocity resolution is 0.038 ms-1. An overview of the system parameters is presented in Table . Throughout the cruise the lidar was configured to undertake several different scan patterns on a fixed cycle. The wind profile scan ran every 10 min, using a five-point measurement: a vertical beam and four off-vertical measurements at an elevation angle of 70∘ and azimuth angles at 90∘ increments. The measurement of velocity along each beam is comprised of 30 000 pulses at 15 kHz and is thus an average over a 2 s interval. The time taken for the scanning head to move between consecutive beam positions is also approximately 2 s. All the individual measurements used in the calculation of a single wind profile thus take place within a period of approximately 20 s.

The lidar was stabilised against the pitch and roll of the ship by mounting it within a specially constructed motion-stabilised frame (Fig. , right). To minimise the torque required, the whole system is balanced so that the axes of rotation pass through the centre of mass of the instrument. The motion of the ship and of the lidar are measured with two Xsens MTi-700-G attitude and heading reference system (AHRS) sensors rigidly mounted to the outer- and innermost frames, respectively. The motion-control algorithm takes the raw measurements of the rates of rotation for pitch and roll from the ship-frame sensor (nominal accuracy: 0.003∘at 10 Hz). It drives servo motors at the same rate but in the opposite direction to hold the inner frame steady against the pitch and roll of the ship. This will maintain the orientation with respect to the horizontal that it had when the system started up. In order to force the inner frame towards the horizontal, the specified rates of rotation are modified by the addition of factors proportional to the absolute pitch and roll of the inner frame. The measurement of the inner-frame attitude uses the solution calculated internally by the Xsens AHRS via a proprietary algorithm which combines all its raw measurements using a Kalman filter (nominal accuracy: 0.2∘; precision: 0.05∘). The complete measurement and control cycle runs at 10 Hz. Both the lidar data logging system and the motion-control system are synchronised hourly to UTC via the ship's time server. Details on the motion-correction algorithm are given in Appendix .

Approximately half of the ACSE cruise took place in sea ice where the ship's pitch and roll are modest (typically less than 2∘) but ship motions can be sudden when breaking ice; the other half was in open water, sometimes near the ice edge and other times hundreds of kilometres distant. Some extensive periods during the second leg were spent in open water under conditions of moderately high winds (10 m winds up to 17 ms-1) and wave heights up to several metres. The Oden is designed primarily for working within sea ice and lacks a keel; it thus has rather poor stability in rough seas and suffers substantial roll motions. Figure a shows a short portion of the time series of roll angles for the ship and the motion-stabilised lidar from 18 September, during the period of roughest seas encountered, when the greatest ship motion was experienced. The probability distribution of lidar roll angle for the 1 h period with greatest ship roll is shown in Fig. b. The maximum ship roll approaches ±8∘ with a period of approximately 8.5 s. The motion-stabilised frame keeps the lidar within 0.3∘ of horizontal 96.5 % of the time, and within 0.5∘ of horizontal 99.3 % of the time. At no point does the lidar roll angle exceed 0.9∘.

The residual attitude and three-dimensional velocity of the stabilised inner frame were calculated and combined with the lidar beam orientation to correct the line-of-sight Doppler velocity measurement for the ship's velocity along the beam. The largest tilts were associated with rotation of the lidar head prior to each new measurement. This changed the centre of mass, inducing a small rotation before the motion-control and logging system detected and compensated for this. In principle the complete platform attitude and velocity solution could be obtained directly from the internal solution of the Xsens AHRS; however, we had no opportunity to calibrate the magnetic field sensors for the hard-iron distortions induced by the ship, and the Xsens compass heading thus suffered significant errors. We therefore utilised a combination of the high-frequency attitude and motion calculated from the raw Xsens AHRS measurements of accelerations (nominal accuracy: 2.5×10-4 ms-1 at 10 Hz) and rotation rates, and low-frequency horizontal velocity and heading from the ship's navigation data; the latter are derived from a GPS and gyro compass and have typical accuracies of order 0.02 ms-1 for velocity and 0.5∘ for heading. We calculate attitude and velocity following the complimentary filtering approach of ; this method is routinely used to motion-correct ship-borne turbulence measurements e.g.. The ship's mean horizontal velocity when underway can approach 6 ms-1; the wave-induced velocity perturbations in open water were a maximum of approximately ±0.2 ms-1 fore–aft and ±1 ms-1 in the vertical and port–starboard.

Because the lidar Doppler velocities are 2 s averages, we use a corresponding 2 s averaged platform velocity to correct them; the standard deviation of the individual 10 Hz platform velocity measurements is also calculated as a quality control measure, in order to flag measurements for which the ship motion changes substantially during the lidar measurement interval and to provide a measure of the noise added to the Doppler winds by the ship motion.

The signal-to-noise ratio (SNR) of the Doppler velocity measurement depends on the set-up of the instrument and the availability of aerosols and cloud droplets to reflect the laser beam and act as tracers for atmospheric motion. It is used to separate reliable data from signal noise . Previous studies using HALO Doppler lidar suggest a SNR threshold ranging from -18.2 to -23 dB . However, these values were derived from measurements at midlatitudes, where aerosol load is generally much higher than in the Arctic. Following the approach of , we derived a conservative SNR threshold of -16 dB based on measurements under relatively steady wind conditions with a vertical wind velocity close to zero. Applying this threshold to our Arctic observations leads us to reject around 18 % of data that would be accepted with the commonly used threshold of -20 dB. This amounts to rejecting a total of 75 % of all individual data points that do not fulfill the SNR criterion. Note that data rejection varies greatly with altitude and atmospheric conditions, and is greatest in the free troposphere, where the majority of rejected data points lie. This rejection rate is particularly great for our Arctic observations as we cannot rely on the abundance of scatterers that lead to generally better SNR in observations at midlatitudes. It is likely that changing the telescope focal length from infinity to 1–2 km would improve data collection for conditions of low aerosol load as encountered during ACSE. Unfortunately, the older model of HALO lidar used in ACSE did not allow for overlap adjustment as easily as its successor, and this adjustment was therefore not made.

Wind speed and direction were obtained from the motion-corrected HALO Doppler lidar measurements using the five-point geometrical wind solution and the four-point sinusoidal fit method both described in , assuming no major changes or air motion occur within the scanning volume . The vertical wind is retrieved as an independent parameter for the sinusoidal fit . For the four-point sinusoidal fit method, we applied the quality assurance criteria described in ; i.e. we tested for horizontal homogeneity of the wind field Eq. 13 inwe applied a threshold of R2>0.95 and the collinearity of the Doppler velocity measurements used within one scan Eqs. 14 and 15 in. For the latter, we ensured that measurements with gaps in the azimuth scan larger than 210∘ were not included for further data analysis, i.e. scans with fewer than three out of four points available for analysis.

We also investigated the influence of changes in the heading of the ship during individual scan cycles with and without applying the quality assurance criteria of . These criteria remove any dependence of wind speed on the change in the ship's heading between the first and the last measurement used in the retrieval. The issue will be addressed in more detail in Sect. .

Continuous wind profiles could be retrieved up to a maximum altitude of 1600 ma.s.l. during cloud-free conditions. Data coverage is greatest in the lowermost 200 m (93 %) and decreases exponentially with height to 13 % at 1000 m. This is less than usually observed with the same type of instrument at midlatitudes due to the generally very low aerosol load in the Arctic e.g.. However, within the atmospheric boundary layer we yield data coverage comparable to previous studies. The average daytime maximum and minimum atmospheric boundary layer depths during the cruise were 576.3 and 81.4 m, respectively. Averaged over all observations, we found that the maximum height of useful wind data was around 100 m lower for cloudy (but not fully attenuating) conditions than for cloud-free conditions. Most of the observed clouds showed a base height of 300 m or less. Fog was frequently observed during the first leg of the cruise, with fewer fog cases encountered during the second leg. Such conditions typically lead to full attenuation of the lidar signal within the fog. The presence of multiple semi-transparent cloud layers allows for measurements of up to 3000 m on some occasions, leading to data coverage of 5 % at this altitude.

In order to assess the accuracy of the lidar wind retrievals, results have been compared to the 6-hourly radio soundings. We used Vaisala RS92 radiosondes with a nominal ascent rate of 4 ms-1. The manufacturer gives a total measurement uncertainty of 0.15 ms-1 and 2∘ for wind speed and direction, respectively . Wind profiles from lidar and radio soundings were interpolated to a common height grid. In addition, lidar winds were averaged over at least 2 scans in the first 30 min after the launch of the radiosonde.

Figure  shows scatter plots of wind speed and direction for lidar vs. radiosonde using the four-point sinusoidal fit solution at an altitude of 75 ma.s.l.. The lidar and radiosonde wind directions agree very well for the five-point geometrical wind solution (R2=0.96, not shown) and the four-point sinusoidal fit (R2=0.99, see Fig. a). Both methods show negligible bias, while a slightly better standard deviation is found for the four-point sinusoidal fit (Table ).

Extreme outliers (red points in Fig. a and b) were determined through the Grubbs test and excluded from the linear fits. The values at around 0 and 360∘ in Fig. a are related to the periodicity of wind direction and do not represent erroneous measurements per se. The lidar slightly underestimates the radiosondes for winds below about 4 ms-1 and overestimates at higher wind speed – by about 2 ms-1 at wind speeds of 16 ms-1 (Fig. b).

Linear fit parameters for both geometric and sinusoidal fits, as well as the number of measurements for the comparisons shown in Fig. , are given in Table .

Both the five-point geometrical and sinusoidal methods to derive wind speed from the lidar data yield similar results. However, the sinusoidal fit is of advantage for our application as it only requires three input points to provide a solution. This increases the number of measurement intervals that can be used by around 25 %. Consequently, sinusoidal fits have been used in the analysis of Doppler lidar measurements during the ACSE cruise. The blue diamonds in Fig. a and b represent observations during which the motion-stabilisation platform was not operating. While these are only five individual scans, they do demonstrate that an unstabilised instrument suffers far greater scatter in the measurements.

Figure a (blue lines) shows a height-resolved view of the correlation coefficient for the different comparisons between lidar winds and the soundings. The squared correlation coefficient for wind direction is approximately 0.99 and almost constant with altitude up to 400 ma.s.l., decreasing slightly to approximately 0.97 at 700 ma.s.l.. Both bias and standard deviation for wind direction increase with altitude (Table ). This is primarily the result of the drift of the radiosonde and the resulting decrease in collocation of the measurements. For wind speed the squared correlation coefficient's minimum (0.86 to 0.89) is a minimum at the lowest altitudes, and it improves with height to values comparable to those for wind direction. Too few data points are available at altitudes higher than 700 ma.s.l. for an effective comparison.

The squared correlation coefficient for the solution for wind speed improves from 0.86 at 100 to 0.94 at 400 m. In addition, the absolute value of the bias decreases from 0.5 to 0.3 ms-1 (Table ). The change from positive to negative bias with height marks systematically larger wind speed from the radio soundings compared to the lidar measurement. A similar improvement (with respect to values obtained using the quality assurance criterion of ) in the comparison to the radiosonde is obtained when the change in the ship's heading during one scan is restricted to smaller angles of 5 or 10∘ rather than 20∘. This is expressed by the red and black lines for different cut-off angles presented in Fig. . Large changes of the heading of the ship shift the azimuth angles used in the four-point sinusoidal fit and lead to non-uniform intervals; this can lead to a degradation of the fit. Fig. b shows the normalised root mean square error (RMSE) for the comparison of measurements with lidar and sounding at different altitudes. The strongest difference in normalised RMSE for the wind speed comparison is found at lower levels when using quality assurance according to or by restricting the change in the ship's heading. At the uppermost altitudes considered here, using provides lower normalised RMSE (7 %) then the other criteria (11 %). The lowest RMSE for wind speed of 1.5 % is found at 200 and 400 m height. The comparison for wind direction (normalised RMSE of 3.5–5.5 %) shows almost no sensitivity to the quality assurance method and little variation with altitude even though performs slightly better.

There are a number of effects that might influence the measurements at low level. The primary source of discrepancy is likely to be the fundamentally different nature of the measurements. The radiosonde follows a unique trajectory resulting from the sum of its buoyant ascent rate and the motion of the air it ascends through. Within the atmospheric boundary layer, turbulence superimposes chaotic perturbations about the mean flow; the largest-scale eddies might result in very different trajectories depending on the precise time and location of launch. The lidar, on the other hand, calculates a wind profile from the air motions along each beam, which are separated both in time and in space – increasingly so with increasing altitude. These effects, along with the increasing spatial separation between the radiosonde and the lidar, may also cause the slight decrease in the correlation for wind direction with increasing altitude. An additional source of discrepancies is flow distortion around the ship; this would influence low-level measurements from both, with different effects on each due to their different locations.

The radiosonde will furthermore take time to accelerate to the ambient wind speed following launch, and it can suffer from pendulum motions as the tether unwinds after launch see Fig. 3 in. The latter effect can be particularly pronounced during high-wind-speed conditions and might affect the calculation of wind speed from consecutive GPS position estimates. The sondes are also launched close to the ship's superstructure and, depending on the ship-relative wind direction, may have been launched from within the local wind shadow of the ship's superstructure, delaying its acceleration to match the wind; for a short while after launch its motion can also be affected by the turbulent wake of the ship. The radiosondes' low-level winds are also affected by the surface winds entered into the proprietary sounding program; these were obtained from a sonic anemometer mounted on the foremast of the ship (Fig. ), approximately 20.6 m above the surface (12.4 m above the deck). The wind speed measurements are corrected for flow distortion using the results of a computational fluid dynamics (CFD) modelling study of flow over the ship following the approach of and; however for relative wind directions from aft, there will remain biases in these measurements.

Time series of wind speed and direction at an altitude of 100 m as measured with radiosonde and lidar are shown in Fig. . The gap from 4 to 12 August 2014 is due to a change in scanning set-up of the lidar; no retrieval of wind speed and direction is possible during this period. During the first leg, the mean horizontal wind speed was 7.2 ms-1, while a slightly larger value of 9.0 ms-1 was obtained for the second leg. Wind direction showed high variability throughout the cruise. However, extensive periods of northerly and south-easterly winds can be identified. The relative difference presented in the bottom panel of Fig.  shows that wind direction generally agrees within 10 %. A stronger spread of values is found for the relative difference of wind speed with extreme values of up to 50 % difference. For the average wind speed of 6 to 8 ms-1 and above, the difference between lidar and radiosonde is below 20 %. The mean (median) wind speeds at 100 ma.s.l. are 7.9±3.4 (7.7) ms-1 and 8.2±3.5 (8.1) ms-1 for the lidar and radiosonde, respectively. The relative difference in wind speed follows a Gaussian distribution (not shown) that is centred slightly off zero as winds measured by lidar are on average 0.3 ms-1 lower than those inferred from the radio soundings. A time series of the absolute differences in wind speed and direction reveals slightly increased scatter in the second part of the cruise during, which higher wind speed has been observed.

A typical measurement day of 17 September 2014 is presented in Fig. . Marine stratocumulus was present for the entire day, with cloud base heights descending from 400 to 200 m during the course of the day. The depolarisation ratio from the Doppler lidar reveals that ice (high values) is precipitating from liquid water clouds (low values) between 06:00 and 18:00 UTC. This is also in agreement with the measurements of vertical wind speed (negative values equal downward motion due to precipitation) and precipitation rate. Averages of 20 min of Doppler lidar measurements around the times of radiosonde launches at 06:00, 12:00, and 18:00 UTC are used for a profile-to-profile comparison of wind speed and direction in Fig. e and f. No major differences are visible for the height range covered by the Doppler lidar apart from the wind speed in the lowermost 100 m for the 18:00 UTC sonde.

Following the approach of and , an auto-covariance scheme has been used to determine the standard deviation of the vertical wind measurements with the motion-stabilised lidar during ACSE. The difference between the zeroth lag (first term in the auto-covariance) and the first lag gives a measure of the random error. This value has to be compared to the theoretical standard deviation of the instrument. The theoretical standard deviation has been estimated for SNR values between -30 and 16 dB as described in (2010, Eq. 7). We used lidar specifications given in Table  and a signal spectral width of 2 ms-1 . To determine the standard deviation for observations during ACSE, we only considered wind profiles during relatively steady wind conditions, i.e. during periods for which at least four consecutive lidar wind profiles showed a mean vertical velocity within ±0.25 ms-1 of zero.

The observed standard deviation of the vertical velocity as a function of SNR is presented in Fig.  together with the theoretical standard deviation calculated as described above. We find a constant random error range between 0.025 and 0.2 ms-1 above -16 dB. This confirms our choice of a conservative SNR threshold of -16 dB according to the method of . SNRs larger than 2 dB refer to cloud measurements. The error increases for lower SNR as the result of increasing signal noise. The experimental error exceeds the theoretical values but follows the expected change with SNR. reported an error between 0.03 and 0.04 ms-1 for SNR larger than -10 dB and errors that increase up to 0.4 ms-1 at -20 dB. Our findings compare well with Fig. 2c in . The larger standard deviation we observe in our measurements is likely to be the result of the limitations of the motion stabilisation. This includes residual small perturbations about the horizontal and the necessity of correcting the Doppler velocity measurements for ship motion only as an average over the 2 s measurement interval; during this time the high-frequency ship motions, induced by waves or ice-breaking, can change significantly.

In addition to the random error we also investigated the measurements of vertical wind speed for systematic errors. To determine the systematic errors of the measurements, we averaged all data for which steady wind conditions prevailed (about 3000 points at 200 ma.s.l. decreasing down to 200 points at 1000 ma.s.l.). While the average vertical velocity should be close to zero, we derived a mean value of 0.1365±0.005 ms-1 for the height range up to 1000 ma.s.l. This means that measurements of vertical velocity will be slightly biased upwards. This constant bias could be due to flow distortions of the ship that are discussed below.

There are a number of potential sources of real bias in the measured vertical wind. One is the projection of horizontal wind into the vertical wind speed measurement as the result of imperfect motion stabilisation or misalignment of the lidar and AHRS units; the latter is estimated to be less than 0.5∘. A comparison of the output of the internal tilt sensor of the lidar with the XSens motion pack unit was performed during installation to level the two with respect to each other. If the lidar alignment deviates from the horizontal by 0.3 or 0.9∘ (respectively the typical and maximum limits of the motion stabilisation) for a campaign mean wind speed of 8 ms-1, the aliasing effect in the vertical wind will be ±0.04 or ±0.12 ms-1, respectively. A source of a real upward air motion over the lidar is flow distortion over the ship. This is known to be a cause of significant bias in estimates of the mean wind .

A CFD study of flow over the Oden was undertaken primarily in order to correct the wind measurements used to determine surface exchange coefficients from estimates of turbulent fluxes. Here we use the CFD model to examine the biases resulting from flow distortion over the lidar location. These calculations are not used as an active part of the lidar retrieval but rather as a means of assessing lidar data quality. A commercial CFD code, VECTIS , was used to model the three-dimensional flow over the Oden for a wide range of wind directions: every 10∘ from bow-on flow, through beam-on flow on both port and starboard sides. Additional runs were undertaken for flow from 120, 150 and 180∘ from the bow. All runs used a 10 m wind speed of 7 ms-1, close to the campaign mean, with one additional (bow-on) run being undertaken for U10=15 ms-1. In the model the stratification is neutral, free-stream wind profiles are logarithmic with altitude and the far-field vertical velocity is zero. The model domain is centred on the ship and extends 1000 m in length, 1800 m in width, and 250 m in height. The number of computational cells within the domain was around 5 million. The cell size varied throughout the domain, with sizes of 0.12 to 0.25 m at the bow mast instrument location. This increased to around 20 m close to the edges of the domain. A grid independency study showed that a 50 % reduction of cell sizes over the whole domain only resulted in a 1 % change in wind speed bias with respect to the free-stream flow at the bow mast anemometer locations.

The ship imposes a significant obstacle to the flow and forces a strong vertical velocity in the lowest few tens of metres above the lidar, which varies with wind direction (Fig. a). This effect is slightly asymmetric about the bow because the lidar is situated towards the port side. At 75 m, the lowest level of robust Doppler wind measurements, the vertical velocity varies from about 0.2 to 0.4 ms-1 for U10=7 ms-1 and for wind directions for which the superstructure does not directly block the flow; this decreases approximately exponentially with altitude. The mean calculated vertical velocity above 75 m (roughly 3 times the obstacle height) is 0.04 ms-1 for flow onto the bow, increasing to a little over 0.12 ms-1 for beam-on flow. The CFD run with U10=15 ms-1 for bow-on flow showed vertical velocity approximately double that at U10=7 ms-1. The altitude-dependent vertical wind speed behaviour derived from CFD modelling was also observed in the Doppler lidar measurements, though with smaller magnitude probably as a result of temporal averaging of turbulent motions and vertical smoothing.

The vertical velocity at the top of the model domain at 250 m is set to zero; in reality a small vertical velocity might extend above this level. The normalised difference between the horizontal wind speed and the far-field wind at the top of the domain is non-zero but less than 5 % (Fig. b), whereas we would expect it to approach zero. This is due to the ship slightly constricting the flow within the domain. Overall the normalised bias (positive) in horizontal wind speed is less than 2 % for all wind directions at altitudes above 75 m. Note that the CFD model results apply to the mean flow only and do not account for potential effects on turbulent flow.