The cloud microphysics instrumental intercomparison was performed at the Puy-de-Dôme atmospheric measurement station (PUY, 45.46∘ N, ∘ E; 1465 m altitude), in central France, as part of the ROSEA project. The station is part of the EMEP (European Monitoring and Evaluation Programme), GAW (Global Atmosphere Watch), and ACTRIS2 (Aerosols, Clouds, and Trace gases Research InfraStructure) networks where atmospheric clouds, aerosols and gases are studied.

The PUY station is located on the top of an inactive volcano at an altitude of 1465 m rising above the surrounding area, where fields and forest are dominant. The main advantage of the site is the high frequency of the cloud occurrence (50 % of the time on average throughout the year). Westerly and northerly winds are dominant. Meteorological parameters, including the wind speed and direction, temperature, pressure, relative humidity and radiation (global, UV and diffuse), atmospheric trace gases (O3, NOx, SO2, CO2) and particulate black carbon (BC) are monitored continuously throughout the year (for more details see Boulon et al., 2011). Long-term studies have been conducted at the site, in particular of aerosol size distribution (Venzac et al., 2009; Rose et al., 2013), aerosol chemical composition (Freney et al., 2011; Bourcier et al., 2012), aerosol optical properties (Hervo et al., 2014), aerosol hygroscopic properties (Asmi et al., 2012; Holmgren et al., 2014), cloud chemistry (Marinoni et al., 2004; Deguillaume et al., 2014) and cloud microphysics (Mertes et al., 2001).

The ROSEA intercomparison campaign took place from 16 to 28 May 2013 (see Table 1 for the details). Eleven cloudy episodes were sampled, each for several hours. Temperatures were always positive, thus preventing freezing from affecting the measurements. The wind parameters were measured with a Vaisala sonic anemometer and a vane anemometer. Typically, the weather conditions were dominated by westerly winds with speeds ranging from 1 to 22 m s-1. The cloud droplet effective diameter ranged between 10 and 30 µm and liquid water content (LWC) values were between 0.1 and 1 g m-3.

A number of instruments were operated simultaneously on the PUY station roof top sampling platform and in the wind tunnel, providing a description of cloud droplets with diameters ranging from a few micrometers up to 50 µm. Measurements include cloud droplet size distribution, effective diameter, extinction coefficient, LWC and number concentration. This cloud instrumentation is composed of two categories: single particle counters (SPCs) and ensemble-of-particles probes (EPP). Generally, a SPC uses the forward scattering (usually within the angles interval around 4 to 12∘) of a laser beam to detect and size individual particles. The size of a particle is deduced from the power of scattered light using Mie theory; and SPCs provide the number concentration for several size bins (Knollenberg, 1981). The obtained cloud droplet size distribution is used to determine cloud microphysical characteristics. The total concentration N, LWC, effective diameter Deff and extinction coefficient σ are computed using the following equations (Cerni, 1983): Ncm-3=∑Dn(D)Vs=∑Dn(D)S⋅TAS⋅ΔtLWC[gm-3]=π6⋅ρw⋅∑DnDVsD3Deff[µm]=∑Dn(D)D3∑Dn(D)D2σkm-1=Qext⋅π4⋅∑DnDVsD2, where n(D) is the number concentration measured for the size bin of diameter D, ρw is the density of the water. Vs is the sampling volume defined as the product of the speed of the air in the inlet TAS (true airspeed), Δt is the sampling duration and S is the sampling surface. S is computed as the depth of field (DOF) multiplied by the width of the laser beam. The extinction efficiency Qext is considered to be equal to 2 within the droplet size and laser wavelength range. Since the measurement principle is similar for all SPC instruments, the uncertainty in normal conditions is broadly the same: the concentration and LWC have uncertainties of 20 and 30 %, respectively (Baumgardner, 1983; Fevbre et al., 2012). Most of the SPCs are calibrated for size measurements but not for number concentration measurements. Some of the major sources of uncertainties specific to SPCs include (see Baumgardner et al., 1985; Brenguier, 1998; Lance et al., 2010 for more details) the following.

Size resolution limits due to Mie resonance: since the same scattered energy can match with several particle sizes, the sizing resolution is limited. For this reason, the cloud particle sizing has an uncertainty of one size bin, which corresponds to values between 2 and 3 µm depending on the size calibration.

The Forward Scattering Spectrometer Probe (FSSP-100) initially manufactured by Particle Measuring Systems (PMS), Inc. of Boulder, Colorado is the oldest instrument still in use for measuring cloud droplet size distribution. It uses a laser at the wavelength of λ=0.633 µm. Electronic delays and changing VAR corrections need to be accounted for in the FSSP data processing. The operation, the uncertainty, the limitations and the corrections are detailed by Dye and Baumgardner (1984), Baumgardner et al. (1985) and Baumgardner and Spowart (1990). For water droplet clouds, the uncertainty of the derived effective diameter and LWC was estimated as 3 µm and 30 %, respectively (Febvre et al., 2012). According to Gayet et al. (1996), errors in particle concentration can reach 20 to 30 %. In the operating range used at the PUY, the resulting counts were summarized into 15 diameter bins, each of 3 µm width, beginning from 2 µm and ending at 47 µm. During the intercomparison, a commercial pump was employed to aspirate a constant air flow through the FSSP-100. The flow through the pump was monitored with a mass flow anemometer. The air flow speed was set to around 15 m s-1. Theoretically, this flow leads to an air speed through the FSSP-100 inlet of 9 m s-1; that value was employed for the data processing. The FSSP was checked periodically to keep the inlet facing into the wind. It should be mentioned that no conical attachment (horn) was mounted on the instrument during this campaign. It means that the air suction into the FSSP inlet tube can generate curved streamlines leading to potential inertial concentration effects (Gerber et al., 1999).

The SPP-100 is a modified model of the FSSP-100 (manufactured by Droplet Measurement Technologies DMT, Inc., Boulder, USA) with 40 size bins and a revised signal-processing package (fast-response electronic components). Brenguier et al. (1998, 2011) have shown that the SSP-100 noticeably improves the accuracy of the size distribution assessment compared to the FSSP-100 version. The electronic system of the SPP-100 is fast enough to neglect the electronic delay; but the data processing still needs regular VAR corrections.

The Fog Monitor (FM-100) is a Forward Scattering Spectrometer Probe (λ=0.658 µm) placed in its own wind tunnel with active ventilation (Eugster et al., 2006), manufactured by Droplet Measurement Technologies (DMT), Inc., Boulder, USA. The design of the transport tubing (consisting of a contraction part and a wind tunnel) reduces mean flow problems during the sampling and make the FM-100 designed for ground-based studies. For the ROSEA experiments, we used a resolution of 20 channels to describe the size distribution with a diameter range between 2 and 50 µm. Details of the operation of this instrument are given by Droplet Measurement Technologies (2011). According to Spiegel et al. (2012), in extreme conditions such as misalignment with the wind direction, uncertainties in concentration resulting from particle losses, i.e., sampling losses and losses within the FM-100 (such as turbulent deposition, sedimentation and inertial losses in contraction) can be as high as 100 %. The FM-100 has a pitot tube to measure the air speed used in the sampling volume computation. However, as it did not work during the campaign, the sampling speed was set to the constant theoretical value of 15 m s-1. This assumption adds uncertainties to the FM sampling volume.

The CDP is a forward-scattering optical spectrometer (λ=0.658 µm), manufactured by DMT. According to Lance et al. (2010), coincidence can be significant for concentrations as low as 200 cm-3. While an oversizing of 60 % and undercounting of 50 % have been quantified at droplet concentrations of 400 cm-3. The CDP has no electronic delay but data processing needs regular VAR corrections. As a result of Mie oscillations, some size bins were grouped to a total of 24 size bins, from 3 to 49 µm. Two types of CDP were used during the campaign: the first version (CDP1) with original tips and the second version (CDP2) with Korolev tips against possible shattering effects. For both versions no pin hole for reducing coincidence effects was added on the sizer of the CDP.

The second type of instruments used during the intercomparison campaign is the ensemble-of-particles probes (EPP). These instruments sample a large number of particles and measure bulk-average parameters. Particle size distributions are not available. The EPP instrumentation of the campaign was composed of a Particle Volume Monitor (PVM-100) and a Present Weather Detector (PWD-22).

The Particle Volume Monitor (PVM-100, manufactured by Gerber Scientific, Inc., Reston, Virginia) is a ground-based forward-scattering laser spectrometer for particulate volume measurements (Gerber, 1984, 1991). It is designed to measure the LWC, the particle surface area (PSA) and to derive the droplet effective radius (reff). The PVM-100 measures the laser light (λ=0.780 µm) scattered in the forward direction by an ensemble of cloud droplets which crosses the probe's sampling volume of 3 cm-3. The light scattered in the 0.25 to 5.2∘ angle range is collected by a system of lenses and directed through two spatial filters. The first filter converts scattered light to a signal proportional to the particle volume density (or LWC) of droplets; the second filter produces a signal proportional to the particle surface area density (PSA) (Gerber et al., 1994). From the ratio of these two quantities, reff can be derived: reff=LWC3σ. These two filters guarantee a linear relationship between scattering intensity and LWC or PSA for droplets diameter from 3 to 45 µm for the PVM-100 (Gerber, 1991). The extinction coefficient σ is directly proportional to the PSA: σkm-1=0.05⋅PSA[cm2m-3]. According to Gerber et al. (1994), the uncertainty of LWC is 10 % for this diameter range. The airborne version of the PVM is the PVM-100A which has a different set of filters to enhance sampling volume resulting in a reduced sensitivity to larger droplets. Wendisch et al. (2002) reported higher errors, up to 50 %, when the mean volume diameter (MVD) exceeds 25 µm.

The Present Weather Detector (PWD22) is a multi-variable sensor for automatic weather observing systems. The sensor combines the functions of a forward scatter visibility meter and a present weather sensor. PWD22 can measure the intensity and the amount of both liquid and solid precipitation. As the detector is equipped with a background luminance sensor, it can also measure the ambient light (Vaisala, 2004). This instrument provides the visibility or Meteorological Optical Range (MOR), which is a measure of the distance at which an object or light can be clearly discerned and from which we can deduce the extinction coefficient σ by (Vaisala, 2004) σkm-1=3000MOR[m]. According to Vaisala (2004), the uncertainty of the MOR and σ is 10 %.

The FSSP-100, the FM-100, the PWD and the PVM-100 were operated on the roof of the station, at approximately 2 m above the platform level (see Fig. 1a). The FSSP and the FM-100 were mounted on a tilting and rotating mast, allowing them to be moved manually in the dominating wind direction. The proper alignment of their inlet with the flow was based on the wind direction measurements performed by a mechanical and ultrasonic anemometer placed on a separate mast fixed on the terrace of the PUY station. The data availability of these instruments is shown in Table 1.

In addition to the continuous measurements performed on the roof of the station, the PUY research station is also equipped with an open wind tunnel located on the west side of the building. The wind tunnel consists of a sampling section, 2 m in length, with an adjustable airflow up to 17 m3 s-1, corresponding to the airspeed of 55 m s-1. The applied air speed inside the wind tunnel was between 10 and 55 m s-1. The method of an icing grid (see e.g., Irvine et al. 2001) was used for airflow uniformity measurements. The tests were performed at the maximal airspeed available in the wind tunnel. According to the preliminary results, the variations of the thicknesses and widths of the iced bands were lower than 5 %, i.e., of the order of the uncertainty of the method. Thus, we can reject the hypothesis of the airflow heterogeneity as the cause of the differences between the microphysical measurement data. For additional information about the site description, see Bain and Gayet (1983) and Wobrock et al. (2001). During the campaign, a Forward Scattering Spectrometer Probe SPP-100 model and two Cloud Droplet Probes (CDP1 and CDP2) were installed in the sampling section of the wind tunnel (see Fig. 1b) to characterize the cloud microphysical properties in terms of droplet size distributions and extinction coefficients. Four experiments were performed in the wind tunnel, each with the duration of nearly 2 h (see Table 1).

During the campaign, instruments collected data at a frequency of 1 Hz. In order to synchronize measurements from multiple instruments, data have been averaged over 10 s or 1 min. The length of the averaging time depends on the duration of the experiment, and cloud heterogeneity. The PVM measurements are provided with routine protocol which averaged data over 5 min; thus any comparison with this instrument has to be carried out with 5 min average data. The FSSP shows incoherent measurements from 23 to 26 March, probably due to electronic interferences. An overview of the data availability during the campaign is shown in Table 1. The SPCs were calibrated in size using glass beads, between the 22 and 29 April 2013 before the campaign, and between the 8 and 30 November 2013 after the campaign. The EPPs were calibrated using opaque disk a few days before the beginning of the campaign. The data unavailability is caused by the absence of experiments in the wind tunnel and instrumental problems on the roof. A summary of the instrument characteristics, with uncertainties in normal and extreme conditions, is reported on Table 2.

The purpose of this section is to give an overview of the microphysical measurement strategy performed during the campaign with a focus on the instrument variability. During the 16 May a large number of instruments were deployed simultaneously on the station platform and in the wind tunnel (see Table 1).

Figure 2 provides an example of the temporal evolution of the parameters measured the 16 May. On this graph, we choose to represent only the time series of the cloud properties averaged over 10 s when the wind tunnel was actually functioning. According to Table 1, the PVM did not properly function on this particular day. The wind speed outside and inside the wind tunnel is shown in Fig. 2a. The outside wind speed varied from 2 to 7 m s-1, while the air speed in the wind tunnel was set up to fixed values ranging from 25 to 55 m s-1. The measured cloud parameters displayed in Fig. 2b–d are the effective diameter, the number concentration and the liquid water content of cloud droplets measured by the FSSP and the FM on the roof of the PUY station as well as those ones obtained from the two CDPs and the SPP located in the wind tunnel. The time series of the extinction coefficient derived from these instruments are shown in Fig. 2e. The observed cloud layers were above the freezing level with temperatures almost constant around 1 ∘C. During the sampling period, the dominant wind was blowing westward and the instruments positioned on the mast were oriented accordingly.

The values and the variability of the effective diameter measured by the instruments are in good agreement with a correlation coefficient close to 0.9 (Fig. 2b).

Although the microphysical properties' variability is well captured by all the instruments (correlation coefficient close to 0.9), the temporal evolution of the number concentration exhibits systematic differences among the instruments (Fig. 2c). The number concentration measured by the FM-100 is systematically lower than that one derived from the other instruments, while the FSSPs (SPP and FSSP-100) show the highest values. The ratio between the concentration measured by the FM and the FSSPs reaches values up to 5. As for the CDPs installed in the wind tunnel, the concentration measurements lie between the values obtained by the FSSPs and the FM-100. The two CDPs have a ratio of 1.35 and the CDP 1 has values close (ratio of 1.6) to those of the FM-100. Similarly, the LWC and extinction coefficient values show significant discrepancies. The measured cloud droplet extinctions vary up to a factor of 2.5 (FSSP) and 0.55 (FM-100) compared to the PWD. The bias between the instruments is potentially very important (up to 5 when comparing the FSSPs extinction to the FM-100). However, the temporal variability of the data shows good correlation(R2 close to 0.9).

The red-framed parts of the time series displayed in Fig. 2 correspond to additional experiments where the orientation of the instruments on the mast was changed (the FM-100 and the FSSP). Those orientation changes lead to a sudden decrease of all the microphysics parameters of the instruments installed on the mast, especially of the FSSP. The data corresponding to those orientation experiments are removed for the following analysis and will be discussed in the Sect. 3.4. On the example of 16 May, we observe that the differences in concentrations measured with different probes seem to vary, and may be a function of wind speed and direction.

This example illustrates that the probes' adequate sizing of cloud droplets is subject to a systematic bias when particle counting (number concentration) is involved. This can be clearly seen in Fig. 3 where the average particle size distributions (PSDs) in concentration, surface and LWC measured by the different spectrometer probes are displayed. It should be noted that these average PSDs were obtained when the probe orientations were coaxial with the wind direction. The PSDs in number is a good indicator of the small droplets concentration while the PSDs in surface and volume are more representative of droplets with intermediate and large sizes, respectively.

The PSDs show similar trends and shapes, with size modes from 10 to 14 µm which explains the agreement in the effective diameter values. The FSSP number PSD show a clear overestimation for the particles smaller than 10 µm, compared to all the other instruments. This could be partly attributed to an enhancement of small droplets in the sampling volume of the FSSP due to super-kinetic sampling. The computed average mean volume diameter (MVD) shows similar values with a maximum deviation of 1.3 µm, which is within the instrumental errors. This confirms the good agreement of mean size for all instruments. However, the discrepancies observed in the measured droplet concentration of the PSD are significant and linked to the systematic concentration bias evidenced in Fig. 2. This means that the size bins' partitioning is correct and the number concentration discrepancies are likely to come from an incorrect assessment of the probe sampling volume. In addition, the SPP-100 tends to overestimate the number concentration for the largest particles (larger than 30 µm), compared to the other instruments, especially for the two CDPs of the wind tunnel. One possible explanation could be the effect of splashing artifacts inside the SPP inlet, as evidenced by Rogers et al. (2006). This result highlights the difficulties of accurately deriving the droplets concentration, which was expected due to the lack of simple number calibration for these instruments.

In this section, we focus on measurements performed in the wind tunnel and on the roof of the station when the wind was isoaxial to the sampling probes inlets, over the whole campaign. Microphysical changes, due to the orientation of the instruments, observed in Fig. 2, will not be analyzed here. The data are averaged over 10 s for the wind tunnel measurements and over 1 min for ambient conditions in order to make the measurements comparable (see Sect. 2.3).

Figure 4 displays the scatter plots of the effective diameter for the instruments deployed on the PUY platform. The dashed lines show the uncertainties applied to the linear fit; the errors considered for each instrument are given in Table 2 for normal conditions. There is a good agreement between the FM-100 and the FSSP with a high linear correlation coefficient value (R2=0.94). Additionally, the bias observed between these two instruments is within the “theoretical” measurement errors. The comparison between the PVM, the FSSP and the FM-100 shows that the overall variability of cloud droplet effective diameter is well captured (R2 close to 0.9). Even if the slope of the linear regression is greater than 1, the measurement points are close to the line 1:1 and the scatter is within the measurement uncertainties. Moreover, the comparisons (not shown here) between the PVM and the FM-100 extinction and LWC give a slope a of 2.1 with R2=0.72 and a=2.6 with R2=0.78, respectively. When comparing the PVM and the FSSP 100 the slopes are a=0.35 with R2=0.65 and a=0.4 with R2=0.8 for the extinction and the LWC, respectively. The rather good correlations obtained between the instruments can be explained by the agreement of the effective diameter and size distribution shape for the different instruments.

The comparison between the number concentrations measured coaxially to the wind direction by the FSSP and the FM-100 over the whole campaign is displayed in Fig. 5. The concentration measurements are slightly less correlated than the effective diameter measurements but the correlation remains acceptable (R2=0.8) and most of the points remain in the uncertainty area. However, a significant discrepancy (slope of 0.18 which corresponds to a factor 5.5) between the instrument concentration measurements is clearly evidenced. This ratio is the same as that one obtained when LWC are compared (not shown), thus confirming that the sizing is coherent between the two instruments. The constant bias found for the concentration affects the extinction and the LWC in the same way. We recall that the FSSP was observed to overestimate small particles and that the FM-100 sampling speed was set to a constant value of 15 m s-1 because the speed measured by the pitot tube was unreliable. This can explain the observed differences between measurements. We observe that measurements performed under low wind speed (lower than 5 m s-1) are more scattered compared to those made at high wind speed (Fig. 5). This will be discussed in more detail in Sect. 4.

A comparison between the 5 min averaged extinction coefficients measured by the PVM and the PWD, two instruments that do not need active ventilation, is shown in Fig. 6. There is a good agreement (R2=0.86) and a slope close to 1. The small discrepancies between these two instruments can be attributed to the heterogeneity of the cloud properties and instrumental errors. The points with the low extinction values show largest variations, corresponding to the cloud edge where the properties are the most heterogeneous.

Therefore, the fact that there is a systematic constant bias (factor of 6 in Fig. 5) in the intercomparison of the droplet number concentration and of the LWC, measured by the different probes, could be indicative of the inaccurate assessment of the probe sampling volume directly linked to the air flow speed measurement accuracy. In order to discuss this issue, the measurements performed under ambient conditions are compared with the measurements in the wind tunnel where the sampling speed is recorded more accurately than in ambient air.

Figure 7a presents the results of the effective diameter as the intercomparisons for the three instruments installed in the wind tunnel. Good agreement is observed among the probes, with correlation coefficients R2 always higher than 0.9. The slope of the linear regression is close to 1, meaning that the assessment of this parameter is consistent for the CDPs and the SPP-100, thus confirming the good calibration in diameter.

The measured droplet concentrations (Fig. 7b) also show high correlation coefficients (R2=0.9), comparable to those measured for the effective diameter. However, linear regression analysis shows that the concentration ratio may reach a factor of 2 for the different instruments. It should be noted that these slopes are independent of the air speed applied in the wind tunnel. Even though the discrepancies are less pronounced than those ones for the instruments placed on the platform of the PUY station, a significant bias still exists. This bias may be attributed to the assessment of the probe sampling speed/volume. However, when the biases are taken into account, at least 90 % of the points are within the uncertainty area.

The bias between the instruments results from systematic errors of the assessment of the sampling volume. The single particle counters (SPCs) have uncertainties in optical parameters such as the DOF and in corrections like the activity. In addition, the data of the ground-based FM and FSSP are affected by errors of the sampling speed assessment. In order to evaluate the consistency of measurements performed in ambient air (on the mast) with those performed in a wind-controlled environment, we characterized the relative sensitivity of the droplet concentration measurements to different wind speeds. As already discussed, all the instruments in the wind tunnel are very well correlated. Since only the slope of the linear regression differs from one instrument to another, we chose to compare the FSSP and the FM-100 sampling on the roof, with the SPP100 sampling in the wind tunnel. These instruments are based on the same measurement principle.

Figure 8 displays the scatter plots of the number concentration measured by the instruments on the mast against the SPP observations performed during the four wind tunnel experiments (the 16, 22, 24 and 28 May with the 10 s average measurements). The concentrations measured by the FM-100 are well correlated to the SPP observations even though the wind speeds are quite different, ranging from 2 to 21 m s-1 for external wind and from 10 to 55 m s-1 in the wind tunnel. Additionally there is no clear dependence of the measurements on the wind speed. We can thus conclude that the FM-100, the SPP-100 and the CDPs' coaxial measurements do not seem to depend on the air speed values (ambient wind speed or applied in the wind tunnel). However, a factor of 4 is found between the concentrations measured on the roof by the FM-100 and by the SSP in the wind tunnel (factor of 3 when compared to the CDP1). These high discrepancies can be explained by the sampling volume uncertainties (including errors in the DOF and the sampling speed that can reach 100 %), instrumental errors (around 20–30 % in the concentrations for most of the instruments), potential turbulent and/or anisokinetic flow and the cloud inhomogeneity.

However, the 10 s average FSSP measurements exhibit a high variability and show no correlation with the SPP observations. Both, the inter- and intra-experiment variability is significant, meaning that correction of global data is not possible. Additionally, due to some instrument data availability (see Table 1), the correlation plots relative to the FSSP and the FM-100 are not directly comparable. The 24 May experiment is not available for the FSSP but shows a large variability in concentration, which results in an increase in the correlation of the FM-100 compared to the FSSP. However, as the FM-100 was designed for ground-based measurements, it is not surprising that the FM-100 measurements are more in agreement with the other instruments of the wind tunnel than the FSSP. On the contrary, anisokinetic sampling of the FSSP leads to higher discrepancies when this instrument is compared to other ones.

The droplet diameter and concentration intercomparisons show that the uncertainties linked to the calibration and to the calculation of the sampling volume lead to systematic biases similar to the measurement of concentration, extinction and LWC. The agreement observed between the FM-100, the SPP and the CDP measurements indicates that these data could be standardized on the basis of a reference instrument, with a simple relation of proportionality that would be valid for the entire campaign. However, particular attention should be addressed to the FSSP measurements which were shown to be sensitive to wind conditions. Therefore, the remainder of this study will focus on the standardization of the results, on biases correction for isoaxial measurements as well as on the study of the effect of the air speed (wind speed or suction in the wind tunnel) on the measurements.

To summarize this section, the comparisons showed good correlations between the deduced parameters, that is, good sizing for all the instruments. At the same time, the instruments displayed large discrepancies in their capability to assess the cloud droplet number concentrations. As the FSSP is aspirated with no flow straightener in front of it, turbulent flow and distortion of the size distributions can be expected. Anisokinetic sampling and errors in the sampling volume can explain the concentration overestimation. For the other instruments, the biases were constant during the campaign and independent of the wind speed and the droplet size (not shown). They are attributed to the assessment of the sampling volume. This includes errors in the sampling speed, the laser width and the DOF. The listed uncertainties are very difficult to quantify and they can reach rather high values. Thus, it seems to be a more productive approach to correct the measured data without computation of all the errors related to the sampling volume. The approach is discussed in the following section.

The instrument concentration biases observed in Sect. 3.2 lead to the need to standardize the recorded data. The most natural way is to standardize the measurements with instruments which are not based on single particle counting but on the measurements of an ensemble of particles (i.e., from an integrated value). Such measurements are performed by the PVM-100 and the PWD.

Since good agreement was found between the extinction coefficients measured by the PVM and the PWD (Fig. 6), these two instruments can be used as absolute reference of the extinction of cloud particles. As the PWD was the only instrument working during the entire campaign, all recorded data are standardized according to this instrument. Hence, the data of other instruments were averaged over 1 min according to the PWD time resolution.

Figure 9 presents the comparison between 1 min averaged PWD extinctions and the data obtained in the wind tunnel for all the experiments, as a function of the wind tunnel air speed. The results show good correlations (R2 > 0.7), and the slope of the regression curves corresponds to the correction coefficient applied to the sampling volume of the probes. The dispersion can be attributed to the spatial difference between the instruments on the roof and in the wind tunnel and the instrumental errors. Ratios of 0.44, 0.63 and 1.04 were found for the SPP, the CDP 2 and the CDP 1, respectively. The correlation coefficient for the CDP 1 is lower than for the other wind tunnel instruments as a result of missing data from the 22 May experiment not being available (see Table 1). Accordingly, the number of points and the range of the extinction values are lower. As those coefficients are linked to the modification of the sampling volume and number calibration, they can be applied to the concentration, the extinction and the LWC with a simple relation of proportionality. Moreover, as discussed in Sect. 3.1, and as shown in Fig. 9, air speed in the wind tunnel has no influence on the measured data when the sampling volume correction is taken into account. This agrees with the results obtained for the 16 May and is shown in Fig. 3. However, the measurements performed with an air speed equal to 10 m s-1 were removed from the data set because of the high discrepancies with the PWD observations (R2=0 for the SPP and 0.4 for the CDP 1), meaning that the sampling is inadequate at this speed. For cloud measurements, it is thus recommended to use the PUY wind tunnel with an air speed higher than 10 m s-1.

In a similar way Fig. 10 presents the comparison of the PWD extinctions with the instruments placed on the mast during the campaign, as a function of the external wind speed (right panels). The FM-100 and PWD measurements are correlated, even though the FM-100 extinction is underestimated by a factor of 2 compared to the PWD reference measurements. This factor is of the same order of magnitude as the bias found when comparing the PWD to the instruments positioned in the wind tunnel (Fig. 9). On the other hand, Fig. 10 shows only a poor correlation between the FSSP and the PWD extinction coefficient measurements. Additionally, the wind speed seems to have an influence on the FSSP measurements. Several points, corresponding to low wind speeds, show a large overestimation of the extinction measured by the FSSP. Removing the data corresponding to a wind speed lower than 5 m s-1, leads to a better correlation (R2=0.55) and a slope of 0.4. It should be pointed out that the results remain almost unchanged for the FM-100 when removing the same low wind speed cases. As a consequence, the FSSP seems to be very sensitive to the wind conditions, and this confirms the hypothesis that anisokinetic sampling affects the FSSP measurements, whereas FM-100 inlet system minimizes this effect as much as possible. Indeed, the FM-100 has a transport tube, which allows a more significant aperture angle and ensures a more laminar flow compared to the FSSP. Again, this reveals that low wind speeds contribute heavily towards the amount of scatters so that some physical phenomenon seems to affect the droplet detection (see Sect. 4).

Table 3 presents the summary of the instrumental intercomparison during the ROSEA campaign in terms of the instrumental bias (slope a) and the correlation coefficient R2. In this table, the correlation between two instruments has been computed when the data of the two instruments were available at the same time (see Table 2), with coaxial measurements toward the wind direction, and during stable cloudy periods. One minute averaged data were used to compare the instruments on the roof, while 10 sec averaged data were used to compare instruments when wind tunnel instrumentation is involved. However, due to the time resolution (see Table 1), comparison with the PWD is made at 1 min average and with the PVM at 5 min average. The comparisons between the PVM and the wind tunnel instruments are not representative due to the lack of points. Comparisons with the PWD measurements, in bold, give the coefficient to be applied in order to normalize the data of each instrument. All the instruments, except the FSSP, show at least an acceptable correlation (R2≥0.6) with the PWD during the entire campaign, independently of the meteorological conditions.

Appendix A presents experiments devoted to the assessment of the particle speed inside the FSSP inlet as a function of the wind and suction speed. To summarize briefly, the variations of the particle transit speed was found to not directly depend of the suction speed of the pump. Our measurements showed that the ramming effect (Choularton et al., 1986) was not significant. However, it is shown that the inertial concentration effect (Gerber et al., 1999) can be significant. In addition, average transit speed was found to be dependent of the droplets diameter (see Fig. 13a), with a larger dispersion for small particles (≤ 18 µm). This confirms the hypothesis of the FSSP anisokinetic sampling with potential turbulent fluxes, leading to a bad correlation with the PWD. A correction which would be proportional to the concentration is thus not possible for the FSSP. Moreover, as the extinction comparisons of the SPCs with the PWD provide correlated linear regression, without saturation, the coincidence phenomenon was assumed negligible.

Up to now we have investigated the coherence of performed measurements using the different probes sampling isoaxially to the main wind stream and showed a way to correct and standardize the data. In the following section, we will investigate the effect of non-isoaxial sampling on the measurements.

In this section we focus on experiments where the mast was oriented in different directions with respect to the main wind stream. Each position was maintained during 5 min and the orientation was regularly moved back and forth to an isoaxial position to check if the cloud properties remained unchanged during the experiment. Four measurement series were carried out during 22 May. The wind was blowing west all day long and the cloud properties were rather stable. Despite the sampling anisotropy of the FSSP, the orientation experiments for a given wind speed are reliable.

Figure 11 presents the temporal evolution of the FSSP and FM-100 size distributions along with the wind speed and the deviation angle between the instrument orientation and the wind direction. First, for the measurement with an angle equal to 0∘, the cloud size distribution is almost unchanged throughout the experiment. The FSSP LWC and number concentration are approximately 1 g m-3 and 1000 cm-3, respectively. Notable changes are observed from angles of 30∘. The concentration decreases with increasing angles and with a more pronounced impact for large water droplets (> 15 µm approximately). An impact on the small droplets is also seen for large angles, but appears to be lower at low wind speeds. Indeed, comparing the series 3 and 4 with the average values of wind speed of 7 and 3 m s-1, the size distribution shows more of a decrease in concentration when the wind is strong. The FM-100 shows the same behavior but with a lower sensitivity.

The impact of the combination of both wind speed and direction on the probe's efficiency to sample cloud droplets is clearly illustrated in Fig. 12, which shows the cloud droplet size distribution, averaged for each angle θ and average wind speed. The percentage of the FSSP isoaxial number concentration loss for each angle and wind speed values is shown in Table 4. This percentage is computed for the total size range of droplets, for small and for large droplets, arbitrarily defined as a droplet diameter lower or greater than 14 µm respectively. On average, the greater the angular deviation from isoaxial configuration is, the more the size distribution is reduced, except for a 3 m s-1 wind speed. For wind speed 5, 6 and 7 m s-1, the total percentages displayed on Table 4 go up from 74, 75 and 28 % to 95, 96 and 98 %, respectively. The results also show that, for the same angle of deviation, the percentage increases with increasing wind speed, with only one exception for 30∘ and a wind speed of 7 m s-1. Thus, with increasing wind speed, the total percentage goes up from 88 to 93 % for 60∘ and from 95 to 98 % for 90∘.

However for a wind speed of approximately 3 m s-1, the size distribution shows very small changes. Despite a ratio of about 4 between the coaxial and a deviation angle of 60∘, the size distribution displays the same shape whatever the angle is. Indeed, the particle loss percentages, presented in Table 4 for small and the large droplets, show very small differences compared to the other wind speed values. The size distribution could then be corrected by applying a constant factor. However, for wind speeds higher than 5 m s-1, the FSSP size distribution shape changes and the effective diameter decreases if the instrument is not facing the wind. Table 4 shows that the particle loss percentage for small particles is almost always lower than for larger droplets. This means that the reduction of the measured particle number concentrations resulting from changes in instrument orientation is more efficient for large particles. An inadequate orientation of the mast leads to an underestimation of the effective diameter. Therefore, a simple correction of the size distribution is not possible if the wind is greater than 3 m s-1 and the deviation angle is larger than 30∘.

Table 5 shows the results for the FM-100. For the same wind speed and direction, the values of the FM-100 concentration loss are systematically lower than for the FSSP. This means that the FM-100 undergoes a weaker loss of measured particles when the instruments are not facing the wind. The variations of the FM-100 concentration loss with the wind speed and the angle are less obvious than variations of the FSSP. Moreover, the amplitude of these variations is much weaker than for the FSSP, with a minimum of 15 % and a maximum of 68 %. This confirms that the FM-100 is less sensitive to the wind speed and orientation than the FSSP-100. The experimental data presented in Table 5 corroborate with the modeling results by Spiegel et al. (2012) who investigated the particle losses caused by increasing sampling angle for the wind-velocity range from 0.5 to 6.2 m s-1 and the sampling speed of 5.25 m s-1. Our study shows that the FM particle losses decrease with increasing wind speed for sampling angles lower than 30∘ and increase for sampling angles higher than 30∘. In addition, for any wind speed greater than 3 m s-1, the particle losses increase with particle diameter and sampling angle.