2.1 Remote sensing instruments

2.1.1 Cloud observations

Observations of the cloud microphysics were conducted at Wolfgang (1630 m; see Fig. 1) using remote sensing and in situ instruments.

Figure 1Overview of the measurement locations and the experimental setup (a). The geographical location of Wolfgang (black cross) and the surrounding topography is shown in (b). The large-scale wind direction was from the west as indicated by the black arrow. The most relevant mountain barrier is indicated by B1. An enlarged section of the measurement sites (black rectangle in b) and the instrument setup is shown in panel (a). The elevation data were obtained from the digital height model DHM25 of the Federal Office of Topography.

Ground-based remote sensing measurements were obtained with a vertically pointing Ka-band polarimetric cloud radar that operated at 35.5 GHz (Mira-36 METEK GmbH, Germany; Görsdorf et al., 2015). The radar was operated at a pulse-repetition frequency of 6000 Hz and a pulse length of $\mathrm{208}\times {\mathrm{10}}^{-\mathrm{9}}$ s, resulting in a vertical resolution of 31.17 m and a maximum unambiguous velocity range of 25.6 m s⁻¹, which spans from −12.8 to 12.8 m s⁻¹. The return signals of the emitted linearly polarized pulses were detected separately in the co- and cross-polarized planes. For both channels, Doppler spectra are derived from Fourier transformations of the return signals from a series of 512 consecutive pulses, corresponding to a Doppler-velocity resolution of 0.05 m s⁻¹. The final temporal resolution of the acquired cloud radar dataset of 10 s is obtained from incoherent averaging of 100 consecutive Doppler spectra.

The 10 s averages of Doppler spectra are the prerequisite for the subsequent data analysis. The moments of the Doppler spectrum provide information about mean volume radar reflectivity, Doppler velocity and Doppler spectral width, based on which the abundance and turbulent properties of clouds can be inferred (Görsdorf et al., 2015). From the ratio of the co- and cross-polarized signal components, the linear depolarization ratio (LDR) is obtained. During the RACLETS campaign, the minimum detectable LDR, which is defined by the quality of decoupling of both detection channels (Myagkov et al., 2015), was found to be −27 dB. The individual Doppler spectra contain valuable information about the microphysical structure of the observed clouds. They can be screened for the presence and properties of multiple spectral peaks in order to evaluate the abundance of different hydrometeor types. In here, such a peak separation is realized by means of the newly developed peakTree retrieval (Radenz et al., 2019). The microphysical properties of ICNC and size are retrieved with the method of Bühl et al. (2019). Both retrievals are further elaborated on in Sect. 2.3.

Moreover, a 14-channel microwave radiometer (HATPRO, Radiometer Physics GmbH, Germany; Rose et al., 2005) was used to observe vertical profiles of atmospheric temperature and humidity, as well as the column-integrated water vapor content (IWV) and liquid water path (LWP). The atmospheric parameters are derived from the measured multi-frequency brightness temperatures following a statistical approach based on a least squares linear regression model (Löhnert and Crewell, 2003). Previous studies reported retrieval uncertainties on the order of 0.5 to 0.8 kg m⁻² for IWV (Steinke et al., 2015) and 16 g m⁻² for the LWP (Crewell and Löhnert, 2003).

2.2 In situ instruments

2.2.3 INP measurements

Aerosols and INP properties were measured at the valley station of Wolfgang (1630 m) and at the mountaintop station of Weissfluhjoch (2700 m) (Fig. 1; see also Mignani et al., 2021; Georgakaki et al., 2020). Aerosol instruments were connected to heated inlets for measurements of ambient air at each site. Additionally, ambient aerosols were collected approximately every 1.5 h with a high flow rate impinger (Coriolis μ, Bertin Technologies, France; Carvalho et al., 2008) operating at 300 L min⁻¹ for 20 min. The impinger collected aerosol particles larger than 0.5 µm in swirling liquid water, and the aqueous solution was analyzed in drop-freezing instruments in order to obtain INP concentration spectra from 0 ^∘C to approximately −20 ^∘C. The DRoplet Ice Nuclei Counter Zurich (DRINCZ; David et al., 2019) was operated at Wolfgang, and the LED-based Ice Nucleation Detection Apparatus (LINDA; Stopelli et al., 2014) was run at Weissfluhjoch. Both drop-freezing instruments use a digital camera to detect freezing by a change in the light transmission through the aqueous solution. An intercomparison of an ambient aerosol sample between both instruments showed slightly higher INP concentrations for LINDA compared to DRINCZ for temperatures along the here-relevant freezing spectrum (i.e., a factor of 2 for −15 ^∘C $<T<-\mathrm{8}$ ^∘C) (Miller et al., 2020), which can be likely attributed to instrumental differences.

The cumulative INP concentration (INPC) was calculated following Eq. (4) in Vali (2019):

$$\begin{array}{}\text{(1)}& \text{INPC}\left(T\right)=-{\displaystyle \frac{\ln (\mathrm{1}-\text{FF}(T\left)\right)}{V_{\mathrm{a}}\cdot C}},\end{array}$$

where

$$\begin{array}{}\text{(2)}& \begin{array}{rl}& C={\displaystyle \frac{F_{\mathrm{impinger}}\cdot t_{\mathrm{sample}}}{V_{\mathrm{liquid}}}}\cdot C_{\mathrm{stdL}}\text{ and }\\ & C_{\mathrm{stdL}}={\displaystyle \frac{p_{\mathrm{ambient}}}{p_{\mathrm{ref}}}}\cdot {\displaystyle \frac{T_{\mathrm{ref}}}{T_{\mathrm{ambient}}}},\end{array}\end{array}$$

using the temperature-dependent frozen fraction FF(T) (total number of aliquots: 96 at Wolfgang, 52 at Weissfluhjoch), the volume of an individual aliquot V_a (50 µL at Wolfgang, 100 µL at Weissfluhjoch) and the normalization factor C, which converts the concentration to standard liters of ambient air. C was calculated for each sample by considering the flow rate of the impinger F_impinger (300 L min⁻¹), the sampling time t_sample (usually 20 min), the end volume of the liquid V_liquid (approx. 15 mL) and the conversion factor from liters to standard liters C_stdL (including the ambient temperature T_ambient and pressure p_ambient at each site and the reference temperature T_ref=273.15 K and pressure p_ref=1013.25 hPa). According to the specifications above, the minimal detectable INP concentration (limit of detection) at Wolfgang was $\mathrm{6.3}\times {\mathrm{10}}^{-\mathrm{4}}{\mathrm{stdL}}^{-\mathrm{1}}$ and at Weissfluhjoch $\mathrm{3.5}\times {\mathrm{10}}^{-\mathrm{4}}{\mathrm{stdL}}^{-\mathrm{1}}$.

Figure 2Overview of the synoptic weather situation on 8 March 2019, showing a satellite picture taken over Europe at 12:00 UTC (a, EUMETSAT) and the vertical temperature profile measured by a radiosonde (12:00 UTC) launched from Payerne (b, MeteoSwiss). The boxplots in (b) indicate the temperature measured at the weather stations of Davos (DAV, 1600 m), Gotschnagrat (GOT, 2300 m) and Weissfluhjoch (WFJ, 2700 m) during the passage of the cloud system. The blue dot indicates the cloud top temperature (−21 ^∘C) and cloud top height (4700 m), which were estimated from the cloud radar observations averaged between 16:00 and 18:00 UTC. The wind barbs are shown on the right side.

2.3 Retrieval of cloud properties and Doppler spectra analysis

4.1 Overview of the microphysical cloud structure

An overview of the observed microphysical cloud structure is shown in Fig. 4. The radar reflectivity shows that the precipitation began at 15:10 UTC and was convective in nature (Fig. 4a). At around 17:30 UTC, the reflectivity increased at all altitudes, and the highest precipitation rates were observed at the surface (Fig. 4e). The period of high reflectivity (>10 dBZ) lasted for about 1 h. After this period, the cloud top lowered from 5000 to 2800 m, and the precipitation ended shortly after 18:40 UTC. The bulk of the precipitation originated at cloud top, as can be seen from the fallstreak pattern of enhanced radar reflectivity (>10 dBZ; Fig. 4a). The contour frequency by altitude diagram (CFAD; Fig. 5) of the radar reflectivity (Fig. 5a) indicates a rapid increase in the radar reflectivity near cloud top, suggesting that the ice crystals were formed in the layer between 5000 and 4000 m. The ice crystals rapidly grew to large sizes between 4000 and 3000 m before they partly sublimated in the layer between 3000 and 2000 m, as indicated by the decreasing radar reflectivity (Figs. 4a and 5a) below 3000 m (assuming horizontal homogeneity). The majority of upward motion was observed above 3500 m (Figs. 4b and 5b). It is important to note that the measured vertical Doppler velocity is the sum of the particle fall speed and the air motion. Thus, as the ice particles grow to larger sizes while falling towards the ground, their fall speed increases and therefore masks the updrafts. The Doppler velocity CFAD shows large variations between −4 to 2 m s ⁻¹ near cloud top (Fig. 5b), indicative of turbulent motions. Indeed, the strong variability in the Doppler velocity was collocated with the enhanced shear layer from the wind profiler (Fig. 3b). Furthermore, the spectral width was also enhanced locally near cloud top (Fig. 4c), which can be attributed to the presence of turbulence (see Fig. 4b) near cloud top.

Figure 5CFADs of the radar reflectivity (a), Doppler velocity (b) and spectral width (c) for the time period between 15:50 and 18:20 UTC. The red line shows the mean vertical profile. The following bin sizes were applied: (1) radar reflectivity from −40 to 30 dBZ in 1 dBZ intervals, (2) Doppler velocity from −4 to 3 m s⁻¹ in 0.1 m s⁻¹ intervals and (3) spectral width from 0 to 1.2 m s⁻¹ in 0.02 m s⁻¹ intervals. A height interval of 100 m was used for all radar properties.

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The occurrence of (1) high radar reflectivity fallstreaks (Fig. 4a), (2) positive Doppler velocities (Fig. 4b) and (3) increased spectral width (Fig. 4c) near cloud top suggests the presence of cloud top generating cells. Cloud top generating cells can enhance ice nucleation and growth and as such have important implications for precipitation formation (e.g., Houze et al., 1981; Hogan et al., 2002; Evans et al., 2005; Ikeda et al., 2007; Crosier et al., 2014; Kumjian et al., 2014; Plummer et al., 2014; Rosenow et al., 2014; Plummer et al., 2015; Rauber et al., 2015), as will be further discussed in Sect. 4.2.

Ice particles that formed within the seeder region interact with other cloud particles while falling through the cloud and thus influence the microphysics of the feeder region below. The low-level cloud structure was observed with the tethered balloon system HoloBalloon (see Fig. 6). The balloon-borne measurements indicate the presence of a low-level liquid layer that was confined to the lowest 300 m of the cloud (see Fig. 6).

Figure 6Vertical in situ profiles of the CDNC (a), ICNC (b) and the IWC $/$ TWC ratio (c). The gray dots in (a) and (b) indicate measurement points which are associated with a liquid water content (LWC) of <0.01 g m⁻³ (for CDNC) or an IWC of 0 L⁻¹ (for ICNC). In (c), red colors represent liquid cloud regions (IWC $/$ TWC < 0.1), light blue mixed-phase cloud regions (0.1 ≤ IWC $/$ TWC < 0.9) and dark blue ice cloud regions (IWC $/$ TWC ≥ 0.9). The cloud radar reflectivity is shown in the background. The numbers in (a), (b) and (c) indicate the mean LWC, IWC and IWC $/$ TWC ratio within the intervals defined by the black vertical lines.

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The cloud droplet number concentration (CDNC) increased from 100 to 350 cm⁻³ between 16:00 and 17:45 UTC (Fig. 6a) before the CDNC decreased after 18:00 UTC. The mean cloud droplet diameter ranged between 8 and 12 µm, as shown by the size distribution in Fig. 7a.

Figure 7Cloud droplet (a) and ice crystal (b) size distributions observed with the HoloBalloon platform. The size distributions were averaged between 17:00 and 17:45 UTC (solid line) and between 18:00 and 18:30 UTC (dashed line).

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The ICNC was in the range of 1 to 4 L⁻¹ between 16:00 and 18:00 UTC (Fig. 6b). ICNCs were higher when fallstreaks of enhanced radar reflectivity reached the surface. During the main precipitation event, after 18:00 UTC, the ICNC increased up to 14 L⁻¹. During the same time period, the ratio between the ice water content (IWC) and total water content (TWC), which is often used to characterize the cloud phase (e.g., Korolev et al., 2003; Lohmann et al., 2016a), increased from 0.05 to 0.3 (liquid to mixed phase) to 0.9 (ice phase). Thus, a transition from a mixed-phase low-level cloud (before 18:00 UTC) to an ice-dominated low-level cloud (after 18:00 UTC) was observed during the passage of the cloud system (Fig. 6c). The cloud radar and microwave radiometer observations suggest that the entire cloud layer glaciated as an increase in the radar reflectivity (Fig. 4a) and a decrease in the LWP (Fig. 4d) was observed after 18:00 UTC. In the absence of sufficiently large updraft velocities for additional cloud droplet activation, the presence of large ice particles or high ICNC can lead to rapid glaciation of the cloud by the WBF process (Korolev and Isaac, 2003).

Even though downward motions were present on the lee side of the mountain barrier (see increased fraction of negative Doppler velocities in Fig. 5b) which contributed to hydrometeor evaporation/sublimation (see decreased reflectivity in Fig. 5a), a persistent low-level liquid layer was observed at Wolfgang. We suggest that this shallow low-level feeder cloud formed due to orographic lifting as the low-level flow in the lee of the mountain barrier was decoupled from the large-scale flow (Fig. 3a) and was forced to rise from Klosters (1200 m) to Wolfgang (1630 m) over the local topography. Similarly, in another case study of the RACLETS campaign, we found that the interaction between local flow effects and topography can induce the formation of updrafts and low-level feeder clouds (Ramelli et al., 2021). It is assumed that this shallow cloud could not generate significant precipitation by itself due to the limited time available for the collision and coalescence of cloud droplets to produce precipitation-sized particles and due to the high temperatures ($>-\mathrm{3}$ ^∘C) which were limiting the amount of INPs and thus ice formed through primary ice nucleation. However, the hydrometeors that formed in the generating cells can “feed” on the low-level liquid layer and thus enhance precipitation by riming and depositional growth. Additionally, it can provide an environment favorable for the production of secondary ice particles, as will be discussed in Sect. 4.3.

4.2 The origin and growth of ice crystals in cloud top generating cells

Observations from the cloud radar, microwave radiometer, HoloBalloon platform and ground-based aerosol measurements were combined to study the microphysics within cloud top generating cells. Since no in situ observations within generating cells or near cloud top were available during the RACLETS campaign, the analysis of the microphysics was limited to observations from remote sensing instrumentation and balloon-borne in situ measurements near cloud base. In the first part of this section, the overall dynamical and microphysical structure of generating cells is characterized, whereas in the second part the origin of ice crystals and the microphysical growth processes active within generating cells are investigated from an INP-cloud perspective.

When the strongest generating cells were present, vertical overshooting of up to 500 m was observed at the cloud top (Fig. 8; e.g., at 16:00 and 16:45 UTC), indicating the presence of strong updrafts.

Figure 8Time series of the radar reflectivity (a), maximum Doppler velocity (b) and number of peaks (c). The black line in panel (a) shows the 17:00 UTC fallstreak, and the dashed lines indicate the regions inside (GC_in) and outside (GC_out) of the 17:00 UTC fallstreak which were used for the analysis in Fig. 9. The maximum Doppler velocity was derived from the Doppler spectra (see Appendix A). The number of peaks were obtained from the peakTree analysis (see Sect. 2.3.2). The evolution of the cloud top anomalies is shown in (d). The radar reflectivity (blue line) and spectral width (red line) were averaged over 600 m from the cloud top. The anomalies were normalized to the mean value, which is indicated in panel (d). The results of the Spearman's rank correlation are shown to the right of panel (d), with ρ indicating the correlation coefficient and pv the p value of the Spearman's rank correlation.

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This was also supported by observations of the maximum Doppler velocity (Fig. 8b), which was derived from the Doppler spectra (see Appendix A) and used as a proxy to identify updraft regions. The maximum Doppler velocity suggests that the strongest updrafts were present in the core regions of the cloud top generating cells (>3 m s⁻¹), whereas updrafts were weaker outside of the generating cells and at altitudes below 3000 m (Fig. 8b). It is likely that liquid water was produced in these updraft cells as a positive correlation was found between the vertically integrated maximum Doppler velocity and the LWP measured by the microwave radiometer (see Fig. A1b). Moreover, anomalies in the cloud top properties and the LWP were observed during the periods with generating cells (Fig. 8d). Coinciding peaks in the anomaly signal were labeled as GC1 (16:00 UTC), GC2 (16:45 UTC) and GC3 (17:55 UTC). The Spearman's rank correlation coefficients of the anomalies ranged between 0.46 (for reflectivity and spectral width) and 0.73 (for reflectivity and LWP), significant at the 5 % level. Thus, given the significant correlation between updrafts, LWP and radar reflectivity within generating cells, it is likely that the updrafts acted as a major driver for the formation and maintenance of generating cells by providing a continuous source of liquid water and thereby enhancing ice nucleation and growth through immersion freezing, subsequent vapor deposition and riming.

To further explore the microphysics within cloud top generating cells, the Doppler spectra along the 17:00 UTC fallstreak were investigated (Fig. 9). This approach allows us to obtain a continuous picture of the evolution of the particle populations along the fallstreak and to draw conclusions regarding the microphysical processes active. Previous studies used the Doppler spectra information for the classification and characterization of ice particle shape and particle populations (e.g., Myagkov et al., 2016; Bühl et al., 2016).

Figure 9Vertical profile of the Doppler spectra along the 17:00 UTC fallstreak averaged over 1 min (indicated by black line in Fig. 8a). The Doppler spectra at three selected heights are shown on the right: 4180 m (within turbulent layer), 2810 m (at mountain barrier height) and 1910 m (at balloon flight height). The red line indicates the Doppler spectrum inside the 17:00 UTC fallstreak (GC_in), whereas the gray spectrum was measured before the fallstreak (GC_out in Fig. 8a).

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The vertical profile of the Doppler spectra shows a broad particle distribution spanning from −5 to 4 m s⁻¹ between 3300 and 5000 m height, indicative of a turbulent layer. This layer likely marked the extent of the generating cell where ice crystals were produced and initial growth occurred. The Doppler spectra show a spectral bimodality below 3300 m (Fig. 9; i.e., presence of multiple particle populations with different fall speeds) which extends down to the surface. When analyzing the Doppler spectra of the full period with the peakTree technique (Sect. 2.3.2), multi-peaked situations become evident at the leading edges of the fallstreaks (Fig. 8c). For example, the Doppler spectrum in Fig. 9b (red line) indicates the presence of two particle populations: a fast falling one (−2 m s⁻¹) and a slow falling one (−0.5 m s⁻¹). The LDR of the slower falling particle population was slightly higher (−25 dB; not shown) compared to the faster-falling population (−28 dB; not shown). These LDR values are characteristic for oblate or plate-like particles (Myagkov et al., 2016). The observed Doppler spectra and the ice habits observed near cloud base (Fig. 13) suggest that the faster falling population represents heavily rimed ice particles and/or graupel, whereas the slower falling population was associated with stellar dendrites. This is also consistent with the observed temperature (dendrite regime; Magono and Lee, 1966; Bailey and Hallett, 2009) and the presence of supercooled liquid (riming) within the generating cells. It is likely that these two particle populations were already present above but only separated below the turbulent layer due to the weaker updrafts and their difference in fall speed.

In the following, we will further investigate the origin of ice particles that formed within generating cells. Numerous studies have observed enhanced ice formation and growth in these updraft regions (Houze et al., 1981; Hogan et al., 2002; Plummer et al., 2014; Ikeda et al., 2007; Crosier et al., 2014; Kumjian et al., 2014; Rauber et al., 2015). For example, Plummer et al. (2014) found that the ICNC was enhanced by a factor of 2 to 3 within the core region of generating cells compared to the region between the cells. While most of the studies agree that radiative cooling is a major driver for the formation and maintenance of cloud top generating cells, less research has focused on the reason for the enhanced ICNCs that were observed within these cells. Here we provide potential reasons from an INP-cloud perspective and propose possible mechanisms by considering the measured INP concentrations and cloud base observations of the ICNC and ice particle size.

INP concentrations were measured at the valley site of Wolfgang (1630 m) and at the mountaintop station of Weissfluhjoch (2700 m) (Fig. 10).

Figure 10INP concentrations measured at Wolfgang (1630 m, circle) and Weissfluhjoch (2700 m, triangle) for different temperatures and times, as indicated in the legend. The cloud top temperature of −21 ^∘C is shown by the vertical dashed line. The dark gray line is a fit to the INP concentrations measured at Weissfluhjoch at temperatures between −9 and −17 ^∘C (pre-cloud INP conditions). The gray shaded area shows the 95 % confidence interval of the fit which was used as an estimate of the upper and lower bounds of the INP concentration (see Fig. 11).

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The observed INP concentrations at a given temperature spanned over 1 order of magnitude. The INP concentration measured at 07:15 and 09:30 UTC was a factor of 3–10 higher at Wolfgang compared to Weissfluhjoch, which was presumably a consequence of the decoupled low-level flow (see Fig. 3a) and thus the sampling of different air masses. Based on the INP measurements at Weissfluhjoch, an INP concentration of 0.27 L⁻¹ (0.15 to 0.48 L⁻¹) was extrapolated at cloud top (Fig. 10). It is important to note that the cloud top INP concentration was estimated from the Weissfluhjoch measurements in the morning (i.e., representative for pre-cloud INP concentrations) as no INP concentrations were measured at Weissfluhjoch during the passage of the cloud system.

Additionally, cloud measurements were conducted by the HoloBalloon platform near cloud base. Since no in situ observations were available within the generating cells, assumptions regarding the upper-level cloud properties were required. We assumed that the largest ice particles (>400 µm; derived from particle size distribution in Fig. 7b) formed near cloud top and grew to these large sizes while falling to the surface. This criterion is based on the assumption that the large ice particles did not sublimate completely prior to reaching the surface. The ICNC_>400 µm observed near cloud base was in the same order of magnitude as the radar-retrieved ICNC at cloud top (red dots in Fig. 11). This observation further supports the assumption that ice particles larger than 400 µm originated near cloud top.

The comparison between the observed ICNC_>400 µm and the estimated INP concentration at cloud top shows a discrepancy between the INP concentration and observed ICNC during certain time periods (Fig. 11). This suggests that the observed ICNC cannot be solely explained by primary ice nucleation, but that other mechanisms were active.

Figure 11Time series of the ICNC_>400 µm (blue line) measured near cloud base by the HoloBalloon platform. Ice particles larger than 400 µm in diameter were assumed to have formed near cloud top. The blue shaded area indicates the uncertainty of the ICNC. No measurements were available between 17:50 and 18:00 UTC. The estimated INP concentrations extrapolated to −21 ^∘C (cloud top, solid line) and to −24.6 ^∘C (convective overshooting, dashed line) are indicated by the black horizontal lines. The gray shaded areas show the upper and lower bounds of the estimated cloud top INP concentration (dark gray) and of the estimated INP concentration at −24.6 ^∘C (light gray) (estimated from the 95 % confidence interval of the fit in Fig. 10). The cloud top ICNCs retrieved from the radar observations (Sect. 2.3.1) are shown by the red dots. The reported ICNCs represent an average over the top 10 range gates (300 m from cloud top) for three different time periods (14:30–17:00 UTC, 17:10–17:45 UTC, 17:45–18:30 UTC). The vertical red lines indicate the error in the retrieved ICNC, whereas the horizontal red lines mark the extent of the time periods.

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Static instability driven by cloud top radiative cooling can produce strong updrafts (Fig. 8b) and lead to convective overshooting of cloud top generating cells (see red arrows in Fig. 8a). This convective overshooting can decrease the cloud top temperature and therefore increase the ICNC formed by primary ice nucleation. For example, the cloud top height during GC1 increased by 500 m from 4500 to 5000 m. Considering the observed temperature profile in Fig. 2b, the cloud top temperature decreased by 3.6 ^∘C from −21 ^∘C (at the average cloud top height) to −24.6 ^∘C (at 5000 m) upon convective overshooting. Consequently, the estimated INP concentration increased by a factor of 3.1 from 0.27 L⁻¹ (0.15 to 0.48 L⁻¹) to 0.85 L⁻¹ (0.42 to 1.7 L⁻¹) (Fig. 10) due to the lower cloud top temperature. The ICNC_>400 µm measured at cloud base lied below or near the extrapolated INP concentration at −24.6 ^∘C before 18:00 UTC (Fig. 11). This suggests that the observed ICNC_>400 µm before 18:00 UTC can be solely explained by primary ice nucleation and convective overshooting. After 18:00 UTC, the ICNC_>400 µm measured near cloud base lied several factors above the convective overshooting line (Fig. 11), suggesting that other processes were occurring.

For example, the positive feedback between supercooled liquid water, radiative cooling and turbulence that has been observed near cloud tops (e.g., Morrison et al., 2012) might have contributed to enhanced ice formation. The presence of supercooled liquid can lead to strong longwave radiative cooling (e.g., Possner et al., 2017). This radiative cooling decreases the stability near cloud top, which causes turbulent motions that in turn can produce further supercooled liquid water. The magnitude of the longwave radiative cooling strongly depends on the cloud phase, the liquid water content (LWC) and particle size distribution, among other factors (e.g., Turner et al., 2018). Indeed, the LWP, as measured by the microwave radiometer, was enhanced within generating cells (see Fig. 8d) and thus likely increased the longwave radiative cooling at cloud top. The question is by how much the radiative cooling was enhanced within generating cells due to the increased cloud liquid water compared to their surrounding regions. Previous studies observed longwave radiative cooling rates in the range of 1 to 5 K h⁻¹ near cloud top (e.g., Chen and Cotton, 1987; Pinto, 1998; Jiang et al., 2000; Rasmussen et al., 2002; Morrison et al., 2011; Morrison et al., 2012; Possner et al., 2017; Turner et al., 2018; Eirund et al., 2019). Additionally, Turner et al. (2018) computed radiative heating rate (RHR) profiles in the atmosphere as a function of cloud type and LWP by using an observational dataset. According to Turner et al. (2018), an increase in the LWP from 50 to 150 g m⁻² (e.g., GC2 in Fig. 8) in MPCs can increase the longwave radiative cooling rate from around 1.7 to 2.9 K h⁻¹ (ΔRHR = 1.2 K h⁻¹). This could potentially cool the cloud top temperature by 0.3 K if a lifetime of 15 min is assumed for generating cells (i.e., 1.2 K h⁻¹ × 15 min = 0.3 K) and increase the estimated INP concentration from 0.27 to 0.3 L⁻¹ (a factor of 1.1; see Fig. 10). Thus, in the present case study, longwave radiative cooling only plays a minor role in enhancing primary ice nucleation. Nevertheless, longwave radiative cooling is of major importance for the production of radiatively driven turbulence near cloud top and thus for maintaining generating cells.

Figure 12Potential mechanisms that could enhance the ICNC in cloud top generating cells: convective overshooting (left), radiative cooling (center) and droplet shattering upon freezing (right). Their ice crystal enhancement factors for the present case study are estimated at the bottom and further discussed in Appendix B.

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Other mechanisms must be active to explain the increased ICNCs after 18:00 UTC. For instance, the enhanced updrafts in generating cells allow for all hydrometeors to grow to larger sizes. It is unlikely that the larger cloud droplet size would significantly increase primary ice nucleation by immersion freezing, which is the dominant ice nucleation mechanism in MPCs (e.g., Ansmann et al., 2008; De Boer et al., 2011; Westbrook and Illingworth, 2011). However, it can play an important role for SIP. For example, the freezing of drizzle-sized droplets can release small secondary ice particles (e.g., Langham and Mason, 1958; Mason and Maybank, 1960; Lauber et al., 2018; Korolev and Leisner, 2020). This process is known as droplet shattering and has been observed to be strongly dependent on the cloud droplet size and to be potentially effective over a large temperature range (Keinert et al., 2020; Korolev and Leisner, 2020). Previous field studies have observed the presence of drizzle-sized droplets in the size range of 100 to 300 µm in regions of strong vertical updrafts (e.g., Hauf and Schröder, 2006; Ikeda et al., 2007; Lauber et al., 2021). Thus, droplet shattering could increase the ICNC in generating cells by several factors if supercooled drizzle drops are present in the updraft regions. However, in situ observations within generating cells would be necessary to further investigate this hypothesis.

In summary, the increased ICNC in generating cells can be the result of different mechanisms or a combination of several mechanisms. Three possible mechanisms have been proposed in this study, and their potential contributions are summarized in Fig. 12 and further discussed in Appendix B.

Firstly, primary ice nucleation in generating cells can be increased due to convective overshooting or radiative cooling. The ICNC observed before 18:00 UTC can likely be explained by these two mechanisms since the estimated INP concentration and the ICNC_>400 µm measured near cloud base agreed within the same order of magnitude (Fig. 11). For the present study, we found that the ice crystal enhancement factor from convective overshooting (factor 2.2 to 5.2) was larger than that of radiative cooling (factor 1 to 1.4). On the other hand, the ICNC_>400 µm after 18:00 UTC exceeded the estimated cloud top INP concentration by up to a factor of 8, suggesting that SIP processes such as droplet shattering might have been active within generating cells and contributed to the glaciation of the MPC. However, more targeted studies are necessary to understand which mechanisms are responsible for enhanced ice formation and growth within cloud top generating cells. In particular, in situ measurements of the cloud properties within generating cells and their environmental conditions (e.g., temperature, updrafts, INP conditions) are of major importance to address these questions.

4.3 Secondary ice production processes in feeder cloud

Ice crystals that formed in the seeder region can grow by microphysical interactions with other cloud particles while falling through the cloud layer and thus influence the microphysics of the entire cloud. For example, if large ice particles fall through a supercooled liquid layer, they can initiate the glaciation of the cloud layer through the WBF process and/or grow by riming. The total number of peaks in Fig. 8c shows multi-peaked situations below 3300 m, indicating the presence of multiple particle populations with different fall speeds. This suggests that secondary ice particles might be produced in the feeder region of the cloud. In the following, we investigate the importance of ice growth and SIP in the feeder region by analyzing the phase-resolved cloud properties measured in situ with the HoloBalloon platform. In particular, the analysis of the ice crystal habit and size can provide important information about the formation and growth history of ice particles.

Figure 13 shows a representative set of ice particle images observed by HOLIMO as a function of height and time. It can be seen that ice crystal habits varied greatly during the passage of the cloud system.

Figure 13Example images of the ice crystals observed with HOLIMO as a function of height and time. The height-corresponding temperature is shown on the y axis on the right side. The boxes indicate columns (yellow), pristine ice particles (purple), large rimed particles (red), irregular particles (green) and aggregates (blue).

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For example, the images indicate the presence of numerous columns between 17:00 and 17:20 UTC at altitudes above 1780 m (yellow boxes) which are known to grow at temperatures between −3 and −10 ^∘C (Magono and Lee, 1966; Bailey and Hallett, 2009). Furthermore, irregular-shaped particles including ice fragments were abundant (green boxes), consistent with previous studies (e.g., Korolev et al., 1999; Stoelinga et al., 2007). A large fraction of graupel and rimed particles was observed between 17:00 and 17:40 UTC (red boxes). After 18:00 UTC, the ice crystals became more aggregated (blue boxes) and less rimed (see also MASC data in Fig. 14d), suggesting a decrease in the amount of liquid water available for riming. Furthermore, from 18:00 UTC onwards dendrites and broken branches of dendrites were more abundant. Small pristine ice crystals (plates and columns) were present over the entire period (see Fig. 15c and purple boxes in Fig. 13).

The large variability in ice crystal habit and size suggests that the ice crystals have formed and grown in different cloud regions. As discussed in Sect. 4.2, it is likely that the heavily rimed ice particles and large dendrites (Fig. 13) were produced within the seeder region of the cloud and gained mass by riming and deposition while falling through the cloud. On the other hand, the small pristine ice crystals were likely formed within the feeder region of the cloud. Previous studies have found that small pristine ice crystals (<100 µm) were spatially correlated with their environment of origin (e.g., Korolev et al., 2020). For example, it is possible that the observed columns originated within the multi-peaked structures (Fig. 8c) as the temperature below 3000 m was in the temperature regime of columnar growth (Bailey and Hallett, 2009). Pristine plates likely grew in the lowest part of the cloud where the prevailing temperature was above −3 ^∘C. These small ice crystals (<100 µm) could have formed either by primary ice nucleation or by SIP processes within the feeder cloud and rapidly grown by diffusion to larger sizes (e.g., Korolev et al., 2020). The contribution of primary ice nucleation to the observed ICNC can be estimated from the measured INP concentration at Wolfgang (Fig. 10; assuming coupling between the surface and the lower part of the cloud), which was below the minimum detectable concentration at a temperature of −3 ^∘C. Thus, the minimum detectable concentration of $\mathrm{6.3}\times {\mathrm{10}}^{-\mathrm{4}}{\mathrm{stdL}}^{-\mathrm{1}}$ (see Sect. 2.2.3) represents an upper limit for the INP concentration within the feeder region. The ICNC_<100 µm in diameter observed in the feeder cloud (1 to 2 L⁻¹; Fig. 14a) exceeded the estimated INP concentration by 3 orders of magnitude, suggesting that primary ice nucleation alone cannot explain the small ice crystals observed.

Secondary ice production processes are necessary to explain the observed ICNC in the feeder cloud. Since the cloud droplets in the low-level feeder cloud were small (< 50 µm in diameter; Fig. 7a), droplet shattering was likely not the responsible mechanism. However, as the temperature at 1900 m was around −3 ^∘C and large rimed particles (Fig. 14a) and cloud droplets larger than 25 µm in diameter (Fig. 14b) were observed in the feeder cloud, the Hallett–Mossop process may have been active (Hallett and Mossop, 1974; Mossop, 1978).

Figure 14Time series of the ice (a) and liquid (b) cloud properties measured by the HoloBalloon platform. The left y axes in (a) and (b) show the total cloud particle concentrations (a: ICNC, b: CDNC), whereas the other cloud properties are displayed on the right y axes. The shaded areas indicate the uncertainty of the concentrations. The dashed line in (b) shows the altitude of the balloon. The temporal evolution of the ice habit fraction is shown in (c), HOLIMO, and (d), MASC (see Sect. 2.2.1 for more information about the classification). The total counts during the 10 min interval are indicated by the black dots. Example ice particles are shown on the right. Shaded areas in (c) indicate particles with a higher degree of riming.

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Another mechanism that could have led to the production of secondary ice particles in the low-level feeder cloud is ice particle fragmentation upon ice–ice collisions (e.g., Vardiman, 1978; Takahashi et al., 1995). As the low-level liquid layer contained small pristine and large rimed ice particles (Fig. 14a), which have different terminal fall velocities and therefore enhanced collision efficiencies, collisional ice fragmentation may have been occurring. Indeed, the ice crystal images in Fig. 13 indicate the presence of ice fragments (e.g., broken-off arms of dendrites after 18:00 UTC). Based on the temporal evolution of the cloud properties within the feeder cloud (Fig. 14a, b), which shows an increase in the ICNC and a decrease in the number of large cloud droplets after 18:00 UTC, we suggest that ice particle fragmentation upon collision was the dominant SIP process after 18:00 UTC. In contrast, the presence of large cloud droplets (>25 µm) before 18:00 UTC suggests that both the Hallett–Mossop process and collisional ice fragmentation contributed to the observed ICNC.

Previous studies have observed large discrepancies between the INP concentration and ICNC in the feeder region of clouds (e.g., Rogers and Vali, 1987; Lloyd et al., 2015; Beck et al., 2018; Lowenthal et al., 2019). These observations were frequently conducted at mountaintop research stations or near mountain slopes where ICNCs of several hundreds to thousands per liter have been reported (e.g., Rogers and Vali, 1987; Lloyd et al., 2015; Lowenthal et al., 2019). These large ICNCs were attributed to the influence of surface processes such as blowing snow (Rogers and Vali, 1987, Geerts et al., 2015), hoar frost (Lloyd et al., 2015), riming on snow-covered surfaces (Rogers and Vali, 1987) or ice crystal enhancement through turbulence and convergence (Beck et al., 2018), whereas the contribution of SIP processes has been suggested to be minor or has been difficult to assess (Lloyd et al., 2015, Beck et al., 2018). By performing balloon-borne measurements in a mountain valley, we measured ICNCs that were 2 orders of magnitude lower than previous observations at mountaintops (1 to 10 L⁻¹ instead of 100 to 1000 L⁻¹) and thus were able to significantly reduce the impact of surface processes. Based on the estimated INP concentration (Fig. 10) and observed ICNC (Fig. 14a), we suggest that SIP processes contributed up to 1–2 L⁻¹ to the observed ICNC in the presented case study and thus accounted for up to 50 % of the total ICNC before 18:00 UTC. However, the increase in the ICNC from 3 up to 14 L⁻¹ after 18:00 UTC (Fig. 14a) cannot be solely explained by SIP within the feeder cloud since the observed increase was primarily due to large ice particles (>300 µm; see Fig. 7b). As a substantial fraction of aggregates after 18:00 UTC are dendrites and broken-off arms of dendrites, the ICNC might be attributed to ice–ice collision breakup within the seeder region. Interestingly, the discrepancy between the INP concentration and ICNC observed in the present study after 18:00 UTC (factor of around 8; see Sect. 4.2) is strikingly similar to the ice multiplication factor of dendrites previously observed at Jungfraujoch in winter clouds (Mignani et al., 2019) even if both studies applied different approaches to determine the enhancement factor and to reduce the influences of surface processes.

If only a small concentration of secondary ice particles is captured by updrafts or turbulence within the feeder region and lifted aloft, they can initiate further ice formation and growth at temperatures well above typical INP activation temperatures and have a significant impact on the development of the cloud (e.g., cloud properties, glaciation, lifetime). While the CDNC and CDNC_>25 µm decreased above 1850 m, the vertical profiles of the ICNC showed no height dependence over the 200 m height interval (Fig. 15).

Figure 15Mean vertical profile of the liquid and ice properties measured in the low-level feeder cloud between 16:45 and 17:45 UTC with the HoloBalloon platform. The shaded areas indicate the uncertainty of the concentrations.

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This suggests that SIP was active over the entire low-level feeder cloud. However, due to the limited vertical extent of the profiles, we cannot make a final statement regarding the impact of SIP within the feeder region on the cloud microphysics aloft. Further observations in “surface-decoupled” environments (i.e., reduced influence of surface processes) with a larger vertical extent are required to assess the role of SIP in feeder clouds. This is important as secondary ice production in the feeder region can potentially enhance orographic precipitation.