2.1 Surface precipitation measurements

The PIP video disdrometer measures hydrometeor size, fall velocity, an estimate of particle shape and PSD. In this study PIP data are used for characterizing the microphysical properties of the snowfall, which include estimates of the mass-dimensional m(D) relations. The PIP instrument works in the same way as its predecessor, the Snow Video Imager (SVI) (Newman et al., 2009), but using a camera with a higher frame rate of 380 frames per second. The 2-D grayscale images of falling particle are obtained, when it falls between the camera and the lamp (distance between the two is 2 m) and from these multiple images the particle fall velocity is derived. The camera focal plane is at 1.3 m and the field of view is 64 mm × 48 mm with a resolution of 0.01 mm². Sampling volume of PIP depends on particle size and fall velocity (Newman et al., 2009). For each particle, the PIP processing software automatically records the disk-equivalent diameter D_Deq, which is the diameter of a disk with the same area as the particle shadow.

Particles smaller than 14 pixels (approximately D_Deq < 0.2 mm) or particles only partly observed or out of focus (blurred) are rejected by the software (Newman et al., 2009). Because of the blurring effect, the sizing standard error is estimated to be 18 % (Newman et al., 2009). Also, other shape-descriptive particle parameters are retrieved with the image processing software (National Instruments IMAQ) such as particle orientation, total area, and bounding box width and height. Particle fall velocities are recorded as a function of D_Deq and values are considered reliable if there are more than two observations of the identified particle and values are higher or equal to 0.5 ms⁻¹ (PIP software release 1308). In later software versions the fall velocity threshold is removed. The PIP dataset includes PSD in ${\mathrm{m}}^{-\mathrm{3}}{\mathrm{mm}}^{-\mathrm{1}}$ for every minute. The PSD is also determined as a function of D_Deq and subdivided into 105 bins (from 0.125 to 25.875 mm) with the last bin containing particles larger than 25.875 mm. The observed maximum diameter D_max for each particle is determined by fitting an ellipse inside the bounding box with considering the particle orientation angle as explained in von Lerber et al. (2017), and the mean ratio between D_max and D_Deq is approximately 1.38. For simplicity, D will be used hereinafter to replace D_Deq.

In this study 5 min time series of the observed PSD, the fitted v(D) and the retrieved m(D) relations are utilized (Tiira et al., 2016; von Lerber et al., 2017) as a function of the diameter D. Typically during the 5 min period 10³ particles are observed. The PSD is averaged from 1 min observations, after spurious particle records are filtered out. The v(D) relation is derived by a linear regression fit in the log space for the observed particles during every 5 min (Tiira et al., 2016). The m(D) relation is retrieved by utilizing the general hydrodynamic theory (Böhm, 1989; Mitchell and Heymsfield, 2005), where a snow particle mass is computed from the observed dimension, fall velocity and area ratio of a snow particle. The PIP observes falling particles from the side, whereas the particle dimensions projected to the flow are needed for the hydrodynamic calculations. In von Lerber et al. (2017), errors associated with the observation geometry, and also with the measured PSD were addressed by devising a simple correction procedure; the value of the correction was chosen for each snow event by comparing the estimated liquid water equivalent (LWE) accumulation to precipitation gauge measurements. Similar to the v(D) relation, the power-law m(D) = $a_m{D}^{b_m}$ fit is determined by a linear regression fit in the log space for the computed particle masses every 5 min. The uncertainty in the retrieved factors of m(D) relation are discussed in detail in von Lerber et al. (2017).

The weighing precipitation gauge, OTT Pluvio² 200, records every minute the bucket weight expressed in mm. The gauge is located on a platform at 2 m height surrounded by a double wind fence similar to Double Fence Intercomparison Reference (DFIR) fence (Goodison et al., 1998). In addition, the gauge has a Tretyakov wind shield. The Hyytiälä measurement site is surrounded by boreal forest, and therefore the wind effects are usually moderate. The PIP measurement volume is open and typically affected less by the wind than instruments with enclosed sampling volumes (Nešpor et al., 2000). Therefore, in these wind conditions, the expected wind-induced errors are expected to be small.

2.2 AMF2 two-channel MWR

The AMF2 two-channel MWR, located 20 m away from PIP, is a sensitive microwave receiver that provides time-series measurements of column-integrated amounts of water vapor and liquid water. Two channels, respectively 23.8 and 31.4 GHz, allow simultaneously to obtain water vapor and liquid water along line-of-sight (LOS) path. The LWP is estimated on a weighted difference of the optical thicknesses of the two channels. In von Lerber et al. (2017) the LWP was used as a proxy of riming, and in this study we use the LWP as the driven observable for the k-means clustering of the dataset.

Table 1Radar technical specifications are shown for C-band polarimetric Doppler weather radar and for the ARM cloud radar systems at X, Ka and W band.

^∗ Sensitivity for 2 s integration time and for nominal ARM radar settings.

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2.3 Ikaalinen C-band weather radar

The Ikaalinen dual-polarization Doppler weather radar (IKA), used for cross-calibration analysis, belongs to the Finnish weather radar network (Saltikoff et al., 2010). It operates at C band and is located circa 64 km west of Hyytiälä. The antenna has a half-power beam width of 1^∘. The radar performs volume scans, repeated every 5 min, and range height indicator (RHI) scans over the Hyytiälä site every 15 min.

The IKA data are quality-controlled and calibrated using a number of techniques. The engineering calibration, where different radar components are characterized, is performed during the radar installation and after major system modifications (Saltikoff et al., 2010). In addition to the engineering calibration, the radar receiver and antenna pointing are monitored using sun observations (Huuskonen and Holleman, 2010). The differential reflectivity calibration is monitored using a combination of vertically pointing scans and sun observations. During the summer months, the IKA radar absolute calibration was checked using the polarimetric self-consistency principle (Gorgucci et al., 1992; Gourley et al., 2009).

Given the continuous monitoring of the radar stability and regular calibration, we use the IKA observations as the calibration standard for the ARM radars. This approach allows us to cross-calibrate the ARM radars even in the presence of radome attenuation caused by, for example, large snow accumulation.

2.4 ARM cloud radar system calibration at X, Ka and W band

The ARM cloud radar systems operating at X, Ka and W band are integral part of the BAECC snowfall IOP. The antennas of the XSACR and KAZR are mounted on top of two containers located 17 m away from each other. The MWACR is mounted on the same container as KAZR. All the ARM radars make zenith-pointing observations. Looking at the radar technical properties in Table 1, the range gate spacing and the temporal sampling are comparable, but there is a difference in the beam width between XSACR and the other two systems. To reduce the beam mismatch and to facilitate the intercomparison with the ground-based sensors, all the radar data are averaged to 5 min. To derive consistent X-, Ka- and W-band Z_e–S relations, the measured radar reflectivity factors were calibrated and corrected for attenuation. The absolute calibration of ARM cloud radars has been performed at the beginning and during the BAECC IOP using engineering calibration and external standard target procedure.

Figure 1Panel (a) shows radar profiles at C, X, Ka and W band for 15 February 2014 at 17:13 UTC where calibration is performed within the most stable height interval between 4 and 6 km. Panel (b) shows calibration error histograms related to the differences (Δ) between X- and C-band radars (dark red), Ka- and X-band radars (orange), and W and Ka radars (yellow).

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We have also performed a cross-calibration in order to reduce biases between different radar systems. The cross-calibration method is based on the assumption that in the low reflectivity region at the cloud top the small crystals basically scatter in the Rayleigh regime (Hogan et al., 2000). We have compared the radar measurements in regions close to cloud top (height higher than 5 km) in non-precipitating ice clouds. The selected radar reflectivity profiles have reflectivity values of less than 0 dB. Furthermore, only cases where no lower clouds or precipitation were detected were used for calibration. The cross calibration was performed for all the cases before and after the snowfall events. Only events where the cross-calibration values did not change are used in this study. As mentioned in Sect. 2.3, the IKA radar observations are considered to be the reference for this analysis. The main reason for this selection is that the IKA radar is very stable and its performance is well monitored. Additionally, given its operating frequency, it does not suffer from attenuation during winter storms. Figure 1a shows the profile of 15 February 2014 at 17:13 UTC in which we performed the calibration between 4 and 6 km and in Fig. 1b the histograms of the three different calibration errors. The calibration error, measured as the standard deviation (SD) of the histograms in Fig. 1b, shows that the best result is for the error between Ka and W band and the worst is for C and X band, this being related mostly to the beam width. Looking at Table 1, it can be seen that the larger the beam width, greater the measured dispersion and vice versa.

Figure 2Relative frequency histograms of the sky-noise antenna temperature for the Ka- and W-band radars for all 10 days of the BAECC IOP campaign.

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One of the reasons for differences in reflectivity measurements can also be attributed to the radome attenuation. For example, the flat shape of the KAZR radome increases the possibility of snow accumulation during a storm. Consequently, when the temperature rises above the melting point of ice, the melting snow could produce heavy attenuation that should be monitored. On the other hand, the conical shape of the MWACR radar limits the amount of accumulated snow, but because of the higher operating frequency it is more sensitive to the freezing rain/drizzle. To monitor the radome attenuation sky-noise analysis has been performed for the millimeter-wavelength radars, KAZR and MWACR. The sudden changes in the sky-noise temperature could have resulted from the increased surface temperature, which may indicate snow melting, and thus increased radome attenuation. The data in these cases are discarded. The stability analysis made with the sky noise is shown in Fig. 2 as a histogram of sky-noise power measured during 10 snowfall days of BAECC IOP. The SD is around 0.25 and 0.14 dBm, respectively, for KAZR and MWACR radar; according to these values, cases during the 10 snowfall events are excluded from the cross-calibration. This is shown in the Ka-band histogram in Fig. 2, where a secondary Gaussian-like peak is visible centered around −68.06 dBm.

During the BAECC IOP, radiosondes were launched four times a day. Using these observations as the input to the millimeter-wave propagation model (Liebe, 1985), the two-way gaseous path attenuation was computed for the dataset. This computation has been performed for all the dataset. For example, for 15 February 2014 at 17:24 UTC, the Ka-band two-way gas attenuation is 0.4334 dB. For the same time sample, the W-band two-way gas attenuation is 1.0206 dB. As expected, the attenuation for the W band is about twice as large as for Ka band. By taking into account the gaseous attenuation, the radar calibration offsets during the snowfall experiment were estimated as 2.9 dB for the XSACR, 3.9 dB for the KAZR and 4 dB for the MWACR. The results shown for the case study of 15 February 2014 have also been checked for the other snow events inside the dataset, confirming the consistency of the calibration analysis.

3.1 Deriving Z_e–S relations at X, Ka and W band

The equivalent reflectivity factor Z_e, measured by the radar systems at different wavelengths, and the liquid-water-equivalent snowfall rate S, evaluated from PIP, are the two related variables. The S (in mm h⁻¹) is derived from mass flux as

where m is the mass (in g), v is the velocity (in m s⁻¹), N is the particle size distribution (PSD, in ${\mathrm{mm}}^{-\mathrm{3}}{\mathrm{m}}^{-\mathrm{1}}$) and ρ_w is the liquid water density (in g cm⁻³). In Eq. (1) all quantities are expressed in terms of the disk-equivalent diameter D=D_Deq and derived from PIP measurements (von Lerber et al., 2017).

The radar data used in this study were collected in the vertical pointing mode. To match radar and in situ measurements, the radar data at the lowest meaningful altitude were used. Given the different radar specifications (see Table 1), the Fraunhofer far-field distance for the radars is different. This distance defines the near-field of the radars and is related to the radar antenna size. The beam width difference is related to the antenna diameter, which is respectively 1.82, 1.82 and 0.9 m for XSACR, KAZR and MWACR, so that the Fraunhofer distance (2D²∕λ) is approximately 214 m for XSACR, 773 m for KAZR and 514 m for MWACR. Taking into account the near-field influence, all radar data are selected at 400 m (Sekelsky, 2002).

Another important aspect is related to the different time acquisitions for the various instruments. In Table 1 we note that the temporal sampling of the radars is 2 s, whereas for the PIP instrument it is 1 min. To ensure similar sampling, we have decided to average data over 5 min. This results in PIP sampling volume of roughly 1 m³ for ice particles falling with a fall velocity of 1 m s⁻¹. As mentioned in Sect. 2, the averaging is also useful to tackle the differences in radar beam widths.

The Z_e–S is expressed in a power-law form, Z_e=aS^b, where Z_e is in mm⁶ m⁻³ and S is in mm h⁻¹ (Carlson and Marshall, 1972; Matrosov et al., 2008). In order to estimate the regression coefficients, we can choose nonlinear least squares in the variable linear space or linear least squares in the log–log variable space. We have adopted the latter approach by applying a linear regression as in Boucher and Wieler (1985). The applied log–log model is given by

where Z_e can be either the time-averaged range-resolved co-polar radar measurement (disregarding the near-field effects) or the numerically simulated backscattering radar response.

3.2 Multi-frequency Z_e–S relations using T-matrix scattering model

Single-scattering computations for spheroids are performed using Python implementation (Leinonen, 2014) of the TMM code (Mishchenko, 2000). The spheroidal particle model has been widely used for describing raindrops but also for approximating more complex particles such as snowflakes (Matrosov, 2007; Dungey and Bohren, 1993). In this study the spheroid model is initiated by using retrieved snowflake masses and maximum dimensions. This leaves the spheroid aspect ratio as a free parameter that adjusts volume, density and therefore the refractive index.

The aspect ratio is defined as $r_{\mathrm{s}}=b_{\mathrm{s}}/a_{\mathrm{s}}$, where a_s and b_s are the horizontal and vertical dimensions of the spheroid (r_s=1 spherical particle, r_s≥ 1 prolate particle and r_s≤ 1 oblate particle) (Dungey and Bohren, 1993). The snowflakes, due to aerodynamic forcing, typically fall with the major axis preferentially oriented horizontally (Magono and Nakamura, 1965; Matrosov, 2007). We have modeled the spheroids preferentially horizontally oriented with 10^∘ SD of the canting angle distribution following Matrosov (2007) and Matrosov et al. (2008). It should be noted that while snowflakes in the nature may have wider orientation angle distributions, the goal of the particle models used for scattering computations is to provide a link between radar observation and cloud/precipitation properties such as snowfall intensity or ice water content. This goal does not necessary imply that all of the particle model properties coincide with properties of naturally occurring snowflakes. Our studies show that use of wider canting angle distributions results in worse agreement between measured and computed radar reflectivity values (Tyynelä et al., 2011).

To test whether the spheroidal model can produce consistent multi-frequency radar observations, the TMM computations are performed using different aspect ratios. If the computations with the same aspect ratio value can explain measured Z_e–S relations at all the frequencies, then the spheroidal model can be considered adequate. If different aspect ratios are needed, then the model has failed. As stated above, the aspect ratio defines particle density as

in which the mass m is defined as in von Lerber et al. (2017) and D_Veq is the volume equivalent diameter defined from D_max, the maximum diameter obtained by PIP (von Lerber et al., 2017), as D_Veq = $r_{\mathrm{s}}^{\mathrm{1}/\mathrm{3}}{D}_{\text{max}}$. The presence of the aspect ratio inside the density reflects its influence on the complex refractive index of snow m_S that is defined through the Maxwell Garnett EMA.

The Γ-size distribution (in ${\mathrm{mm}}^{-\mathrm{1}}{\mathrm{m}}^{-\mathrm{3}}$) is assumed to model the PSD:

where N_w,Veq is the intercept parameter (in ${\mathrm{mm}}^{-\mathrm{1}}{\mathrm{m}}^{-\mathrm{3}}$), f(μ_Veq) and μ_Veq parameters are dimensionless, Λ_Veq is the slope of the distribution in 1 mm⁻¹ and D_0,Veq is the median volume diameter in mm. This Γ-size distribution can be expressed starting from the moments of the snowflake distributions measured by PIP, as in Bringi and Chandrasekar (2001), taking into account the variable changing from D_max to D_Veq as follows:

with

In the computations we have used the Γ-modeled size distribution, with the maximum dimension of 2.5 D_0,veq.

3.3 Multi-frequency Z_e–S relations using DDA scattering model

The DDA model is used to characterize the single-scattering properties of snowflakes when described with complex and more realistic shape models. Because of computational reasons, here DDA is not used to compute the scattering properties of the observed snowflakes, but rather the pre-calculated lookup tables (LUTs) are utilized for realistically shaped particles. Leinonen and Szyrmer (2015) have published an extensive LUT of backscattering properties for realistically modeled unrimed and rimed snow particles. The shape model is obtained by accurately simulating the microphysical processes that lead to snowflake growth. In particular, the snowflake formation is simulated by aggregation of pristine dendrites and subsequent or simultaneous riming of those aggregates using multiple values of equivalent LWP which in turn determine the degree of riming. The simulation of the riming process provides the scattering database to span through a large range of particle masses and sizes allowing to use those microphysical features to constrain the ice particle scattering properties. Moisseev et al. (2017) have shown that during BAECC experiment snow particles were moderately to heavily rimed; therefore the selection of the database that includes rimed particles appears to be justified. The scattering properties of the simulated particles are in fact picked from the LUT by finding the entries that most closely match the retrieved particle size and mass in von Lerber et al. (2017).

According to the PSD bin sizes of the PIP, the LUT is filtered to find entries which falls within each bin category. Then, using the retrieved m(D) relation determined in von Lerber et al. (2017) the LUT entries are sorted with respect to the difference between their mass, and the expected particle mass is computed using the retrieved m(D) relation. An arbitrary number of 10 entries that most closely match the retrieved m(D) relation are selected and their scattering properties are averaged in order to define the representative backscattering cross section of that particular size range. Larger number of particles can be picked from the LUT in order to represent a larger variability of particle mass, but the effects of including heavier and lighter particles tend to cancel out in the averaging and do not produce notable differences in the final integrated reflectivity value.

It is worth noting that the particles of Leinonen and Szyrmer (2015) are partially horizontally aligned where orientation of their shortest principal axis is, being normally distributed, with the SD of 40^∘.

4.1 Analysis of X, Ka and W bands Z_e–S empirical relations

The dataset was divided into three riming classes – lightly, moderately and heavily rimed (LR, MR and HR) snow – following the same logic used presenting the m(D) relations in von Lerber et al. (2017). Case studies were divided into classes using LWP values for the direct correspondence to the degrees of riming. Since the ice particle mass growth rate due to riming is proportional to LWP along the particle fall trajectory, the LWP can be seen as a proxy for riming (e.g. Moisseev et al., 2017). Given the growth rate, the riming degree of snowfall may also be influenced by the average particle size, such as D₀. To take all of this into account, the presented four snowfall events were classified into three subgroups using a k-means cluster analysis trained by LWP and D₀. The results are presented in Fig. 3 where the three clusters are identified in the LWP-D₀ space. It is worth noting that riming is strongly related to LWP but almost not dependent on the estimated size D₀ of snow particles. In summary, we have analyzed four events for Ka and W bands and three cases (excepted 20 March 2014) for X band. For the Ka and W band radar observations we have 282 data samples, which correspond to 1410 measurement minutes, of which 50.35 % are LR, 37.23 % MR and 12.42 % HR. For X band we have 174 data samples, 870 measurement minutes, divided into 49.42 % of LR, 35.06 % of MR and 15.52 % of HR. In Figs. 4–6 the derived Z_e–S relations for all radar frequencies and riming classes are presented.

Table 2Empirical Z_e–S for the four snow cases divided into LR, MR and HR snowfall regimes as shown in Figs. 4–6. Z_e–S at X, Ka and W band derived from ARM radar and PIP video disdrometer using a least-squares regressive analysis in the log–log space for each riming regime. The root-mean-square error (RMSE) is also shown in addition to the normalized RMSE (NRMSE) for the value range (defined as the maximum value minus the minimum value) of the measured data. The variability of coefficients, a and b, is shown in Fig. 7. Coefficients are related to the Z_e–S reference model in power-law form, i.e., Z_e=aS^b, where Z_e is expressed in mm⁶ m⁻³ and S is in mm h⁻¹.

BAECC cases of snowfall events with the a and b coefficients estimated in a 5 min time window.

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Table 3The prefactors and exponents of the Z_e–S relation in the literature for X, Ka and W band.

^a Boucher and Wieler (1985) provided a mean X-band relation between snowfall depth S_S and equivalent radar reflectivity as Z_e = $\mathrm{5.07}{S}_{\mathrm{S}}^{\mathrm{1.65}}$. This relation is expressed in Table for the snow-to-liquid ratio of 8:1 (10:1).
^b Fujiyoshi et al. (1990) presented a best-fit power-law relationship using 1 and 30 min respectively of averaged S and Z_e.

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Table 4Z_e–S (with Z_e in mm⁶ m⁻³, S in mm h⁻¹) relationships for all three riming classes, derived from TMM-based numerical simulations of Z_e and PIP-derived S and using the oblate-particle aspect ratio as a tuning parameter between 0.2 and 1. The best Z_e–S relation is highlighted in bold and corresponds to the power-law minimizing both RMSE and NRMSE.

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The LR snowfall samples are plotted in Fig. 4, showing the retrieved liquid-water-equivalent snowfall rate S from PIP (see in Eq. 1) with respect to the measured equivalent reflectivity factor Z_e from the ARM radars. A representation with Z_e in dBZ and S in base-10 logarithm has been chosen to adhere to the log–log model in Eq. (2). The parameters of the three Z_e–S relations are given in the Table 2. The accuracy of Z_e–S relations has been evaluated using the root-mean-square error (RMSE) in dB (where the error is defined as the difference between observed Z_e and estimated from PIP, using the regression coefficients). The normalized RMSE (NRMSE), i.e., RMSE values normalized by the observed reflectivity range, is also presented in the table. The NRMSE in percentages of the regressions shown in Fig. 4 is about 13 and 10 % for X and Ka/W band, respectively. Both prefactors and exponents of the Z_e–S relations tend to decrease with the radar frequency increase similar to what is presented in Matrosov (2007) and Matrosov et al. (2008). The regression coefficients are very close to those of Matrosov (2007) and Matrosov et al. (2008) and are rather different from those of Boucher and Wieler (1985) and Fujiyoshi et al. (1990). The literature values of the Z_e–S relations are summarized in Table 3.

Similar to the light riming class, the MR snowfall Z_e–S observations are shown in Fig. 5. The prefactor, a, and exponent, b, are slightly different with respect to the LR snowfall class; they decrease with the frequency but they have lower values, especially for X band. The trend of the a coefficient can also be considered in line with Matrosov (2007) and Matrosov et al. (2008), while the b coefficient is close to Matrosov (2007) and Matrosov et al. (2008) only for W band.

The last result is for HR snowfall, presented in Fig. 6. The values of the a and b coefficients are lower than those of the previous two riming regimes and than those of Matrosov (2007) and Matrosov et al. (2008), having a worse NRMSE accuracy of about 17 % (X band), 14 % (Ka band) and 16 % (W band).

Figure 7Frequency trend for the a (a) and b (b) regression coefficients, estimated in Table 2 using the power-law form Z_e=aS^b for the four studied snowfall cases divided into LR, MR and HR.

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Using data of the studied snowfall cases, the frequency behavior of the a and b power-law coefficients in Table 2 may be useful to suggest a general trend of the Z_e–S relation, even though only three frequency at X, Ka and W band are available. Figure 7 shows the spectral variation of the a and b coefficients, splitting the results between lightly rimed snowfall (black triangles), moderately rimed (black circles) and heavily rimed snowfall classes (black squares). The spline interpolation has been introduced for the three riming classes to outline a possible trend for these two coefficients. Considering all the limitations of the presented analysis it is still worth noting that (i) the monotonic decrease in the a coefficient with the frequency has been noted for all the three classes in Fig. 7a (the slope is higher for the LR with respect to the MR and HR), and (ii) the different spectral trend of the b coefficient in Fig. 7b decreases with the frequency, but it could be also used to separate the three regimes.

While analyzing the presented Z_e–S relation trends, we should understand that these relations depend on PSD parameters, such as N_w, m(D) and corresponding single-scattering ice particle properties. The difference between Z_e–S obtained for different radar frequency bands arises from the changes in the snowflake scattering properties. In the Rayleigh regime, the dependence of radar cross section (RCS), on D, is given by m(D)². For higher frequencies the exponent of RCS(D) relation will become smaller, and therefore the exponent of Z_e–S relation should decrease as well. However, the relations derived for different snowfall riming regimes are influenced not only by changes in m(D) but also by changes in PSD. Furthermore, here not only changes in average values of, for example, N_w are important but also PSD parameter variations during the recorded events (von Lerber et al., 2017). Therefore, some of the changes in the a and b coefficients between the riming classes are probably caused by the PSD values and variations.

Figure 8Radar and TMM computations from 12 February 2014 between 04:00 and 08:50 UTC for (a) X band, (b) Ka band and (c) W band. Radar reflectivities (LR and MR snowfall in black triangles and circles, respectively) from XSACR, KAZR and MWACR are corrected for sky-noise, calibration offsets and attenuations (as better explained in Sect. 2.4). The error bars are used to represent the variation (min–max difference) of radar data within a 5 min window with respect to their averaged value (black triangles and circles). TMM-based computations (red, orange, green, magenta and blue lines for r_s=0.2, r_s=0.4, r_s=0.6, r_s=0.8 and r_s=1, respectively) are derived from PIP data.

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4.2 Explaining Z_e–S relations with scattering simulations

Time series of multi-frequency radar measurements can provide a further insight into the analysis of snowfall regime and the capability to simulate its behavior. Figure 8 shows the equivalent reflectivity factor Z_e as a function of time for the snow case study of the predominantly LR 12 February 2014 case (100 % for X band, 91.67 % for Ka/W band). The black triangles and circles correspond to ARM-radar mean Z_e, whereas the bars are related to the variation between their minimum and maximum values within the same averaging time interval of 5 min. A total of 8.33 % of the measurements (black circles in Fig. 8b and c) at the beginning of the event correspond to the MR snow data (0 % for X band, 8.33 % for Ka/W band) and they are disregarded since the variation index (defined as the ratio between minimum–maximum variability interval and its mean value) is considered to be too high. The different colored lines refer to Z_e, simulated using TMM from PIP data with a variable aspect ratio r_s between 0.2 and 1 with a step of 0.2. The smaller value r_s = 0.2 (red line) indicate very oblate particles, whereas r_s = 1.0 (blue line) correspond to spherical snowflakes. By comparing ARM measurements and TMM simulations, the optimal aspect ratio value seems to decrease when increasing the frequency: X-band data are better represented by TMM-derived spherical particles (r_s=1), whereas Ka- and W-band results are in agreement with an aspect ratio of 0.6. After 07:00 UTC within the heavy precipitation period, no data are available for X-band radar in this case study, but the optimal aspect ratio tend to change to a value around of r_s=0.4 for the millimeter-wave radars (Ka and W band). This frequency dependence of the aspect ratio indicates that the soft-spheroid model is not consistent across the frequencies, for this snow event. This finding is in line with Leinonen et al. (2012) and Kneifel et al. (2015), who showed that for low-density aggregates the soft-spheroid model may not be adequate.

Figure 9Same as Fig. 8, but for 15/16 February 2014, 21:00–01:48 UTC (LR and MR snowfall in black triangles and circles, respectively).

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Figure 10Same as Fig. 8, but for 21/22 February 2014, 16:00–03:24 UTC (LR, MR and HR snowfall in black triangles, circles and squares, respectively).

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Figure 11Same as Fig. 8, but for 20 March 2014, 16:00–20:48 UTC (LR, MR and HR snowfall in black triangles, circles and squares, respectively). The X-band radar data are not available for this time window.

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Figure 9 again shows Z_e as a function of time for the mixed LR–MR 15–16 February 2014 snowfall case (61.02 % for LR and 38.98 % for MR). We can distinguish two main intervals: before the heavy precipitation around 22:10 UTC, as for the previous case, the optimal aspect ratio decreases when increasing the radar frequency, whereas during the heavy precipitation interval (from 22:50 UTC on) the optimal aspect ratio seems to be around 0.6 independent of the frequency that corresponds to the LR time period. Figure 10 shows the time behavior for 21–22 February 2014, a snow case with all three regimes present (X band: 13.33 % for LR, 56.67 % for MR and 30 % for HR; Ka/W band: 10.14 % for LR, 65.94 % for MR and 23.91 % for HR). Until 22:00 UTC, in the presence of MR snow, the optimal aspect ratios seem to be around 1, 0.8 and 0.8 at X, Ka and W band, respectively, whereas during the heavy precipitation period (23:00–00:00 UTC), in the presence of LR snow, it is constant around r_s=0.6 irrespective of the frequency. These considerations are also valid for 20 March 2014 in Fig. 11, in which the optimal aspect ratio is about r_s=0.6 for the millimeter-wave radars (Ka and W band) and in fact it was a predominantly LR case (72.41 % for LR, 24.14 % for MR and 3.45 % for HR). For this case X-band data are not available and thus they are not shown in the figure.

As a general comment on Figs. 8–11, we note that measured data fall within the computed range of uncertainty. The incremental difference in terms of Z_e due to an increase of 0.2 in the particle aspect ratio is about 1.7 dBZ at X band, 2.5 dBZ at Ka band and 6 dBZ at W band. The difference between the value for r_s=0.2 and r_s=1 is on average 5.5 dBZ for X band, 7 dBZ for Ka band and 12 dBZ for W band. By increasing the frequency from X to W band, the radar reflectivity seems to be, in general, more sensitive to the non-spherical shape of the snowflakes, with r_s decreasing from 1 to 0.6.

To investigate how the soft-spheroid model performs in terms of reproducing the observed Z_e–S relations, the TMM computed reflectivity factors were used to derive multi-frequency Z_e–S relations. The relations were computed using different values of the soft-spheroid model aspect ratio. The derived relations are summarized in Table 2 and shown in Figs. 4, 5, and 6. Similar to the analysis of the Z_e time series, presented above, we may conclude that to reproduce the observed Z_e–S relations at different frequencies, different spheroid aspect ratios may need to be used. This effect is clearest for the LR class (see Fig. 4), where for X band the best fitting aspect ratio is 1 and for W band it is closer to 0.6. For heavier rimed particles this difference becomes less pronounced.

Figure 12Horizontally polarized cross section σ, expressed as a function of the diameter disk-equivalent D_Deq at W band by comparing DDA computations (black line) and TMM computations (red, green and blue lines, matching r_s=0.2, r_s=0.6 and r_s=1). The product between σ and PIP-derived snowflake size distribution N shows the main contribution of particle size in terms of diameter disk-equivalent for DDA computations (dotted black line) and TMM computations (r_s=0.6, dotted green line).

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It should be noted that the observed differences between observed Z_e and ones computed using TMM are not as large as was previously expected. To investigate why this is the case, single-scattering properties computed using DDA (Leinonen and Szyrmer, 2015) were compared to the TMM results for the three cases shown in Fig. 12. The computations are performed for the W band. TMM simulations are given by red, green and blue lines referring to different aspect ratios (r_s=0.2, 0.6, 1, respectively), whereas DDA results are given by the black line. The dotted line shows the product between the snowflake PSD and the RCS computed using TMM with aspect ratio of 0.6 (green dotted line) and the DDA (black dotted line). This figure shows that TMM computations using lower aspect ratios agree better with DDA. Furthermore, it indicates that there is a compensating effect, where TMM overestimates RCS for smaller snowflakes and underestimates it for larger particles. This may explain smaller differences between the DDA and TMM calculations.

Figure 13Panel (a) shows PSD for snowfall case of 12 February 2014 and (b) shows PSD for snowfall case of 15/16 February 2014. Red circles are representative of the normalized PSD measured by PIP, the dashed black line represents the normalized estimated Γ-PSD in Eq. (4) and the green line is the last one truncated at the maximum value of 2.5 multiplied by the median diameter D₀.

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This compensation effect depends on the integration limits used to compute the Z_e. In this study we have integrated from 0 to 2.5 D₀. To check whether this integration limit is valid, in Fig. 13 measured and fitted PSDs are shown. As can be seen, the assumed upper integration limit appears to be valid.