3.2.1. Stratiform composite
Table 5 and Figure 8(a) and (b) provide an overview of the domain average and maximum 24-hour (0000–2400 UTC) accumulated surface precipitation for all experiments, and as derived from combined radar and rain-gauge information for the stratiform composite (blue symbols). Domain-average surface precipitation in ExpH shows an overestimation of about 25% (Table 5), and domain-maximum accumulations are even overestimated by over 50%. Considerable variation exists for different cases, with some cases overestimating surface precipitation by more than 100% while others underestimate it (Figure 8(a) and (b)). In contrast to many previous studies, which found a strong reduction in surface precipitation by replacing large hail by small graupel, no such impact was found in our stratiform composite (ExpG; Table 5). Viewing the composite averages, no significant reductions in domain-average or maximum precipitation are found as compared to ExpH. For individual cases, the differences generally remain within 10% of ExpH. A 20% increase in domain-average surface precipitation occurs in ExpGS (Table 5 and Figure 8), compared to ExpG. All but one case in the stratiform composite have higher domain-average surface precipitation accumulations in ExpGS as compared to the other experiments, while the domain-maximum precipitation decreases slightly in most cases (Figure 8).
Figure 8. Observed vs. simulated (a) domain-average 24-hour surface precipitation and (b) domain-maximum 24-hour surface precipitation for all cases and all experiments. Stratiform cases are in blue and convective cases are in red. The different experiments are denoted with different symbols as outlined in the top panel. The 1:1 line is provided as reference (symbols above this line indicate model overestimation, symbols below this line model underestimation).
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Table 5. Overview of statistics on observed and simulated surface precipitation in the stratiform and the convective composite for all experiments and averaged for all cases.
|Mean Stratiform||6.6 (39.1)||8.4 (60.7∗)||8.1 (65.8∗)||9.7∗ (56.1∗)|
|Bias Stratiform||—||1.8 (22.2)||1.4 (27.2)||2.5 (19.3)|
|RMSE Stratiform||—||3.6 (39.2)||3.6 (48.4)||4.3 (32.0)|
|Mean Convective||5.2 (55.9)||9.4∗ (101.1∗)||9.5∗ (80.9∗)||9.1∗ (70.8)|
|Bias Convective||—||4.2 (44.9)||4.3 (24.8)||3.9 (14.7)|
|RMSE Convective||—||5.9 (64.0)||5.8 (35.9)||5.3 (23.5) |
Although ExpG and ExpH behave similarly in terms of surface precipitation, there are notable differences in the vertical distribution of hydrometeors (Figure 5(a)). As mentioned in the previous section, snow above the melting layer is prevalent in ExpH, while graupel is prevalent in ExpG. Summing the hail/graupel and snow downward mass fluxes yields similar fluxes near the melting layer between ExpH and ExpG (not shown). Fluxes of graupel near the melting layer are somewhat higher in ExpGS, which also enhances rain mass flux since this mainly originates from melting graupel. A more detailed analysis of the reasons for differences in surface precipitation accumulation between ExpGS and the other experiments is outlined in section 3.3.1.
3.2.2. Convective composite
The simulations in the convective composite tend to produce excessive surface precipitation. From Figure 8(a) and (b) (red symbols) and Table 5, virtually all convective simulations considerably overestimate the domain-average and maximum surface precipitation in ExpH, which results in an all-case overestimation of about 80% (Table 5). The results of ExpG suggest a limited impact on domain-average surface precipitation for a large number of real-case simulations (Table 5 and Figure 8). Domain-average surface precipitation for individual cases only varies within about 10% between ExpG and ExpH and on the average for all cases does not show any impact. Note that the convective composite contains a large range of storm types and even during a supercell case (on 22 June 2008) precipitation was only slightly reduced. A more important sensitivity is seen in the domain-maximum precipitation. The average overestimation for all cases in ExpG is reduced to 45% from 80% for ExpH. Further, modifying the snow size distribution assumptions (ExpGS) again hardly impacts the domain-average surface precipitation (Figure 8 and Table 5), but the domain-maximum precipitation overestimation is further reduced to 27%.
In contrast, numerous idealized case-studies of deep convection suggest that replacing large hail by small graupel tends to reduce surface precipitation significantly (Gilmore et al., 2004; van den Heever and Cotton, 2004; Morrison and Milbrandt, 2011). Those idealized studies typically do not include boundary layer and radiation processes and, for supercell simulations, also do not capture the full life cycle of the convection. Hence it is uncertain how this behaviour extrapolates to real-case simulations of deep convection.
By and large, the nature of the largest rimed ice species (graupel or hail) can alter surface precipitation in two possible ways. First, there might be a change in latent heating (e.g. due to more freezing) and hence to the general dynamics of the storm systems. Second, there might be a change in precipitation efficiency of the storm systems, which would lead to differences in surface precipitation since the same amount of total excess water vapour is turned to condensate. For instance, this could be due to changes in sublimation or evaporation of condensed particles caused by changes in fall speed. Figure 9 provides vertical profiles of the maximum up- and downdraughts, and of the total up- and downdraught mass flux averaged for all convective cases (up/downdraughts are defined as regions ascending/descending at 0.5 m s−1). Updraught mass flux and vertical velocity is larger in the experiments including graupel, which is likely due to enhanced latent heating associated with freezing processes. Indeed, total cloud water is significantly reduced in ExpG compared to ExpH (Figure 5(b)) as more of it is consumed for riming on the more numerous graupel particles (not shown). However, Figure 9 also shows enhanced downdraught mass fluxes and maximum velocities for ExpG, associated with enhanced evaporation and sublimation. Hence, it is likely that in ExpG more vapour is turned to condensate due to more vigorous updraughts, but then more of this condensate is returned to vapour that leads to decreased precipitation efficiency as suggested, for example, by Van Weverberg et al. (2011b).
Figure 9. (a) Vertical profiles of domain maximum and minimum vertical velocity for all experiments, averaged over all convective cases, and (b) vertical profiles of updraught and downdraught total mass flux of dry air for all experiments, averaged over all output times and all convective cases.
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A number of factors might explain the difference between the small impact found for our real-case simulations and the large impact of the precipitating ice species found by previous idealized studies on surface precipitation (Gilmore et al., 2004; van den Heever and Cotton, 2004; Morrison and Milbrandt, 2011). One factor might be that there is much more stratiform precipitation between the convective cells than in a real-case simulation and, therefore, the differences between ExpG and ExpH are somewhat smoothed out. However, the smaller differences between ExpG and ExpH in the domain-maximum precipitation (Table 5), which is associated with convective cells, rules out this hypothesis. In order to investigate the role of other environmental conditions, a correlation analysis was performed between the differences in precipitation rates between ExpH and ExpG and the mean-freezing level, cloud-base height, updraught velocity, vertical-wind shear and mid-level humidity (Figure 10). The coefficient of determination, R2, is given on each plot. Each data point in Figure 10 represents the difference between ExpH and ExpG in instantaneous domain-average precipitation rate (for a certain case and output time and when grid cells have precipitation intensities of more than 10 mm h−1 only). Of the investigated factors, the freezing level (Figure 10(a)) and the updraught velocity (Figure 10(e)) account for the largest part of the explained variance in the difference between ExpH and ExpG. The reason might be derived from Figure 11. Hail in ExpH and graupel in ExpG have very different average vertical velocities (Figure 11(c); VH); however, due to the larger mass content of graupel in ExpG, the averaged downward flux (Figure 11(f); FluxH) of graupel (i.e. larger precipitation rate) around the freezing level is larger as compared to the averaged downward hail flux in ExpH. As most rain originates from melting graupel, averaged downward rain fluxes just below the freezing level are enhanced as well (Figure 11(e); FluxR). However, in ExpH, hail melts very slowly and melting hail continues to add to the rain content down to the surface. In ExpG, graupel melts almost instantaneously at the melting level, leaving more time for rain to evaporate. Hence, due to more rain evaporation in ExpG, the precipitation rates at the surface in ExpH increase to the precipitation rate in ExpG. Thus, the higher the melting level is, the longer that rain will evaporate at a higher rate in ExpG as compared to ExpH, and the more surface precipitation rates deviate between ExpH and ExpG. In that context, it is worth noting that the highest melting level in any of our real cases was situated around 3000 m, while previous idealized experiments were initiated with freezing levels around 4000 m. Further, the stronger the updraughts, the higher that graupel will be lofted, and the more time there is for it to sublimate instead of melting to rain (as suggested by Van Weverberg et al., 2011b). Again, while maximum updraught velocities in our simulations reach about 30 m s−1, updraughts in Gilmore et al. (2004) were up to 60 m s−1. Hence, it is likely that the large reduction in surface precipitation found in idealized experiments containing graupel is only valid for certain environmental conditions, which are typical for supercell convection in Midwest USA but not for western Europe. An additional factor that might contribute to different behaviour between the idealized experiments and our real-case simulations is that the idealized simulations were typically rather short (about 2 hours) and might not have captured the full convective cycle.
Figure 10. Correlation analysis of the percentage difference in precipitation rate between ExpH and ExpG and different environmental variables. Each data point in the figure denotes this percentage difference in domain-average instantaneous precipitation rate for a particular output time and case (for grid cells exceeding 10 mm h−1 only). Factors examined are (a) freezing-level height, (b) cloud-base height, (c) mid-level vertical wind shear (0–6 km), (d) low-level vertical wind shear (0–3 km), (e) maximum vertical velocity and (f) mid-level relative humidity. Coefficients of determination are shown on the respective figures. Lines indicate a linear regression fit through the points.
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Figure 11. Domain-average vertical profiles of (top) mixing ratio, (middle) fall speed and (bottom) flux for (left) rain and (right) graupel, averaged over all output times and all cases in the convective composite for each experiment.
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ExpGS behaves similarly to ExpG, except that the graupel fall speed is even lower (due to the Locatelli and Hobbs (1974) relation used in the fall speed formulation). However, the total graupel mass is also increased and hence the total downward mass flux is similar to that in ExpG. Thus, in contrast to the stratiform precipitation events, ExpGS does not enhance domain-average precipitation during convective cases. Indeed, snow plays a lesser role in the convective composite compared to the stratiform composite (Figure 5(a) and (b)) and hence the enhancement in snow depositional growth in ExpGS might not be sufficient to significantly impact precipitation efficiency.
The general overestimation in all experiments of surface precipitation associated with the convective composite might, to some extent, be related to the coarse resolution of the experiments presented here. Indeed, Deng and Stauffer (2006) suggested that updraughts might become too vigorous when they are forced on coarser-than-natural scales. Bryan and Morrison (2012) suggested that entrainment rates in updraughts become larger when horizontal resolution decreases. Therefore, some of the sensitivities presented here might be resolution-dependent to some extent. The reasons for differences in domain-maximum precipitation accumulations among the different experiments are explored in section 3.3.2.