How Mountain Geometry Affects Aerosol-Cloud-Precipitation Interactions: Part II. Deep Convective Clouds

How Mountain Geometry Affects Aerosol-Cloud-Precipitation Interactions: Part II. 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Deep Convective Clouds Jaemyeong Mango Seo, Jong-Jin Baik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6553203/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The sensitivity of aerosol effects on orographic precipitation from deep convective clouds to mountain upslope steepness is examined using the Weather Research and Forecasting (WRF) model coupled with a bin microphysics scheme. During the early stage, the sensitivity resembles that of warm, shallow orographic convection as discussed in Part I. As time progresses, interactions between vigorously developed lower-layer clouds and upstream-extending upper-layer clouds become crucial for enhancing surface precipitation via melting and direct sedimentation of ice-phase particles such as graupel and hail. In the symmetric mountain cases, higher aerosol number concentration enhances surface precipitation through stronger condensational latent heating and more active mixed-phase processes (freezing, Wegener-Bergeron-Findeisen process, and riming). Under asymmetric mountain configurations, however, the sensitivities are non-monotonic. In the steep upslope cases, fast liquid drop growth in the clean case and strong latent heating in the polluted case both support cloud development and enhance precipitation. In contrast, the control case exhibits weaker precipitation due to slower drop growth than the clean case and weaker heating than the polluted case, suppressing the cloud interaction. In the gentle upslope cases, the control case shows enhanced precipitation due to sufficient droplet supply and latent heating which help clouds grow through the freezing level. In contrast, the clean case lacks sufficient cloud droplets, while the polluted case suffers from weak convection despite strong aerosol-induced heating. In both cases, the cloud interaction and mixed-phase processes are suppressed. aerosol-cloud-precipitation interactions aerosol number concentration mountain upslope geometry orographic precipitation bin microphysics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1 Introduction Because aerosol particles act as cloud condensation nuclei (CCN) or ice nuclei (IN) in the atmosphere, aerosol loading controls precipitation characteristics through complex aerosol-cloud-precipitation interactions. To understand the role of aerosol particles in controlling precipitation characteristics, many researchers have investigated the impacts of aerosols on clouds and precipitation extensively (e.g., Khain 2009 ; Tao et al. 2012 ; Fan et al. 2016 ). These aerosol effects can become particularly complex in mountainous regions. Precipitation over mountainous regions, known as orographic precipitation, is affected by various factors, including mountain geometry as well as background static stability, wind speed, and humidity (Colle 2004 ; Pathirana et al. 2005 ; Roe and Baker 2006 ). However, the sensitivity of orographic precipitation to the environmental factors and mountain geometry is generally focused on orographic precipitation from shallow clouds; because in a broad range of the environmental factors and mountain geometry, downdrafts involved in mountain waves restrict convective development of orographic clouds. Several studies have investigated orographic precipitation in the conditionally unstable atmosphere and have categorized the behavior of convective orographic clouds depending on convective available potential energy (CAPE), mountain width, and the Froude number ( F = U / Nh m ), where U is the background wind speed, N is the background buoyancy frequency, and h m is the maximum height of the mountain (e.g., Chu and Lin 2000 ; Chen and Lin 2005 ; Chen et al. 2008 ; Miglietta and Rotunno 2009 ; Sever and Lin 2017 ). Many previous studies have shown that the increase in aerosol number concentration usually results in decrease in surface precipitation from shallow convection (Xue and Feingold 2006 ; Cheng et al. 2007 ; Fan et al. 2012 ) and in increase in surface precipitation from deep convection (Khain et al. 2005 ; Rosenfeld et al. 2008 ; Clavner et al. 2018 ). Recent studies have further refined this understanding by highlighting the dominant role of CCN in glaciation (Munawar et al. 2025 ) and the contrasting effects of different aerosol types on cloud microphysics (Chen et al. 2025 ). In orographic convection, aerosol-cloud-precipitation interactions exhibit greater complexity due to terrain-modulated airflow and microphysical variability. Many studies have shown that air pollution causes suppression of orographic precipitation (Givati and Rosenfeld 2004 ; Jirak and Cotton 2006; Rosenfeld and Givati 2006 ; Rosenfeld et al. 2007 ; Guo et al. 2014 ). Xiao et al. ( 2015 ) showed that the increase in aerosol number concentration results in the enhanced orographic precipitation, but the effects on deep convective orographic precipitation have been less studied. The recent work by Chavez and Barros ( 2023 ) further indicates that aerosol indirect effects can shift the spatial distribution of orographic precipitation, especially by modifying cloud longevity and riming processes along windward slopes. Bin microphysics models predict the evolution of each size bin of each hydrometeor separately, allowing detailed tracking of microphysical growth and phase changes (e.g., Khain et al. 2000 ; Lynn et al. 2005a , b ). To investigate the microphysical processes under orographic precipitation more precisely, numerical studies using bin microphysics schemes have been conducted (Lynn et al. 2007 ; Xue et al. 2010 ; Xiao et al. 2014 ). However, most of these studies have focused on shallow convective orographic clouds, where warm rain processes dominate. Relatively few studies have used bin schemes to examine deep orographic convection involving mixed-phase microphysics; Xiao et al. ( 2015 ), for example, investigated aerosol effects on mixed-phase orographic precipitation, but such work remains limited. This study aims to investigate how the windward mountain slope modulates aerosol effects on orographic precipitation from deep convective clouds. Section 2 describes the experimental design of the simulations. Sections 3.1 and 3.2 present the characteristics of deep convective orographic precipitation and its sensitivity to aerosol number concentration. Section 3.3 discusses how aerosol effects vary with changes in the windward mountain slope. Section 4 provides a summary and concluding remarks. 2 Simulation Design In this study, the Weather Research and Forecasting (WRF) model, version 3.6.1, coupled with the Hebrew University Cloud Model (HUCM) is used (Skamarock et al. 2008 ; Lee and Baik 2016 ). The model is the same as in Part I (Seo et al. 2020 ), except that mixed-phase microphysical processes are included. This model predicts seven types of hydrometeors [liquid drops, ice crystals (column, plate, and dendrite), snow, graupel, and hail] as well as aerosols, which are subdivided into 43 mass-doubling bins. Detailed descriptions of HUCM are provided in Khain et al. ( 2000 , 2004 , 2011 ). Two-dimensional simulations are conducted to examine orographic precipitation from deep convective clouds. Figure 1 shows the schematic of the simulations. The bell-shaped mountain, defined as below, triggers orographic clouds by forced uplift. Here, h m = 2 km is the maximum height and a (= a 1 for x < 0 km and = a 2 = 10 km for x ≥ 0 km) is the half-width of the bell-shaped mountain. As in Part I, we classify simulation cases by aerosol number concentration at 1% supersaturation N 0 [= 100 cm –3 in CLN (clean), = 500 cm –3 in CNT (control), and = 2500 cm –3 in PLT (polluted)] and the windward width (upslope steepness) of the mountain a 1 (= 5 km for narrow, = 10 km for control, and = 20 km for wide). Table 1 provides the name and specific settings for each case. The background wind speed U = 10 m s –1 is constant in the vertical. Table 1 Names of the nine simulation cases and the corresponding aerosol number concentrations ( N 0 ) and windward half-widths ( a 1 ) of the mountain. N 0 (cm –3 ) a 1 (km) CLN 100 10 CNT 500 10 PLT 2500 10 CLNn 100 5 CNTn 500 5 PLTn 2500 5 CLNw 100 20 CNTw 500 20 PLTw 2500 20 To generate vigorous convective orographic clouds, a modified version of Weisman and Klemp ( 1982 ) sounding is used. In this study, the potential temperature θ and relative humidity H are given as follows: where θ 0 = 298.15 K and θ tr [ H 0 (= 0.9) and H tr ] are the potential temperature (relative humidity) at the surface and the tropopause height z tr (= 12 km), respectively. T tr is the temperature at the tropopause, c p is the specific heat of air at constant pressure, N (= 0.01 s –1 ) is the buoyancy frequency, and g is the gravitational acceleration. By fixing N , the Froude number is easily controlled. The skew T -log p diagram of this sounding is shown in Fig. 2 . The lifting condensation level (LCL ~ 300 m) is located below the mountain top, which enables orographic cloud formation through forced uplift. Although the background atmosphere is statically stable ( N = 0.01 s –1 ), the sounding becomes highly unstable after saturation, with CAPE reaching 4457 J kg –1 . This allows deep and strong convection to develop through the tropopause. A horizontal domain of 200 km with a horizontal grid size of 250 m is used. The vertical domain size is 20 km with a 5-km sponge layer and 401 terrain-following levels. An open lateral boundary condition is applied in the horizontal. Each case is integrated for 12 h using the WRF-bin model with a time step of 0.6 s. However, only the first 6 h of results are analyzed, for reasons discussed in Section 3.1. Except for the bin microphysics scheme and basic turbulent/diffusion parameterization, other parameterizations are not used. 3 Results and Discussion 3.1 General Characteristics of the Simulated Orographic Precipitation Figure 3 shows the mixing ratios of liquid drops (bluish shading), low-density ice particles (brownish shading), such as ice crystals and snow, and high-density ice particles (black contours), such as graupel and hail, along with wind vectors in the CNT case. In addition, gray contours in Fig. 3 represent the theoretical vertical velocity field associated with mountain waves, which is calculated using the linear analytic solution of Seo et al. ( 2018 ). This solution assumes a two-layer hydrostatic atmosphere where the buoyancy frequency N is 0.01 s –1 in the troposphere and 0.02 s –1 in the stratosphere, with a tropopause height of 12 km. Other parameters are consistent with the simulation setup: U = 10 m s –1 , a = 10 km, and h m = 2 km. This theoretical vertical wave structure is overlaid to evaluate how the simulated wind field and cloud structures align with the idealized mountain wave pattern. Contour intervals is set to 0.1 m s –1 . From the early stage, orographic clouds are generated over the upslope. In the subcritical condition ( F < 1, where F is the Froude number), flows converge into a thin layer and accelerate over the downslope. As a result, a hydraulic jump occurs, and strong updrafts are generated over the downslope (Fig. 3 a). Although the simulated wind field in this stage exhibits general subsidence over the windward slope, it does not yet align well with the idealized mountain wave structure shown in the gray contours. This discrepancy is likely due to early transient convective activity near the mountain. The deep convective system is advected downstream by downslope wind and results in heavy precipitation on the lee of the mountain (Fig. 3 b). As the transiently generated deep convective system moves out of the depicted region, the simulated wind field begins to exhibit more organized mountain wave patterns, gradually resembling with the theoretical structure calculated using the analytic solution. Following this transition, two vertically separated cloud layers become apparent (Fig. 3 c). The lower layer consists mainly of orographic clouds with liquid drops, while the upper layer is dominated by low-density ice particles. In the stratified atmosphere, downdrafts associated with mountain waves inhibit the vertical development of shallow orographic clouds. In the upper layer over the lee side, particularly between 8 and 11 km altitude, weak updrafts generated by the wave structure facilitate the growth of pre-existing ice crystals into snow particles. Since these updrafts coincide with the negative perturbation horizontal wind, upper-layer clouds containing low-density ice particles extend upstream when the perturbation wind speed exceeds the background wind speed. After t ~ 4 h, lower-level stratiform orographic clouds transition into cellular-type convective orographic clouds due to condensational latent heating and extend upstream. Occasionally, some convective cells deepen and interact with upper-layer mixed-phase clouds. This interaction leads to intermittent heavy precipitation caused by melting of high-density ice particles or direct precipitation of graupel or hail (Fig. 3 d). Figure 4 shows the Hovmöller diagrams of surface rain rate (shaded) and ice-phase precipitation rate (contoured at 0.01 mm h –1 ) in CLN, CNT, and PLT. As described earlier, the transiently generated deep convective system is advected downstream, while steady orographic precipitation occurs near the mountain. This pattern corresponds to Regime III in Chu and Lin ( 2000 ). In CNT and PLT, orographic precipitation extends upstream after t ~ 5 h. In all cases, ice-phase precipitation becomes dominant near and downstream of the mountain peak after t ~ 6 h, as the freezing level gradually decreases over time. In this study, we focus on how aerosol number concentration and upslope steepness modulate surface rainfall over the mountainous terrain through interactions between deep convective orographic clouds and upper-level mixed-phase clouds. Therefore, averaged and accumulated variables from t = 4 to 6 h are analyzed to avoid the initial period dominated by the transiently developed deep convective system ( t = 0–4 h) and the later stage dominated by ice-phase precipitation ( t = 6–12 h). 3.2 Aerosol Effects on Orographic Precipitation Figure 5 shows the averaged mixing ratios of liquid drops, low-density ice particles, and high-density particles, along with wind vectors from t = 4 to 6 h in CLN, CNT, and PLT. During this period, lower-level convective orographic clouds develop over the mountain. In the cases with higher aerosol number concentration, a larger number of condensates results in stronger condensational latent heating, which leads to deeper and stronger convection. As a result, more liquid drops can interact with ice particles in the upper-layer cloud and freeze into ice crystals above the freezing level. These processes increase the mixing ratio of high-density ice particles in the cases with high aerosol number concentration. The high-density ice particles grow via riming in both lower- and upper-layer clouds and enhance surface precipitation through melting or direct sedimentation. This process is consistent with the findings of Xiao et al. ( 2015 ), who demonstrated that high aerosol number concentrations promote the upward transport of small supercooled liquid droplets, enhancing freezing and riming above the freezing level and thereby increasing precipitation efficiency. These results are also consistent with Munawar et al. ( 2025 ), who emphasized the dominant role of CCN in initiating glaciation. Figure 6 and Table 2 show that increasing aerosol number concentration leads to greater total and maximum precipitation amounts and an upstream shift in the location of the maximum precipitation. This dependency of precipitation characteristics on aerosol number concentration is opposite to the shallow, warm orographic precipitation case described in Part I. However, an increase in total and maximum precipitation amounts caused by an increase in aerosol loading has also been reported in previous studies (Khain et al. 2005 ; Rosenfeld et al. 2008 ; Xiao et al. 2015 ; Clavner et al. 2018 ). Table 2 Total accumulated surface precipitation amount over 6 h ( P tot ), maximum 6-h surface precipitation amount ( P max ), and the x -location where P max occurs ( x max ) in the nine simulation cases. P tot (mm) P max (mm) x max (km) a 1 (km) 5 10 20 5 10 20 5 10 20 clean 1755 1298 1711 15.8 15.0 33.7 2.0 16.0 3.8 control 1000 2022 2551 10.6 19.9 29.3 16.0 12.5 –2.0 polluted 1260 2071 1394 11.8 23.7 25.2 14.0 1.0 4.8 Figure 7 shows the vertical profiles of temperature change due to microphysical processes over the upslope ( x = − 50–0 km) and downslope ( x = 0–50 km) of the mountain. On both sides, nucleation is strongest in PLT. Over the upslope, the nucleation rate decreases with height, while over the downslope it peaks around 5 km above ground level (AGL) (Figs. 7 a and 7 f). A higher number of condensates over the upslope in PLT leads to stronger condensational latent heating there and also stronger evaporative cooling over the downslope, compared to the other cases (Figs. 7 b and 7 g). Over the upslope, many liquid drops freeze into ice crystals (Fig. 7 d), which then grow through the Wegener-Bergeron-Findeisen (WBF) process in PLT (Figs. 7 b and 7 c). This process is followed by intensive riming, which induces strong localized heating over the upslope in PLT (Fig. 7 e), where riming is most active across nearly all altitudes. In contrast, riming is relatively suppressed over the downslope in PLT (Fig. 7 j). This distribution suggests that the riming zone in PLT has shifted upstream, a feature observed in the case with high aerosol number concentration. Such spatial displacement of the riming zone resembles the mechanism proposed by Chavez and Barros ( 2023 ), in which aerosols shift riming activity upwind, extending the hydrometeor growth pathway and enhancing precipitation. In CLN, similar processes, intense freezing and the WBF process, are strongest over the downslope compared to the other cases (Figs. 7 g–i). Riming between ice-phase particles and liquid drops primarily contributes to enhanced surface precipitation through mixed-phase processes (Figs. 7 e and 7 j). During these processes, the upstream extension of the upper-layer cloud creates an environment that increases the mixing ratio of high-density ice particles via riming when lower-layer convective clouds grow through the freezing level. This environment is associated with advection of ice particles by negative horizontal velocity within mountain waves over the mountain peak (see Figs. 3 and 5 ). Figure 8 presents the accumulated advection rates of hydrometeors over the mountain peak ( x = 0) as a function of hydrometeor size and height AGL. The advection rate is calculated using the following equation: where ρ H ( r H ) is the size-dependent density function of each hydrometeor, u is the horizontal velocity, and q H is the mixing ratio of each hydrometeor. With higher aerosol number concentration, cloud droplets become smaller and lower-layer convective clouds can reach higher altitude (Figs. 8 a, 8 f, and 8 k). The positive advection rate of columnar ice crystals below approximately 6 km AGL is much higher in PLT, while it is nearly absent in CLN (Figs. 8 b, 8 g, and 8 l) due to stronger freezing and WBF processes over the upslope in PLT compared to CLN (Figs. 7 b–d). Similarly, the negative advection rate in upper layer is larger in CLN than in the other cases. Over the mountain peak, lower-layer convective orographic clouds overlap with upper-layer clouds in PLT (Fig. 5 c). In the upper-layer clouds, many ice crystals grow into snow particles, some of which are advected to the upslope side (Fig. 8 m). These snow particles further grow through riming in the overlapping layer over the upslope (Fig. 7 e), transform into graupel and hail, and are again advected over the mountain peak (Figs. 8 n and 8 o). These graupel and hail particles melt and contribute to surface precipitation (Fig. 7 i). In CLN, however, the overlap between lower and upper clouds is limited to a narrow region over the mountain peak (Fig. 5 a). Although many ice particles generated and grown over the downslope are advected over the mountain peak, their further growth is limited. Moreover, even though riming is active, ice particles in CLN often fail to reach the surface over the downslope (Fig. 7 j). For this reason, the total and maximum precipitation amounts in CLN are lower than those in the other cases. 3.3 Modulation of Aerosol Effects on Orographic Precipitation by Upslope Steepness In Part I, the sensitivity of aerosol effects on orographic precipitation to upslope steepness was examined. As discussed in Part I, Fig. 9 shows that the decrease in precipitation amount and the downstream shift of the location of the maximum precipitation are clear in the cases with a 1 = 5 km (Figs. 9 a–c) and less apparent in the cases with a 1 = 20 km (Figs. 9 d–f) during the early stage ( t = 0–4 h). As time progresses, however, the sensitivity evolves differently depending on the case. In the cases with a 1 = 5 km, the upstream extension of the precipitation area starts earlier in PLTn and CLNn than in CNTn. This results in lighter total and maximum precipitation in CNTn compared to the other cases (Table 2 ). In the cases with a 1 = 20 km, a broad precipitation region over the mountain is present in all cases. In CLNw and PLTw, however, the upstream extension of precipitation area does not occur. This results in heavier total precipitation in CNTw compared to the other cases (Table 2 ). These comparisons collectively reveal a non-monotonic relationship between aerosol number concentration and precipitation characteristics under varying upslope steepness, a pattern also identified in previous studies (e.g., Xiao et al. 2015 ; Chen et al. 2025 ). Such non-monotonicity underscores the complex interactions between orographic dynamics, latent heating of convective clouds, and microphysical processes. Figure 10 shows the averaged mixing ratio of liquid drops, low-density ice particles, and high-density ice particles and wind vectors from t = 4 to 6 h in CLNn, CNTn, and PLTn. In CNTn, narrower lower-layer orographic clouds are generated over the upslope due to the narrower upslope width. Although high-density ice particles affect precipitation over the downslope, the interaction between lower- and upper-layer clouds is weaker compared to the other cases. Surface precipitation is concentrated over the downslope, and precipitation over the upslope is very limited (Fig. 11 a). In CLNn and PLTn, upper-layer clouds extend upstream, which clearly contributes to increased precipitation over the upslope. This upstream extension of upper-layer cloud also appears to facilitate the development of lower-layer clouds, which is more distinct in these cases compared to CNTn (Fig. 10 ). Among all cases, CLNn exhibits the strongest ice-phase precipitation, reflecting favorable upper-layer cloud interactions and enhanced mixed-phase processes. Over both the upslope and downslope, nucleation is strongest in PLTn (Figs. 12 a and 12 f). As discussed in Part I, in these cases, nucleated cloud droplets do not grow sufficiently before reaching the mountain peak due to the short advection time scale over the upslope, even though the steep upslope generates strong convection. For this reason, the mass of cloud droplets remains larger and mass of raindrops is smaller compared to the cases with a symmetric mountain (compare Figs. 8 a, 8 f, and 8 k and Figs. 13 a, 13 f, and 13 k). In the cases with higher aerosol number concentration, strong condensational latent heating produces deeper convective clouds over the upslope and many small liquid drops are frozen above the freezing level (Figs. 12 b and 12 d). Compared to the other cases, the WBF process is strongest in PLTn (Figs. 12 b and 12 c). Similar to PLT, frozen and grown ice particles continue to grow via strong riming over both the upslope and downslope, resulting in heavy surface precipitation through melting or direct sedimentation of ice-phase particles (Figs. 11 a, 12 e, 12 j, and 13 i–o). In CLNn, lower-layer clouds containing liquid drops develop through the freezing level. Because the growth rate of liquid drops is faster (Fig. 13 a), the resulting frozen ice particles are larger compared to the other cases (Fig. 13 b). As a result, the sizes of high-density ice particles become very large, contributing to heavy liquid- and ice-phase precipitation (Figs. 11 a, 13 d, and 13 e). In CNTn, however, weaker condensational latent heating than PLTn and slower drop growth than CLNn result in weaker lower-layer convection (Fig. 13 a). As a result, the interaction between lower- and upper-layer clouds is inhibited over the upslope (Figs. 12 b–e). However, advected liquid drops that pass over the mountain peak continue to grow and freeze over the downslope, and the resulting ice crystals are further enhanced via the WBF process and riming (Figs. 12 g–j). Consequently, precipitation in CNTn is concentrated over the downslope (Fig. 11 a). Figure 14 shows the averaged mixing ratios of the liquid drops, low-density ice particles, and high-density ice particles, along with wind vectors from t = 4 to 6 h in CLNw, CNTw, and PLTw. Over the wide upslope, broader but weaker convective orographic clouds form, compared to the cases with the symmetric mountain and narrow upslope. In CLNw and PLTw, lower- and upper-layer clouds interact only near the mountain peak (Figs. 14 a and 14 c). In CNTw, however, the interaction over the upslope occurs due to the upstream extension of lower-layer clouds (Fig. 14 b). For this reason, the total precipitation amount in CNTw is larger than in CLNw and PLTw (Table 2 ). Surface precipitation is concentrated over the downslope in CLNw and PLTw and is smaller in PLTw than CLNw (Table 2 and Fig. 11 b). As discussed in Part I, cloud droplets over the wide upslope grow sufficiently into raindrops. As a result, the mass of raindrops is larger than the mass of cloud droplets over the mountain peak (Figs. 16 a, 16 f, and 16 k). Similar to the other cases, the nucleation rate is highest in PLTw over both the upslope and downslope (Figs. 15 a and 15 f). In CNTw, many ice crystals are generated from frozen droplets and grow into snow particles via the WBF process at approximately 6 km AGL over the downslope (Figs. 15 g–i). These low-density ice particles are advected toward the upslope side (Figs. 15 g and 15 h). In addition, the freezing rate is strongest in CNTw (Fig. 15 d). Low-density ice particles generated over the upslope and advected from the downslope continue to grow into high-density ice particles through deposition and riming and are again advected downslope side (Figs. 15 b–e and 16 g–j). The melted high-density ice particles enhance surface precipitation via melting. In CLNw and PLTw, however, such mixed-phase processes are inhibited. In CLNw, faster drop growth results in fewer cloud droplets, which serve as the primary source of ice crystals above the freezing level (Fig. 16 a). In PLTw, weak convection associated with the gentle upslope is insufficient for small liquid particles to eventually produce large ice particles (Fig. 16 k). 4 Summary and Conclusions In this study, the sensitivity of aerosol effects on orographic precipitation from deep convective clouds to windward slope steepness is examined using the WRF model coupled with a bin microphysics scheme. Aerosol number concentration and the windward half-width of a bell-shaped mountain are systematically controlled to construct nine simulation cases. During the early stage, orographic precipitation near the mountain is primarily produced by lower-layer clouds. At this stage, the dependencies on aerosol number concentration and upslope steepness resemble those of warm, shallow orographic convection discussed in Part I. As time progresses, however, lower-layer convective clouds vigorously develop and upper-layer mixed-phase clouds extend upstream in some cases. When both strong convection and sufficient vertical cloud overlap occur, the interaction between lower- and upper-layer clouds leads to enhanced surface precipitation via melting or direct sedimentation of high-density ice particles, such as graupel and hail. In the symmetric mountain cases, increasing aerosol number concentration leads to stronger convection and more active mixed-phase microphysical processes (freezing, the WBF process, and riming), resulting in greater total and maximum precipitation amounts. However, under steep and gentle upslope conditions, the trends are non-monotonic. In CLNn, strong convective clouds induced by the steep upslope and the rapid growth of liquid drops and ice particles lead to intense precipitation. In PLTn, although drop growth is slower, strong condensational latent heating compensates for it and produces heavy precipitation. In contrast, in CNTn, both the development of convection and mixed-phase processes are suppressed, resulting in weaker precipitation. In the wide upslope cases, CNTw exhibits enhanced precipitation even with the weak orographic forcing, due to condensational heating that supports vertical cloud development through the freezing level. In CLNw, the low aerosol number concentration limits the formation of cloud droplets which act as the primary source of ice crystals above the freezing level. As a result, the interaction between cloud layers and subsequent precipitation is suppressed. In PLTw, the interaction is also suppressed. This is not due to a lack of condensates, but because the gentle upslope induces weak orographic uplift, limiting the development of convection despite strong aerosol-induced heating. This study adopts a stably stratified atmosphere with high CAPE to allow deep convection to develop and to facilitate controlled orographic cloud formation via forced uplift across a bell-shaped mountain. Without sufficient CAPE, downdrafts associated with mountain waves would likely suppress convection. The results demonstrate that lower-layer cloud development through the freezing level is a key prerequisite for activating mixed-phase interactions and achieving strong surface precipitation. Therefore, vertical instability, in addition to aerosol number concentration and mountain geometry, plays a crucial role in modulating orographic precipitation. Future studies should explore how different vertical soundings influence aerosol-cloud-precipitation interactions under various terrain settings. Declarations Competing interests The authors declare that they have no competing interests. Author Contribution J.-J. Baik designed the numerical experiments. J. M. Seo performed the simulations, analyzed the results, and prepared the main manuscript. Both authors contributed to the interpretation of the results and the revision of the manuscript. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) under grant RS-2025-00562044. The authors thank supercomputer management division of the Korea Meteorological Administration for providing us with the supercomputer resource. References Chavez, S. P., and A. P. 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Seifert, 2005a: Spectral (bin) microphysics coupled with a mesoscale model (MM5). Part I: Model description and first results. Mon. Wea. Rev. , 133 , 44–58. Lynn, B. H., A. Khain, J. Dudhia, D. Rosenfeld, A. Pokrovsky, and A. Seifert, 2005b: Spectral (bin) microphysics coupled with a mesoscale model (MM5). Part II: Simulation of a CaPE rain event with a squall line. Mon. Wea. Rev. , 133 , 59–71. Lynn, B., A. Khain, D. Rosenfeld, and W. L. Woodley, 2007: Effects of aerosols on precipitation from orographic clouds. J. Geophys. Res. , 112 , D10225. Miglietta, M. M., and R. Rotunno, 2009: Numerical simulations of conditionally unstable flows over a mountain ridge. J. Atmos. Sci. , 66 , 1865–1885. Munawar, I., Y. Zhu, M. Wang, D. Rosenfeld, J. Liu, and Y. Wang, 2025: The dominant role of aerosol’s CCN effect in cloud glaciation. npj Clim. Atmos. Sci. , 8 , 121. Pathirana, A., S. Herath, and T. Yamada, 2005: Simulating orographic rainfall with a limited-area, non-hydrostatic atmospheric model under idealized forcing. Atmos. Chem. Phys. , 5 , 215–226. Roe, G. H., and M. B. Baker, 2006: Microphysical and geometrical controls on the pattern of orographic precipitation. J. Atmos. Sci. , 63 , 861–880. Rosenfeld, D., J. Dai, X. Yu, Z. Yao, X. Xu, X. Yang, and C. Du, 2007: Inverse relationship between amounts of air pollution and orographic precipitation. Science , 315 , 1396–1398. Rosenfeld, D., U. Lohmann, G. B. Raga, C. D. O’Dowd, M. Kulmala, S. Fuzzi, A. Reissell, and M. O. Andreae, 2008: Flood or drought: How do aerosols affect precipitation? Science , 321 , 1309–1313. Rosenfeld, D., and A. Givati, 2006: Evidence of orographic precipitation suppression by air pollution-induced aerosols in the western United States. J. Appl. Meteor. Climatol. , 45 , 893–911. Seo, J. M., J.-J. Baik, and S. Moon, 2018: Orographic-convective flows, wave reflection, and gravity-wave momentum fluxes in a two-layer hydrostatic atmosphere. Tellus A: Dyn. Meteorol. Oceanogr. , 70 , 1–16. Seo, J. M., H. Lee, S. Moon, and J.-J. Baik, 2020: How mountain geometry affects aerosol-cloud-precipitation interactions: Part I. Shallow convective clouds. J. Meteorol. Soc. Jap. , 98 , 43-60. Sever, G., and Y.-L. Lin, 2017: Dynamical and physical processes associated with orographic precipitation in a conditionally unstable uniform flow: Variation in basic-state wind speed. J. Atmos. Sci. , 74 , 449–466. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Tech Note, NCAR/TN-475+STR, 8 pp., Natl. Cent. For Atmos. Res., Boulder, Colo. Tao, W.-K., J.-P. Chen, Z. Li, C. Wang, and C. Zhang, 2012: Impact of aerosols on convective clouds and precipitation. Rev. Geophys. , 50 , RG2001. Weisman, M. M., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev. , 110 , 504–520. Xiao, H., Y. Yin, L. Jin, Q. Chen, and J. Chen, 2014: Simulation of aerosol effects on orographic clouds and precipitation using WRF model with a detailed bin microphysics scheme. Atmos. Sci. Lett. , 15 , 134–139. Xiao, H., Y. Yin, L. Jin, Q. Chen, and J. Chen, 2015: Simulation of the effects of aerosol on mixed-phase orographic clouds using the WRF model with a detailed bin microphysics scheme. J. Geophys. Res. Atmos. , 120 , 8345–8358. Xue, H., and G. Feingold, 2006: Large-eddy simulations of trade wind cumuli: Investigation of aerosol indirect effects. J. Atmos. Sci. , 63 , 1605–1622. Xue, L., A. Teller, R. Rasmussen, I. Geresdi, and Z. Pan, 2010: Effects of aerosol solubility and regeneration on warm-phase orographic clouds and precipitation simulated by a detailed bin microphysical scheme. J. Atmos. Sci. , 67 , 3336–3354. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Jun, 2025 Reviews received at journal 31 May, 2025 Reviews received at journal 19 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers invited by journal 01 May, 2025 Editor assigned by journal 29 Apr, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 29 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6553203","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451101438,"identity":"c941a2c7-90af-4a6f-8b28-3bdc0eb02f16","order_by":0,"name":"Jaemyeong Mango Seo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDADfhhDgmgtkg0kazE4QKwW3fazh198+GMjZ3z+7DEJhho7BsnZB/BrMTuTl2Y5sy3N2OxGXpoEw7FkBmm+BAJaDuSYGfM2HE7cdoPHTIKB7QCDHA8Bh5mdf2NmzPPnf+Lm/jNALf+I0XIjx/gxD9uBxA0MOWYSjG0HGKQJa3ljxjizLdlYAqjXIrEvmUeyh6DDcow/fPhjJ8fff8bwxodvdnISZwhoAQI2REwkMDAQchYYMH8gRtUoGAWjYBSMYAAAui09OFv8HjgAAAAASUVORK5CYII=","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Jaemyeong","middleName":"Mango","lastName":"Seo","suffix":""},{"id":451101439,"identity":"87d2d2d2-7f47-43e3-9c60-fd7864491445","order_by":1,"name":"Jong-Jin Baik","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jong-Jin","middleName":"","lastName":"Baik","suffix":""}],"badges":[],"createdAt":"2025-04-29 06:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6553203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6553203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82309284,"identity":"3e43ba44-c141-447a-b656-a0a8169d37bb","added_by":"auto","created_at":"2025-05-09 01:42:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":261367,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the simulations. The dashed, solid, and dotted upslope shapes represent the mountains with narrow windward-width (steep upslope), symmetric width, and wide windward-width (gentle upslope), respectively. Tropopause is located at \u003cem\u003ez\u003c/em\u003e = 12 km, and the sponge layer is located from \u003cem\u003ez\u003c/em\u003e = 15 to 20 km. The vertical profiles of temperature \u003cem\u003eT\u003c/em\u003e, dew point temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e), and relative humidity (\u003cem\u003eH\u003c/em\u003e) are schematically illustrated.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/768b7aaf19c1a394a9f0be98.png"},{"id":82310294,"identity":"990bd202-3a37-48b2-9940-2fbd2ae37379","added_by":"auto","created_at":"2025-05-09 01:50:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":645789,"visible":true,"origin":"","legend":"\u003cp\u003eSkew \u003cem\u003eT\u003c/em\u003e-log \u003cem\u003ep\u003c/em\u003e diagram used in the nine simulations. This sounding is a modified version of the sounding in Weisman and Klemp (1982).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/cebb8ce27c4571001a9611ea.png"},{"id":82308441,"identity":"caac1e3d-02c1-41c9-82d7-ea9b21014ddb","added_by":"auto","created_at":"2025-05-09 01:34:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1182658,"visible":true,"origin":"","legend":"\u003cp\u003eFields of mixing ratios of liquid drops (bluish shading), low-density ice particles (brownish shading), such as ice crystals and snow, and high-density ice particles (black contours), such as graupel and hail, along with wind vectors at \u003cem\u003et\u003c/em\u003e = (a) 1 h, (b) 2 h, (c) 4 h, and (d) 5 h 40 m in CNT. The contour interval of high-density ice particle mixing ratio is 0.02 g kg\u003csup\u003e–1\u003c/sup\u003e. Gray contours show the theoretical vertical velocity field of mountain waves based on the two-layer hydrostatic solution from Seo et al. (2018).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/cf9578d4c56bba1d8875c66e.png"},{"id":82307575,"identity":"55d12c7c-5317-4349-bc9a-88c476f57986","added_by":"auto","created_at":"2025-05-09 01:26:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":806444,"visible":true,"origin":"","legend":"\u003cp\u003eHovmöller diagrams of surface rain rate (shaded) and ice-phase precipitation rate (contoured at 0.01 mm h\u003csup\u003e–1\u003c/sup\u003e) in (a) CLN, (b) CNT, and (c) PLT.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/f61197419142ae343e84cb23.png"},{"id":82308452,"identity":"749b7733-bacd-4781-847c-f41ab1f1a97a","added_by":"auto","created_at":"2025-05-09 01:34:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1069067,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 3, but averaged from \u003cem\u003et\u003c/em\u003e = 4 to 6 h in (a) CLN, (b) CNT, and (c) PLT. The contour interval of high-density ice particle mixing ratio is 0.05 g kg\u003csup\u003e–1\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/2c230a8e6b14989bcc2a11b9.png"},{"id":82307585,"identity":"3b7e35f0-3045-4bd6-9f04-e070f04bb448","added_by":"auto","created_at":"2025-05-09 01:26:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":157806,"visible":true,"origin":"","legend":"\u003cp\u003eAccumulated precipitation amount from \u003cem\u003et\u003c/em\u003e = 4 to 6 h as a function of \u003cem\u003ex\u003c/em\u003e in CLN, CNT, and PLT.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/f00649c39c4af628cd95154f.png"},{"id":82307598,"identity":"93da1060-f7af-4d6c-8188-1411af5e0249","added_by":"auto","created_at":"2025-05-09 01:26:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":688180,"visible":true,"origin":"","legend":"\u003cp\u003eVertical profiles of temperature change due to (a, f) nucleation, (b, g) condensation/evaporation, (c, h) deposition/sublimation, (d, i) freezing/melting, and (e, j) riming over the upslope (\u003cem\u003ex\u003c/em\u003e = –50–0 km) (upper panels) and downslope (\u003cem\u003ex\u003c/em\u003e = 0–50 km) (lower panels) of the mountain in CLN, CNT, and PLT.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/4f273b821dee469f5800a350.png"},{"id":82307612,"identity":"8ddf680c-8877-4e54-98fe-da8071091e2e","added_by":"auto","created_at":"2025-05-09 01:26:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":748523,"visible":true,"origin":"","legend":"\u003cp\u003eAccumulated advection rates of (a, f, h) liquid drops, (b, g, l) columnar ice crystals, (c, h, m) snow, (d, i, n) graupel, and (e, j, o) hail across \u003cem\u003ex\u003c/em\u003e= 0 as a function of hydrometeor size and height AGL in CLN (upper panels), CNT (middle panels), and PLT (lower panels). Dashed lines in (a, f, k) indicate \u003cem\u003er \u003c/em\u003e= 40 μm, the threshold size separating cloud droplets and raindrops.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/ec5e03b565c5ac72ebb0be1b.png"},{"id":82308448,"identity":"5bd0e2b4-73d7-4500-bed0-024b2b9d0e56","added_by":"auto","created_at":"2025-05-09 01:34:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1472750,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 4 except for (a) CLNn, (b) CNTn, (c) PLTn, (d) CLNw, (e) CNTw, and (f) PLTw.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/d9361fcfa9f323a0cc1d5034.png"},{"id":82307607,"identity":"4f5e2032-a9b5-455e-81a6-afaf6f9972f0","added_by":"auto","created_at":"2025-05-09 01:26:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1251298,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 5 except for (a) CLNn, (b) CNTn, and (c) PLTn.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/05a85b71f4e6a581d8380f25.png"},{"id":82307593,"identity":"4aa880da-0252-49f1-aeb8-a6a2a921f142","added_by":"auto","created_at":"2025-05-09 01:26:52","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":336019,"visible":true,"origin":"","legend":"\u003cp\u003eAccumulated liquid (black) and ice (gray) precipitation amounts from \u003cem\u003et\u003c/em\u003e = 4 to 6 h as a function of \u003cem\u003ex\u003c/em\u003e in the cases with (a) \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 5 km and (b) 20 km.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/ac95b85afc02c6d7eb42b5af.png"},{"id":82309286,"identity":"c1437c01-5f94-486c-9751-2ac30acd5ced","added_by":"auto","created_at":"2025-05-09 01:42:52","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":668540,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 7 except for the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 5 km.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/2dfe85caff52d3a54d3aa7ba.png"},{"id":82308446,"identity":"eefa0a7f-a221-4c8e-a52c-737e2bc976be","added_by":"auto","created_at":"2025-05-09 01:34:51","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":769622,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 8 except for the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 5 km.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/da275db4b4a432d764e78471.png"},{"id":82307592,"identity":"f7a17d66-8761-41c7-a2ad-e445a95e34b3","added_by":"auto","created_at":"2025-05-09 01:26:52","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1078331,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 5 except for (a) CLNw, (b) CNTw, and (c) PLTw.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/fb4b0e634eb66db2797598ea.png"},{"id":82308451,"identity":"6b8a28c2-49f8-41b9-9969-c7bf7ded5291","added_by":"auto","created_at":"2025-05-09 01:34:52","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":655874,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 7 except for the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 20 km.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/8fda85d2b271938155e73301.png"},{"id":82307604,"identity":"ff6bbb49-dbc2-4d3d-a1e0-da8960af22b3","added_by":"auto","created_at":"2025-05-09 01:26:53","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":746591,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 8 except for the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 20 km.\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/e8e90c55818a90e433291d3a.png"},{"id":82311656,"identity":"db0bd44c-a248-4d3e-a8ab-efc591f2049a","added_by":"auto","created_at":"2025-05-09 01:58:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13365957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6553203/v1/41a109fc-3556-484b-926d-70ef95e21948.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Mountain Geometry Affects Aerosol-Cloud-Precipitation Interactions: Part II. Deep Convective Clouds","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBecause aerosol particles act as cloud condensation nuclei (CCN) or ice nuclei (IN) in the atmosphere, aerosol loading controls precipitation characteristics through complex aerosol-cloud-precipitation interactions. To understand the role of aerosol particles in controlling precipitation characteristics, many researchers have investigated the impacts of aerosols on clouds and precipitation extensively (e.g., Khain \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tao et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These aerosol effects can become particularly complex in mountainous regions.\u003c/p\u003e \u003cp\u003ePrecipitation over mountainous regions, known as orographic precipitation, is affected by various factors, including mountain geometry as well as background static stability, wind speed, and humidity (Colle \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pathirana et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Roe and Baker \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, the sensitivity of orographic precipitation to the environmental factors and mountain geometry is generally focused on orographic precipitation from shallow clouds; because in a broad range of the environmental factors and mountain geometry, downdrafts involved in mountain waves restrict convective development of orographic clouds. Several studies have investigated orographic precipitation in the conditionally unstable atmosphere and have categorized the behavior of convective orographic clouds depending on convective available potential energy (CAPE), mountain width, and the Froude number (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eU\u003c/em\u003e/\u003cem\u003eNh\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e), where \u003cem\u003eU\u003c/em\u003e is the background wind speed, \u003cem\u003eN\u003c/em\u003e is the background buoyancy frequency, and \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e is the maximum height of the mountain (e.g., Chu and Lin \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chen and Lin \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Miglietta and Rotunno \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sever and Lin \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany previous studies have shown that the increase in aerosol number concentration usually results in decrease in surface precipitation from shallow convection (Xue and Feingold \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and in increase in surface precipitation from deep convection (Khain et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rosenfeld et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Clavner et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent studies have further refined this understanding by highlighting the dominant role of CCN in glaciation (Munawar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and the contrasting effects of different aerosol types on cloud microphysics (Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn orographic convection, aerosol-cloud-precipitation interactions exhibit greater complexity due to terrain-modulated airflow and microphysical variability. Many studies have shown that air pollution causes suppression of orographic precipitation (Givati and Rosenfeld \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jirak and Cotton 2006; Rosenfeld and Givati \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rosenfeld et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Xiao et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) showed that the increase in aerosol number concentration results in the enhanced orographic precipitation, but the effects on deep convective orographic precipitation have been less studied. The recent work by Chavez and Barros (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further indicates that aerosol indirect effects can shift the spatial distribution of orographic precipitation, especially by modifying cloud longevity and riming processes along windward slopes.\u003c/p\u003e \u003cp\u003eBin microphysics models predict the evolution of each size bin of each hydrometeor separately, allowing detailed tracking of microphysical growth and phase changes (e.g., Khain et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lynn et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005a\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003eb\u003c/span\u003e). To investigate the microphysical processes under orographic precipitation more precisely, numerical studies using bin microphysics schemes have been conducted (Lynn et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xiao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, most of these studies have focused on shallow convective orographic clouds, where warm rain processes dominate. Relatively few studies have used bin schemes to examine deep orographic convection involving mixed-phase microphysics; Xiao et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), for example, investigated aerosol effects on mixed-phase orographic precipitation, but such work remains limited.\u003c/p\u003e \u003cp\u003eThis study aims to investigate how the windward mountain slope modulates aerosol effects on orographic precipitation from deep convective clouds. Section 2 describes the experimental design of the simulations. Sections 3.1 and 3.2 present the characteristics of deep convective orographic precipitation and its sensitivity to aerosol number concentration. Section 3.3 discusses how aerosol effects vary with changes in the windward mountain slope. Section 4 provides a summary and concluding remarks.\u003c/p\u003e"},{"header":"2 Simulation Design","content":"\u003cp\u003eIn this study, the Weather Research and Forecasting (WRF) model, version 3.6.1, coupled with the Hebrew University Cloud Model (HUCM) is used (Skamarock et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lee and Baik \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The model is the same as in Part I (Seo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), except that mixed-phase microphysical processes are included. This model predicts seven types of hydrometeors [liquid drops, ice crystals (column, plate, and dendrite), snow, graupel, and hail] as well as aerosols, which are subdivided into 43 mass-doubling bins. Detailed descriptions of HUCM are provided in Khain et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo-dimensional simulations are conducted to examine orographic precipitation from deep convective clouds. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the schematic of the simulations. The bell-shaped mountain, defined as below, triggers orographic clouds by forced uplift.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"576\" height=\"74\"\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e = 2 km is the maximum height and \u003cem\u003ea\u003c/em\u003e (=\u0026thinsp;\u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e for \u003cem\u003ex\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0 km and =\u0026thinsp;\u003cem\u003ea\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;10 km for \u003cem\u003ex\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0 km) is the half-width of the bell-shaped mountain. As in Part I, we classify simulation cases by aerosol number concentration at 1% supersaturation \u003cem\u003eN\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e [=\u0026thinsp;100 cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e in CLN (clean), = 500 cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e in CNT (control), and =\u0026thinsp;2500 cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e in PLT (polluted)] and the windward width (upslope steepness) of the mountain \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e (=\u0026thinsp;5 km for narrow, = 10 km for control, and =\u0026thinsp;20 km for wide). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the name and specific settings for each case. The background wind speed \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10 m s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e is constant in the vertical.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNames of the nine simulation cases and the corresponding aerosol number concentrations (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) and windward half-widths (\u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e) of the mountain.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e (km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLNn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNTn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLTn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLNw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNTw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLTw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo generate vigorous convective orographic clouds, a modified version of Weisman and Klemp (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) sounding is used. In this study, the potential temperature \u003cem\u003eθ\u003c/em\u003e and relative humidity \u003cem\u003eH\u003c/em\u003e are given as follows:\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"575\" height=\"248\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;298.15 K and \u003cem\u003eθ\u003c/em\u003e\u003csub\u003etr\u003c/sub\u003e [\u003cem\u003eH\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (=\u0026thinsp;0.9) and \u003cem\u003eH\u003c/em\u003e\u003csub\u003etr\u003c/sub\u003e] are the potential temperature (relative humidity) at the surface and the tropopause height \u003cem\u003ez\u003c/em\u003e\u003csub\u003etr\u003c/sub\u003e (=\u0026thinsp;12 km), respectively. \u003cem\u003eT\u003c/em\u003e\u003csub\u003etr\u003c/sub\u003e is the temperature at the tropopause, \u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the specific heat of air at constant pressure, \u003cem\u003eN\u003c/em\u003e (=\u0026thinsp;0.01 s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) is the buoyancy frequency, and \u003cem\u003eg\u003c/em\u003e is the gravitational acceleration. By fixing \u003cem\u003eN\u003c/em\u003e, the Froude number is easily controlled. The skew \u003cem\u003eT\u003c/em\u003e-log \u003cem\u003ep\u003c/em\u003e diagram of this sounding is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The lifting condensation level (LCL\u0026thinsp;~\u0026thinsp;300 m) is located below the mountain top, which enables orographic cloud formation through forced uplift. Although the background atmosphere is statically stable (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01 s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), the sounding becomes highly unstable after saturation, with CAPE reaching 4457 J kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e. This allows deep and strong convection to develop through the tropopause.\u003c/p\u003e \u003cp\u003eA horizontal domain of 200 km with a horizontal grid size of 250 m is used. The vertical domain size is 20 km with a 5-km sponge layer and 401 terrain-following levels. An open lateral boundary condition is applied in the horizontal. Each case is integrated for 12 h using the WRF-bin model with a time step of 0.6 s. However, only the first 6 h of results are analyzed, for reasons discussed in Section 3.1. Except for the bin microphysics scheme and basic turbulent/diffusion parameterization, other parameterizations are not used.\u003c/p\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General Characteristics of the Simulated Orographic Precipitation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the mixing ratios of liquid drops (bluish shading), low-density ice particles (brownish shading), such as ice crystals and snow, and high-density ice particles (black contours), such as graupel and hail, along with wind vectors in the CNT case. In addition, gray contours in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e represent the theoretical vertical velocity field associated with mountain waves, which is calculated using the linear analytic solution of Seo et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This solution assumes a two-layer hydrostatic atmosphere where the buoyancy frequency \u003cem\u003eN\u003c/em\u003e is 0.01 s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in the troposphere and 0.02 s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in the stratosphere, with a tropopause height of 12 km. Other parameters are consistent with the simulation setup: \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10 m s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, \u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10 km, and \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e = 2 km. This theoretical vertical wave structure is overlaid to evaluate how the simulated wind field and cloud structures align with the idealized mountain wave pattern. Contour intervals is set to 0.1 m s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrom the early stage, orographic clouds are generated over the upslope. In the subcritical condition (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1, where \u003cem\u003eF\u003c/em\u003e is the Froude number), flows converge into a thin layer and accelerate over the downslope. As a result, a hydraulic jump occurs, and strong updrafts are generated over the downslope (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Although the simulated wind field in this stage exhibits general subsidence over the windward slope, it does not yet align well with the idealized mountain wave structure shown in the gray contours. This discrepancy is likely due to early transient convective activity near the mountain. The deep convective system is advected downstream by downslope wind and results in heavy precipitation on the lee of the mountain (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). As the transiently generated deep convective system moves out of the depicted region, the simulated wind field begins to exhibit more organized mountain wave patterns, gradually resembling with the theoretical structure calculated using the analytic solution. Following this transition, two vertically separated cloud layers become apparent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe lower layer consists mainly of orographic clouds with liquid drops, while the upper layer is dominated by low-density ice particles. In the stratified atmosphere, downdrafts associated with mountain waves inhibit the vertical development of shallow orographic clouds. In the upper layer over the lee side, particularly between 8 and 11 km altitude, weak updrafts generated by the wave structure facilitate the growth of pre-existing ice crystals into snow particles. Since these updrafts coincide with the negative perturbation horizontal wind, upper-layer clouds containing low-density ice particles extend upstream when the perturbation wind speed exceeds the background wind speed. After \u003cem\u003et\u003c/em\u003e\u0026thinsp;~\u0026thinsp;4 h, lower-level stratiform orographic clouds transition into cellular-type convective orographic clouds due to condensational latent heating and extend upstream. Occasionally, some convective cells deepen and interact with upper-layer mixed-phase clouds. This interaction leads to intermittent heavy precipitation caused by melting of high-density ice particles or direct precipitation of graupel or hail (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Hovm\u0026ouml;ller diagrams of surface rain rate (shaded) and ice-phase precipitation rate (contoured at 0.01 mm h\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) in CLN, CNT, and PLT. As described earlier, the transiently generated deep convective system is advected downstream, while steady orographic precipitation occurs near the mountain. This pattern corresponds to Regime III in Chu and Lin (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In CNT and PLT, orographic precipitation extends upstream after \u003cem\u003et\u003c/em\u003e\u0026thinsp;~\u0026thinsp;5 h. In all cases, ice-phase precipitation becomes dominant near and downstream of the mountain peak after \u003cem\u003et\u003c/em\u003e\u0026thinsp;~\u0026thinsp;6 h, as the freezing level gradually decreases over time. In this study, we focus on how aerosol number concentration and upslope steepness modulate surface rainfall over the mountainous terrain through interactions between deep convective orographic clouds and upper-level mixed-phase clouds. Therefore, averaged and accumulated variables from \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 to 6 h are analyzed to avoid the initial period dominated by the transiently developed deep convective system (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u0026ndash;4 h) and the later stage dominated by ice-phase precipitation (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6\u0026ndash;12 h).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Aerosol Effects on Orographic Precipitation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the averaged mixing ratios of liquid drops, low-density ice particles, and high-density particles, along with wind vectors from \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 to 6 h in CLN, CNT, and PLT. During this period, lower-level convective orographic clouds develop over the mountain. In the cases with higher aerosol number concentration, a larger number of condensates results in stronger condensational latent heating, which leads to deeper and stronger convection. As a result, more liquid drops can interact with ice particles in the upper-layer cloud and freeze into ice crystals above the freezing level. These processes increase the mixing ratio of high-density ice particles in the cases with high aerosol number concentration. The high-density ice particles grow via riming in both lower- and upper-layer clouds and enhance surface precipitation through melting or direct sedimentation. This process is consistent with the findings of Xiao et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who demonstrated that high aerosol number concentrations promote the upward transport of small supercooled liquid droplets, enhancing freezing and riming above the freezing level and thereby increasing precipitation efficiency. These results are also consistent with Munawar et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who emphasized the dominant role of CCN in initiating glaciation. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that increasing aerosol number concentration leads to greater total and maximum precipitation amounts and an upstream shift in the location of the maximum precipitation. This dependency of precipitation characteristics on aerosol number concentration is opposite to the shallow, warm orographic precipitation case described in Part I. However, an increase in total and maximum precipitation amounts caused by an increase in aerosol loading has also been reported in previous studies (Khain et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rosenfeld et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Xiao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Clavner et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal accumulated surface precipitation amount over 6 h (\u003cem\u003eP\u003c/em\u003e\u003csub\u003etot\u003c/sub\u003e), maximum 6-h surface precipitation amount (\u003cem\u003eP\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e), and the \u003cem\u003ex\u003c/em\u003e-location where \u003cem\u003eP\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e occurs (\u003cem\u003ex\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e) in the nine simulation cases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etot\u003c/sub\u003e (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e (km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026ndash;2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epolluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the vertical profiles of temperature change due to microphysical processes over the upslope (\u003cem\u003ex\u003c/em\u003e = \u0026minus;\u0026thinsp;50\u0026ndash;0 km) and downslope (\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u0026ndash;50 km) of the mountain. On both sides, nucleation is strongest in PLT. Over the upslope, the nucleation rate decreases with height, while over the downslope it peaks around 5 km above ground level (AGL) (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). A higher number of condensates over the upslope in PLT leads to stronger condensational latent heating there and also stronger evaporative cooling over the downslope, compared to the other cases (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eg). Over the upslope, many liquid drops freeze into ice crystals (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), which then grow through the Wegener-Bergeron-Findeisen (WBF) process in PLT (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). This process is followed by intensive riming, which induces strong localized heating over the upslope in PLT (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ee), where riming is most active across nearly all altitudes. In contrast, riming is relatively suppressed over the downslope in PLT (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ej). This distribution suggests that the riming zone in PLT has shifted upstream, a feature observed in the case with high aerosol number concentration. Such spatial displacement of the riming zone resembles the mechanism proposed by Chavez and Barros (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), in which aerosols shift riming activity upwind, extending the hydrometeor growth pathway and enhancing precipitation. In CLN, similar processes, intense freezing and the WBF process, are strongest over the downslope compared to the other cases (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eg\u0026ndash;i). Riming between ice-phase particles and liquid drops primarily contributes to enhanced surface precipitation through mixed-phase processes (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ee and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ej).\u003c/p\u003e \u003cp\u003eDuring these processes, the upstream extension of the upper-layer cloud creates an environment that increases the mixing ratio of high-density ice particles via riming when lower-layer convective clouds grow through the freezing level. This environment is associated with advection of ice particles by negative horizontal velocity within mountain waves over the mountain peak (see Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the accumulated advection rates of hydrometeors over the mountain peak (\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0) as a function of hydrometeor size and height AGL. The advection rate is calculated using the following equation:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"570\" height=\"52\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eρ\u003c/em\u003e\u003csub\u003e\u003cem\u003eH\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003eH\u003c/em\u003e\u003c/sub\u003e) is the size-dependent density function of each hydrometeor, \u003cem\u003eu\u003c/em\u003e is the horizontal velocity, and \u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eH\u003c/em\u003e\u003c/sub\u003e is the mixing ratio of each hydrometeor.\u003c/p\u003e \u003cp\u003eWith higher aerosol number concentration, cloud droplets become smaller and lower-layer convective clouds can reach higher altitude (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ef, and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ek). The positive advection rate of columnar ice crystals below approximately 6 km AGL is much higher in PLT, while it is nearly absent in CLN (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eg, and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003el) due to stronger freezing and WBF processes over the upslope in PLT compared to CLN (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb\u0026ndash;d). Similarly, the negative advection rate in upper layer is larger in CLN than in the other cases. Over the mountain peak, lower-layer convective orographic clouds overlap with upper-layer clouds in PLT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In the upper-layer clouds, many ice crystals grow into snow particles, some of which are advected to the upslope side (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003em). These snow particles further grow through riming in the overlapping layer over the upslope (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ee), transform into graupel and hail, and are again advected over the mountain peak (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003en and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eo). These graupel and hail particles melt and contribute to surface precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ei). In CLN, however, the overlap between lower and upper clouds is limited to a narrow region over the mountain peak (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Although many ice particles generated and grown over the downslope are advected over the mountain peak, their further growth is limited. Moreover, even though riming is active, ice particles in CLN often fail to reach the surface over the downslope (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ej). For this reason, the total and maximum precipitation amounts in CLN are lower than those in the other cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Modulation of Aerosol Effects on Orographic Precipitation by Upslope Steepness\u003c/h2\u003e \u003cp\u003eIn Part I, the sensitivity of aerosol effects on orographic precipitation to upslope steepness was examined. As discussed in Part I, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows that the decrease in precipitation amount and the downstream shift of the location of the maximum precipitation are clear in the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5 km (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003ea\u0026ndash;c) and less apparent in the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;20 km (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003ed\u0026ndash;f) during the early stage (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u0026ndash;4 h). As time progresses, however, the sensitivity evolves differently depending on the case. In the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5 km, the upstream extension of the precipitation area starts earlier in PLTn and CLNn than in CNTn. This results in lighter total and maximum precipitation in CNTn compared to the other cases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the cases with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;20 km, a broad precipitation region over the mountain is present in all cases. In CLNw and PLTw, however, the upstream extension of precipitation area does not occur. This results in heavier total precipitation in CNTw compared to the other cases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These comparisons collectively reveal a non-monotonic relationship between aerosol number concentration and precipitation characteristics under varying upslope steepness, a pattern also identified in previous studies (e.g., Xiao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such non-monotonicity underscores the complex interactions between orographic dynamics, latent heating of convective clouds, and microphysical processes.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the averaged mixing ratio of liquid drops, low-density ice particles, and high-density ice particles and wind vectors from \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 to 6 h in CLNn, CNTn, and PLTn. In CNTn, narrower lower-layer orographic clouds are generated over the upslope due to the narrower upslope width. Although high-density ice particles affect precipitation over the downslope, the interaction between lower- and upper-layer clouds is weaker compared to the other cases. Surface precipitation is concentrated over the downslope, and precipitation over the upslope is very limited (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003ea). In CLNn and PLTn, upper-layer clouds extend upstream, which clearly contributes to increased precipitation over the upslope. This upstream extension of upper-layer cloud also appears to facilitate the development of lower-layer clouds, which is more distinct in these cases compared to CNTn (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Among all cases, CLNn exhibits the strongest ice-phase precipitation, reflecting favorable upper-layer cloud interactions and enhanced mixed-phase processes.\u003c/p\u003e \u003cp\u003eOver both the upslope and downslope, nucleation is strongest in PLTn (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003ea and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003ef). As discussed in Part I, in these cases, nucleated cloud droplets do not grow sufficiently before reaching the mountain peak due to the short advection time scale over the upslope, even though the steep upslope generates strong convection. For this reason, the mass of cloud droplets remains larger and mass of raindrops is smaller compared to the cases with a symmetric mountain (compare Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ef, and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ek and Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ea, \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ef, and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ek). In the cases with higher aerosol number concentration, strong condensational latent heating produces deeper convective clouds over the upslope and many small liquid drops are frozen above the freezing level (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eb and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003ed). Compared to the other cases, the WBF process is strongest in PLTn (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eb and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003ec). Similar to PLT, frozen and grown ice particles continue to grow via strong riming over both the upslope and downslope, resulting in heavy surface precipitation through melting or direct sedimentation of ice-phase particles (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003ea, \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003ee, \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003ej, and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ei\u0026ndash;o).\u003c/p\u003e \u003cp\u003eIn CLNn, lower-layer clouds containing liquid drops develop through the freezing level. Because the growth rate of liquid drops is faster (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ea), the resulting frozen ice particles are larger compared to the other cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003eb). As a result, the sizes of high-density ice particles become very large, contributing to heavy liquid- and ice-phase precipitation (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003ea, \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ed, and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ee). In CNTn, however, weaker condensational latent heating than PLTn and slower drop growth than CLNn result in weaker lower-layer convection (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). As a result, the interaction between lower- and upper-layer clouds is inhibited over the upslope (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eb\u0026ndash;e). However, advected liquid drops that pass over the mountain peak continue to grow and freeze over the downslope, and the resulting ice crystals are further enhanced via the WBF process and riming (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eg\u0026ndash;j). Consequently, precipitation in CNTn is concentrated over the downslope (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows the averaged mixing ratios of the liquid drops, low-density ice particles, and high-density ice particles, along with wind vectors from \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 to 6 h in CLNw, CNTw, and PLTw. Over the wide upslope, broader but weaker convective orographic clouds form, compared to the cases with the symmetric mountain and narrow upslope. In CLNw and PLTw, lower- and upper-layer clouds interact only near the mountain peak (Figs.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003ea and \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003ec). In CNTw, however, the interaction over the upslope occurs due to the upstream extension of lower-layer clouds (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003eb). For this reason, the total precipitation amount in CNTw is larger than in CLNw and PLTw (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Surface precipitation is concentrated over the downslope in CLNw and PLTw and is smaller in PLTw than CLNw (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAs discussed in Part I, cloud droplets over the wide upslope grow sufficiently into raindrops. As a result, the mass of raindrops is larger than the mass of cloud droplets over the mountain peak (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e16\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e16\u003c/span\u003ef, and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e16\u003c/span\u003ek). Similar to the other cases, the nucleation rate is highest in PLTw over both the upslope and downslope (Figs.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003ea and \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003ef). In CNTw, many ice crystals are generated from frozen droplets and grow into snow particles via the WBF process at approximately 6 km AGL over the downslope (Figs.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eg\u0026ndash;i). These low-density ice particles are advected toward the upslope side (Figs.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eg and \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eh). In addition, the freezing rate is strongest in CNTw (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003ed). Low-density ice particles generated over the upslope and advected from the downslope continue to grow into high-density ice particles through deposition and riming and are again advected downslope side (Figs.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eb\u0026ndash;e and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e16\u003c/span\u003eg\u0026ndash;j). The melted high-density ice particles enhance surface precipitation via melting. In CLNw and PLTw, however, such mixed-phase processes are inhibited. In CLNw, faster drop growth results in fewer cloud droplets, which serve as the primary source of ice crystals above the freezing level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e16\u003c/span\u003ea). In PLTw, weak convection associated with the gentle upslope is insufficient for small liquid particles to eventually produce large ice particles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e16\u003c/span\u003ek).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Summary and Conclusions","content":"\u003cp\u003eIn this study, the sensitivity of aerosol effects on orographic precipitation from deep convective clouds to windward slope steepness is examined using the WRF model coupled with a bin microphysics scheme. Aerosol number concentration and the windward half-width of a bell-shaped mountain are systematically controlled to construct nine simulation cases.\u003c/p\u003e \u003cp\u003eDuring the early stage, orographic precipitation near the mountain is primarily produced by lower-layer clouds. At this stage, the dependencies on aerosol number concentration and upslope steepness resemble those of warm, shallow orographic convection discussed in Part I. As time progresses, however, lower-layer convective clouds vigorously develop and upper-layer mixed-phase clouds extend upstream in some cases. When both strong convection and sufficient vertical cloud overlap occur, the interaction between lower- and upper-layer clouds leads to enhanced surface precipitation via melting or direct sedimentation of high-density ice particles, such as graupel and hail.\u003c/p\u003e \u003cp\u003eIn the symmetric mountain cases, increasing aerosol number concentration leads to stronger convection and more active mixed-phase microphysical processes (freezing, the WBF process, and riming), resulting in greater total and maximum precipitation amounts. However, under steep and gentle upslope conditions, the trends are non-monotonic. In CLNn, strong convective clouds induced by the steep upslope and the rapid growth of liquid drops and ice particles lead to intense precipitation. In PLTn, although drop growth is slower, strong condensational latent heating compensates for it and produces heavy precipitation. In contrast, in CNTn, both the development of convection and mixed-phase processes are suppressed, resulting in weaker precipitation.\u003c/p\u003e \u003cp\u003eIn the wide upslope cases, CNTw exhibits enhanced precipitation even with the weak orographic forcing, due to condensational heating that supports vertical cloud development through the freezing level. In CLNw, the low aerosol number concentration limits the formation of cloud droplets which act as the primary source of ice crystals above the freezing level. As a result, the interaction between cloud layers and subsequent precipitation is suppressed. In PLTw, the interaction is also suppressed. This is not due to a lack of condensates, but because the gentle upslope induces weak orographic uplift, limiting the development of convection despite strong aerosol-induced heating.\u003c/p\u003e \u003cp\u003eThis study adopts a stably stratified atmosphere with high CAPE to allow deep convection to develop and to facilitate controlled orographic cloud formation via forced uplift across a bell-shaped mountain. Without sufficient CAPE, downdrafts associated with mountain waves would likely suppress convection. The results demonstrate that lower-layer cloud development through the freezing level is a key prerequisite for activating mixed-phase interactions and achieving strong surface precipitation. Therefore, vertical instability, in addition to aerosol number concentration and mountain geometry, plays a crucial role in modulating orographic precipitation. Future studies should explore how different vertical soundings influence aerosol-cloud-precipitation interactions under various terrain settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.-J. Baik designed the numerical experiments. J. M. Seo performed the simulations, analyzed the results, and prepared the main manuscript. Both authors contributed to the interpretation of the results and the revision of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) under grant RS-2025-00562044. The authors thank supercomputer management division of the Korea Meteorological Administration for providing us with the supercomputer resource.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChavez, S. P., and A. P. Barros, 2023: Aerosol indirect effects on orographic clouds and precipitation. \u003cem\u003eFront Earth Sci.\u003c/em\u003e, \u003cstrong\u003e11\u003c/strong\u003e, 1025266.\u003c/li\u003e\n\u003cli\u003eChen, F., Y. Yang, L. Yu, Y. Li, W. Liu, Y. Liu, and S. Lolli, 2025: Distinct effects of fine and coarse aerosols on microphysical processes of shallow-precipitation systems in summer over southern China. \u003cem\u003eAtmos. Chem. Phys.\u003c/em\u003e, \u003cstrong\u003e25\u003c/strong\u003e, 1587\u0026ndash;1601.\u003c/li\u003e\n\u003cli\u003eChen, S.-H., and Y.-L. Lin, 2005: Orographic effects on a conditionally unstable flow over an idealized three-dimensional mesoscale mountain. \u003cem\u003eMeteorol. Atmos. 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Sci.\u003c/em\u003e, \u003cstrong\u003e67\u003c/strong\u003e, 3336\u0026ndash;3354.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-meteorological-society-of-japan","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44394","submissionUrl":"https://submission.springernature.com/new-submission/44394/3","title":"Journal of the Meteorological Society of Japan","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"aerosol-cloud-precipitation interactions, aerosol number concentration, mountain upslope geometry, orographic precipitation, bin microphysics","lastPublishedDoi":"10.21203/rs.3.rs-6553203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6553203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe sensitivity of aerosol effects on orographic precipitation from deep convective clouds to mountain upslope steepness is examined using the Weather Research and Forecasting (WRF) model coupled with a bin microphysics scheme. During the early stage, the sensitivity resembles that of warm, shallow orographic convection as discussed in Part I. As time progresses, interactions between vigorously developed lower-layer clouds and upstream-extending upper-layer clouds become crucial for enhancing surface precipitation via melting and direct sedimentation of ice-phase particles such as graupel and hail. In the symmetric mountain cases, higher aerosol number concentration enhances surface precipitation through stronger condensational latent heating and more active mixed-phase processes (freezing, Wegener-Bergeron-Findeisen process, and riming). Under asymmetric mountain configurations, however, the sensitivities are non-monotonic. In the steep upslope cases, fast liquid drop growth in the clean case and strong latent heating in the polluted case both support cloud development and enhance precipitation. In contrast, the control case exhibits weaker precipitation due to slower drop growth than the clean case and weaker heating than the polluted case, suppressing the cloud interaction. In the gentle upslope cases, the control case shows enhanced precipitation due to sufficient droplet supply and latent heating which help clouds grow through the freezing level. In contrast, the clean case lacks sufficient cloud droplets, while the polluted case suffers from weak convection despite strong aerosol-induced heating. In both cases, the cloud interaction and mixed-phase processes are suppressed.\u003c/p\u003e","manuscriptTitle":"How Mountain Geometry Affects Aerosol-Cloud-Precipitation Interactions: Part II. Deep Convective Clouds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 01:26:46","doi":"10.21203/rs.3.rs-6553203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T03:30:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-31T14:50:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T00:18:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181191660654675589971615264651110397584","date":"2025-05-08T17:12:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232087347491476561485661387337367840967","date":"2025-05-06T23:55:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-02T03:11:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T09:02:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T09:01:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of the Meteorological Society of Japan","date":"2025-04-29T06:35:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-meteorological-society-of-japan","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44394","submissionUrl":"https://submission.springernature.com/new-submission/44394/3","title":"Journal of the Meteorological Society of Japan","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a015e73f-8553-4902-aafb-a756bf6853e5","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-16T02:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 01:26:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6553203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6553203","identity":"rs-6553203","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}