Supported by the Scientific Research Project of the Bayingol Mongolian Autonomous Prefecture in Xinjiang (202318), China Meteorological Administration (CMA) Weather Modification Centre Innovation Team Project (WMC2023IT01), and CMA Key Innovation Team Project (CMA2022ZD10).
In this study, we employed a three-dimensional mesoscale cold-cloud seeding model to simulate the microphysical impacts of artificial ice crystals used as cloud seeding catalysts. Our objective was to elucidate the mechanism of snowfall enhancement in stratiform clouds in the Bayanbulak test area of Xinjiang, China. The results indicated that the optimal seeding time was the early stages of weather system development. In this case, the optimal seeding zone was identified as the northwest of the test area, especially near the cloud top (altitudes between 3500 and 4000 m, temperatures range −11 to −15°C), and the ideal concentration of catalyst was with ice crystal density of 1.0 × 107 kg−1 within the target area. Under such conditions, the total precipitation rate in the seeding-affected area increased to 50.1 mm h−1. The results also showed that the favorable seeding region was featured by high content of supercooled water and low population of natural ice crystals, where artificial ice crystals could substantially increase the snowfall. This augmentation typically appeared in a unimodal pattern, with the peak formed within 2–3 h after seeding. Seeding in the ice–water mixed zone of a supercooled cloud facilitated rapid ice crystal growth to snowflake pieces via the Bergeron process, which in turn consumed more supercooled water via collision–coalescence with cloud water droplets. Simultaneously, the intensive consumption of supercooled water impeded the riming process and reduced the formation of graupel particles within the cloud. The dispersion of artificial ice crystals extended over tens of kilometers horizontally; however, in the vertical direction most particles remained approximately 1 km below the seeding layer, due to limited vertical ascent rate in the stratiform clouds restricting upward movement of artificial ice crystals. The above results help better understand the snowfall enhancement mechanism in stratiform clouds and facilitate related weather modification practice.
In recent years, enhanced global warming has led to marked imbalance in precipitation patterns (Su et al., 2020). This phenomenon is particularly pronounced in regions such as Bayanbulak in Xinjiang, China, where low amounts of precipitation contribute to severe water shortages. Freshwater resources, vital for regional ecosystems, require scientific and efficient management to mitigate the effects of such water scarcity problems. Since the first effective test seeding of stratiform cloud (Schaefer, 1946), subsequent seeding tests have been undertaken globally (Vonnegut, 1947; Hsie et al., 1980; Kopp et al., 1983; Farley et al., 1994; Bruintjes, 1999; Qiu and Cressey, 2008; Zhao et al., 2018; Dong et al., 2020, 2021; Geresdi et al., 2020). Stratiform cloud systems are one of the key targets for the development and utilization of cloud water using techniques for artificial enhancement of precipitation (Liu et al., 2021b). However, implementing such techniques necessitates scientific and feasible technical guidelines to ensure their effectiveness. Notably, not all regions within a stratiform cloud system are conducive to artificial enhancement of precipitation (Liu et al., 2021a; Li et al., 2022). Consequently, conducting reasonable and precise assessment of the outcomes of artificial enhancement of precipitation (snow) is crucial. Observation-based assessment has been widely investigated. The coexistence of supercooled liquid water and ice crystals was particularly suitable for cloud seeding. Higher cloud particle number concentration with larger ice crystals appeared in the seeding influenced region, and liquid water content (LWC) dropped obviously (Yang et al., 2022). On the horizontal scale, the radar echoes were relatively loose before seeding, but tended to increase and concentrate after seeding (Li et al., 2024). Addressing this challenge also requires establishing and refining seeding operation technologies. Furthermore, employing numerical models to simulate seeding processes provides an effective approach for evaluation of the operations for artificial enhancement of precipitation (Liu et al., 2021a), and investigating the underlying mechanisms.
The evolution of cloud is affected by multiple natural factors and artificial seeding complicates the picture. Therefore it is arduous to distinguish the alterations induced by seeding operations from the natural changes of the cloud system, which is a primary contributor to the uncertainty prevalent in physical and statistical evaluations of such interventions (National Research Council, 2003; Xue et al., 2016; Liu et al., 2021a). Mesoscale models were particularly highlighted in evaluating initiatives for artificial enhancement of precipitation (National Research Council, 2003; Xue et al., 2016). Observational studies comparing clouds with and without seeding in cases with similar development characteristics can provide useful information (Manton et al., 2011; French et al., 2018); however, they also have inherent uncertainties.
In the realm of cold cloud seeding, extensive simulations have been conducted to model the ice-forming processes induced by major catalysts such as silver iodide, dry ice, and liquid carbon dioxide/liquid nitrogen. These simulations have been integrated into various cloud and mesoscale models, supported by studies spanning several decades (Hsie et al., 1980; Kopp et al., 1983; Ćurić and Janc, 1990, 1993; Meyers et al., 1995; Ćurić et al., 2006; Guo et al., 2006; Chen and Xiao, 2010; Xue et al., 2013, 2016; Chen et al., 2016). Specifically, in relation to stable stratiform topographic clouds, Xue et al. (2013) highlighted that silver iodide deployed by aircraft predominantly forms ice crystals through condensation nucleation. Conversely, the primary nucleation seeding mode of ground-based silver iodide involves immersion freezing, followed by deposition, condensation freezing, and contact freezing nucleation, the latter being the least prevalent method in both aerial and ground-based deployments. Lou et al. (2014) further applied the same silver iodide nucleation module to convective cloud seeding. These simulations revealed that condensation freezing and immersion freezing were the dominant nucleation methods for silver iodide in this context. These results also indicate that silver iodide nucleation can exhibit variability in numerical simulations under different environmental backgrounds, despite similar experimental setups. Therefore, the complexity and sensitivity of cloud nucleation processes under various atmospheric conditions should be emphasized.
The classical theoretical foundation of technology for artificial enhancement of precipitation focuses on the process of introducing catalysts into clouds to create artificial ice crystals. These crystals then facilitate conversion of supercooled water into precipitation through a water–ice conversion mechanism. However, Hu et al. (1983) challenged the notion that the water source for artificial enhancement of precipitation is derived solely from this water–ice conversion process. They proposed that vapor–ice conversion also plays a major role, especially in stratiform clouds with low supercooled water content. This insight provides a critical theoretical basis for enhancing precipitation in such clouds. Following this theory, Hu (2001) further developed a new mechanism for artificial enhancement of precipitation in stratiform clouds, positing that artificial ice crystals could induce precipitation not only through conventional water–ice conversion but also via the vapor–ice process. Hu (2001) also highlighted that the latent heat released during the seeding processes of water vapor supplemental condensation and supercooled water freezing has substantial impact. This released heat increases the temperature of the cloud locally and intensifies airflow velocity, subsequently fostering the development of precipitation induced by the catalyst. These findings underscore the complexity of the processes involved in artificial enhancement of precipitation and the importance of considering various conversion mechanisms in different cloud types.
In cloud seeding simulations, a common approach is presenting the seeding process by artificially increasing the concentration or mass of ice crystals within a specific spatiotemporal range in the simulated cloud (Koenig and Murray, 1983; He et al., 2001; Sun et al., 2010). He et al. (1999) conducted a study using a cloud model to examine the impact of the initial ice core concentration on cold cloud convective precipitation. Their findings indicated that increase in the initial natural ice core concentration leads to rise in the ice and snow content of the cloud. However, this increment simultaneously limits the content of rain and hail within the cloud, resulting in reduction of local precipitation. It was also found that effect of the continuous reduction in rainfall was most pronounced following large doses of artificial ice crystals in the mature stage of cloud development. The mechanism is the increased number of artificial ice crystals colliding with supercooled raindrops, which elevates the concentration of graupel particles but reduces their average size. This reduction in size diminishes the velocity of the graupel, making it less likely to fall and melt since it is unable to overcome the upward movement of air within the cloud. Consequently, this leads to reduction in precipitation reaching the ground. Although large numbers of artificial ice crystals have shown potential for reducing rainfall in simulation experiments (Orville, 1996; Wang et al., 2001; Sun et al., 2010), research on snow enhancement seeding is notably scarce. This gap highlights the need for further research and analysis of snow enhancement seeding as a potential approach for future weather modification.
In this study, we intentionally set aside the physical process of development from catalyst particles to ice crystals, but concentrated on theoretical explorations of the operational conditions suitable for and the effects of seeding of stratiform clouds in the Bayanbulak test area. The main precipitation system in the Bayanbulak region in winter is cold air, and the main pathway of such a system is from the northwest. We employed a three-dimensional mesoscale cold cloud seeding model, based on the Weather Research and Forecasting (WRF) model, to numerically simulate an idealized seeding operation of a stratiform cloud typical of the snowfall process in the Bayanbulak region during 2021. The purpose of this simulation was to establish an effective operational index for this region. Furthermore, we evaluated the rainfall enhancement effects of the seeding operations to gain insight into their efficacy. Based on the findings, we proposed a more scientifically robust operation index system. This system could guide future seeding operations in stratiform clouds, particularly in regions similar to the Bayanbulak test area, thereby enhancing the precision and effectiveness of weather modification efforts.
2.
Introduction to model and simulation settings
This study employed a three-dimensional mesoscale cold cloud seeding model, as developed by Liu et al. (2016, 2021a, b), within the WRF framework. This model was integrated with the mixed-phase, two-parameter microphysics scheme of the Chinese Academy of Meteorological Sciences (CAMS), originally proposed by Hu and Yan (1986) and Hu and He (1987), and elaborated further by Lou et al. (2012). To this scheme, a cold cloud seeding module was appended. This addition enabled direct simulation of artificial ice crystal formation or the various nucleation processes induced by silver iodide catalysts. For an in-depth description of the specific microphysical processes involved, the reader is referred to Gao et al. (2011), Lou et al. (2012), and Liu et al. (2023).
In the context of cloud seeding, categorization of ice crystals based on their nucleation origins is crucial. Ice crystals that develop from the nucleation of natural ice nuclei are referred to as “natural ice crystals,” and those that form through the nucleation of silver iodide particles or other artificially introduced ice crystals are termed “artificial ice crystals.” During the seeding process, artificial and natural ice crystals coexist in the same environment, and they compete for the available water vapor and supercooled water. The distribution patterns and sizes of artificial and natural ice crystals differ, leading to distinct growth processes for each type. To accurately simulate their development within the cloud, artificial ice crystals are modeled as a separate hydrometeorological species, distinct from their natural counterparts.
In this research, the model was enhanced by the addition of two novel microphysical predictions specific to artificial ice crystals: the mixing ratio of artificial ice crystals (Qia) and their number concentration (Nia). These additions allowed a more nuanced and comprehensive simulation of the microphysical processes involving artificial ice crystals in the cloud seeding scenarios. In addition to the initial nucleation process, there are nine types of microphysical processes involved in artificial ice crystals. These comprise the collision of rain, snow, and graupel with artificial ice crystals (Car, Cas, and Cag, respectively), collision between artificial ice crystals (Caa), collision of artificial ice crystals with rain and natural ice crystals (Cra/Car and Cia), condensation and sublimation of artificial ice crystals (Sva) and automatic transformation into snow (Aas), and melting of artificial ice crystals into cloud droplets (Mac). The corresponding microphysical source–sink equations are expressed as follows:
ΔQiaΔt=Pva+Cia−Car−Cas−Cag−Aas−Mac+Sva,
(1)
ΔNiaΔt=NPva−NCaa−NCar−NCas−NCag−NAas−NMac+NSva,
(2)
where Pva is the mixture ratio of silver iodide nucleation process, and N is the microphysical conversion rate corresponding to the number concentration of these processes.
These processes are represented in the model through inclusion of the corresponding microphysical source–sink equations. These equations quantify the changes in the concentration and distribution of artificial ice crystals due to various microphysical interactions. By incorporating these complex interactions into the model, the study aimed to provide a more accurate and detailed simulation of the effects of cloud seeding, thereby enhancing understanding of artificial ice crystal dynamics in cloud microphysical processes.
In this study, a key assumption was made regarding the properties of artificial ice crystals in the context of cloud seeding, i.e., it was assumed that the distribution spectrum and the physical properties of the artificial ice crystals were identical to those of natural ice crystals as defined in the CAMS scheme. This assumption simplifies the modeling process and allows more straightforward comparison between the effects of natural and artificial ice processes. Under this assumption, the equations governing the eight microphysical processes for artificial ice crystals are aligned with those for natural ice crystals within the CAMS scheme. This alignment includes processes such as growth, sublimation, and interactions with other hydrometeor types. The only exception is the collision process involving natural ice crystals, which is inherently different owing to the artificial nature of the introduced ice crystals. The impact zone of seeding was characterized by several key parameters: the number concentration of artificial ice crystals across different atmospheric layers, the horizontal distribution of their maximum vertical concentration, and the horizontal spread of the artificial ice crystal concentration. This approach supports better understanding of both the potential and the limitations of cloud seeding as a weather modification technique.
For the numerical simulation, the model was configured with a high-resolution 1-km grid in the horizontal direction (Fig. 1) and 76 layers in the vertical direction, with the top of the model layer at 50 hPa. The simulation extended from 0600 UTC 11 to 1200 UTC 12 February 2021. The initial field used in the model was the Final Operational Global Analysis reanalysis dataset (FNL), and the model microphysics scheme used the CAMS scheme (Gao et al., 2011; Lou et al., 2012) with a 5-s time step (Table 1).
For the idealized seeding tests, we could change the parameters of the input file, flexibly set the time, region, height, type of catalyst (e.g., AgI or artificial ice crystals), and dose or seeding rate, such that a variety of different forms of seeding test could be performed. In this study, 11 simulation experiments were performed: one was a natural cloud simulation without seeding, and the other 10 comprised cloud seeding sensitivity test simulations, which were the same except for differences in the seeding position and the seeding dose (see Table 2 for seeding simulation settings). The elevation of most terrain in the Bayanbulak test area exceeds 2000 m, and it can substantially influence regional weather patterns and cloud formations, making it a pertinent factor in cloud seeding experiments. The simulation results enabled in-depth analysis of the diffusion and transport characteristics of artificial ice crystals within the cloud. Subsequently, by comparing the development and evolution of both seeded and natural clouds, we investigated the changes in the macrolevel and microlevel physical characteristics of clouds and precipitation resulting from seeding operations. This analysis, termed the physical response of seeding, sheds light on the underlying causes of these changes.
Table
2.
Summary of seeding sensitivity experiments. Bold text indicates the test with the best seeding effect
3.
Comparison of natural cloud simulation results with observations
During 11–12 February 2021, owing to the influence of cold air activities and the main pathway of weather systems from the northwest, light to moderate snowfall occurred in the Bayanbulak test area in Xinjiang. This type of weather pattern is very common in winter in the Bayanbulak region. Comparison of the 500-hPa field observed at 1200 UTC 11 February 2021 (Fig. 2a) and 0000 UTC 12 February 2021 (Fig. 2b) with the simulated situation (Figs. 3a, b) reveals that the simulation accurately captured the characteristics of the 500-hPa zonal circulation and the prevailing northwesterly winds, demonstrating the effectiveness of the model in replicating the dynamics of the weather system. At 1200 UTC 11 February, the atmosphere over the northern region of the test area had high relative humidity and abundant water vapor. However, by 0000 UTC 12 February, there was notable reduction in relative humidity in the north compared with that of the previous day, indicating less abundant water vapor.
Fig
2.
Synoptic situation (from NCEP) at 500 hPa: (a) observations at 1200 UTC 11 February 2021 and (b) observations at 0000 UTC 12 February 2021. Blue contours represent geopotential height (m), red contours indicate temperature (°C), and green shading denotes relative humidity (%).
Fig
3.
As in Fig. 2, but for (a) simulation at 1200 UTC 11 February 2021 and (b) simulation at 0000 UTC 12 February 2021.
The observational data of precipitation used in this study were based on the 1-km-resolution product (ART_1km, ground) of the CMPAS multisource fusion (hourly precipitation product for China). According to Fig. 4, the 24-h cumulative precipitation from 1200 UTC 11 to 1200 UTC 12 February was primarily concentrated in the northwest of the Bayanbulak test area. The maximum recorded precipitation, which fell in the form of snow, exceeded 20 mm. Additionally, light snowfall was observed in the northeast of the area. The simulated total precipitation closely mirrored the actual conditions. In the mountainous regions (42.5°–43.0°N), the simulated precipitation exceeded 20 mm, but this was not well reflected in the observational data, potentially owing to lack of sufficient observational data in mountainous areas. In terms of spatial distribution, the pattern of simulated precipitation aligned closely with that of the observed precipitation.
Fig
4.
Comparison of (a) observed (gray dots are the locations of surface station) and (b) simulated 24-h cumulative precipitation (colored shading; mm).
4.
Simulation results
During the formation of stratiform cloud precipitation, the water vapor supersaturation ratio over ice exceeds 0.04, which substantially influences the growth of ice crystal deposition, as highlighted by Meyers et al. (1995) and Shi et al. (2022). This high supersaturation of water vapor with respect to ice can serve as a valuable reference for selecting areas for artificial seeding operations, particularly in environments where supercooled water is scarce. The simulated horizontal distribution of vertically accumulated supersaturated water vapor over the ice surface, as depicted in Fig. 5, aligns closely with the distribution of vertically accumulated supercooled water shown in Fig. 6. In the Bayanbulak test area, the content of supersaturated water vapor over the ice surface displays a gradient of reduction from the north toward the south. The area of vertically accumulated supersaturated water vapor over the ice surface in the northwest of the region surpasses 0.3 mm, and most northern parts of the area present conditions suitable for ice nucleation and deposition growth.
Fig
5.
Horizontal distribution of vertically accumulated supersaturated water vapor over the ice surface from 1200 UTC 11 to 1200 UTC 12 February 2021 (natural cloud; colored shading; mm).
Fig
6.
Horizontal distribution characteristics of vertically accumulated supercooled water from 1200 UTC 11 to 1200 UTC 12 February 2021 (natural cloud; colored shading; mm. d01 region: 43.7°–44.2°N, 81.8°–83.0°E; d02 region: 43.7°–44.0°N, 83.0°–84.5°E).
Analyzing the horizontal distribution of vertically accumulated supercooled water in the Bayanbulak test area, as illustrated in Fig. 6, reveals distinct regional variations. In the northwest, the concentration of vertically accumulated supercooled water is markedly higher compared with that in other areas, with the maximum value reaching approximately 0.5 mm. In areas marked by high precipitation (d02: black box), the content of supercooled water is relatively low. Conversely, in regions with a higher concentration of supercooled water (d01: red box), the precipitation is weaker. This pattern suggests potential influence of topography on the conversion of supercooled water to precipitation. In areas with lower terrain, supercooled water is not efficiently transformed into precipitation. However, as terrain elevation increases, the conversion process becomes more effective. Based on these different characteristics in the northwest region of the Bayanbulak test area, two distinct experimental zones (i.e., d01 and d02) were selected for comparative analysis.
In the analysis of supercooled water (Qc) within the Bayanbulak test area, average values were interpolated across different height layers in experimental zones d01 and d02 (Fig. 7), aligned with corresponding altitudinal layers. In domain d01, the peak concentration of supercooled water is observed at altitudes of 2.0–4.0 km ASL, corresponding to temperatures in the range of −3 to −15°C. Here, the maximum supercooled water content exceeds 0.05 g m−3. Comparatively, in domain d02, the highest concentration of supercooled water is found predominantly at altitudes of 2.5–4.0 km ASL, within a temperature range of −5 to −15°C, with a maximum value of approximately 0.03 g m−3. These findings have practical implications for cloud seeding operations. In terms of airborne and ground-based operating conditions, the 2.0–4.0-km altitude range (temperature: −3 to −15°C) in the d01 area presents conditions that are more favorable. In contrast, the d02 area exhibits complete conversion of supercooled water into precipitation, resulting in notably lower concentrations compared with those of the d01 area. During the development and maintenance phases of precipitation, the supercooled water content remains high in the d01 area, indicating sustained potential for precipitation enhancement through artificial seeding.
Fig
7.
Comparison of the vertical distribution characteristics of supercooled water (colored shading; g m−3) in the experimental area: (a) d01 and (b) d02.
In this study, we focused on conditions pertaining to stratiform cloud snowfall and we selected areas for seeding in the northwest of the study region, which had high concentration of supercooled water and a high ice–supersaturated water vapor ratio. This approach aligns with the favorable conditions for seeding identified in previous studies (Hu, 2001; Geresdi et al., 2020), which advocated conducting seeding operations in regions with substantial supercooled water but deficient ice crystal content. Here, artificial ice crystals were introduced to simulate seeding. The study incorporated 10 unique experiments, as detailed in Table 2; however, additional seeding trials involving various doses of artificial ice crystals, which were conducted post-1300 UTC 11 February 2021, are not listed. The snow enhancement results obtained from these subsequent trials proved less efficacious compared with those executed at 1200 UTC on the same day. Intriguingly, certain trials conducted later resulted in reduced snowfall, underscoring the complexity of the seeding process and its variable impact on weather modification.
Our numerical experiments investigating various seeding doses at different seeding time revealed that the early stages of weather system development represent the most effective window for spreading. During this phase, certain cloud areas contain abundant supercooled cloud water, but the conversion of this water into precipitation particles is limited by scarcity of ice phase particles. As time progresses, both the cloud and the precipitation systems tend to weaken, resulting in gradual reduction in supercooled cloud water content across the cloud area, which consequently diminishes the efficacy of the seeding process.
Based on the negative correlation between the supercooled water content and precipitation, two seeding areas (d03 and d04) were selected from regions d01 and d02 for comparative analysis of the results of the snow enhancement experiments, as shown in Fig. 8. Figure 9 reveals that the d04 area did not exhibit a notable snow enhancement effect, regardless of the catalyst dosage applied. In certain instances, application of seeding in d04 area led to reduction in snowfall, contrary to the intended outcome of the experiments. Comparative analysis indicates that the d04 region exhibited the most effective seeding reaction near the cloud top, which resulted in an increase in snowfall in the affected area, quantified as approximately 2.80 mm post-seeding. However, despite this localized efficacy near the cloud top, the overall impact of the snow enhancement seeding tests in the d04 area was suboptimal.
Fig
8.
The cloud band (the vertical accumulation of total hydrometeors; colored shading; mm) in the experimental seeding area at 1200 UTC 11 February 2021 (d03: 43.7°–43.9°N, 82.4°–82.6°E; d04: 43.7°–44.0°N, 83.4°–83.7°E; point A: 43.8°N, 82.5°E; point B: 43.5°N, 84.5°E). The red arrow is the vertical section from point A to point B in Figs. 11–15.
Fig
9.
Change in hourly increase in snowfall (mm) over time in each of the 10 test seeding cases.
In the d03 seeding area, the effect of spreading artificial ice crystals at various altitudes, where the temperature was < 0°C, was superior to that in the d04 area. Specifically, in the d03 area, for the same catalyst quantity, seeding was most effective when it occurred near the cloud top. Within the altitude range of 3500–4000 m, corresponding to temperatures between −11 and −15°C, more than 30% of the cloud conditions exhibited updrafts with velocity of < 0.28 m s−1, while the downdraft velocity was approximately −0.31 m s−1. The average supercooled cloud water content was recorded at 0.026 g kg−1, with near absence of ice crystals. A higher dosage of artificial ice crystals, especially at the order of 1.0 × 107, resulted in the maximum amount of snowfall. For the stratiform cloud snowfall process, the S6 scheme demonstrated the highest effectiveness of seeding. Analysis of the S6 scheme, as depicted in Fig. 10, reveals that 3 h post-seeding, the affected area extended downstream of the operation zone by over 100 km (1.5° longitude). The accumulated precipitation within and around the operation area up to 100 km downstream was primarily in the form of an increase in snowfall. The total increase in snowfall across the entire operation area affected by the optimal seeding scheme (i.e., S6) was quantified at 150.29 mm.
Fig
10.
Variation in accumulative surface snowfall (seeding cloud test minus natural cloud test; color shading; mm) after seeding for the S6 scheme during 2000–2300 UTC. The black box indicates the seeding location.
The main reason for the area of precipitation reduction (shown by the green color in Fig. 10) at the edge of the area of precipitation enhancement is the dynamic effect caused by seeding (Liu et al., 2021b). In the supercooled layer of the cloud region, there will be compensatory downdrafts at the edge of the area with enhanced updraft caused by seeding. In these peripheral regions, the artificial ice crystal concentration is very low and has little influence on cloud development. The growth of ice particles is limited mainly by weakening of the updraft, which results in reduction of snow particles and ultimately leads to reduced snowfall on the ground.
In each of the 10 seeding tests, the optimal seeding altitude was determined as approximately 3.5–4.0 km, i.e., near the cloud top. This indicates that seeding should be concentrated in areas characterized by relative abundance of supercooled cloud water content and high supersaturation of water vapor over the ice surface, where ice particles are absent, particularly near the cloud top. Analysis of the cloud conditions revealed that optimal snow enhancement could be achieved by preferentially conducting seeding operations in the northwest of the Bayanbulak test area. This recommendation is based on the observation that those areas showing notable increase in snowfall correspond to regions with plentiful supercooled cloud water content and lower concentrations of ice and snow crystals. Thus, the spatial distribution of increased snowfall is linked closely to the specific cloud water content and ice crystal characteristics of the seeding location.
The results obtained 15 min post-seeding indicate that the composition of the catalyzed clouds—including water vapor, supercooled cloud water, natural ice crystals, and graupel—was largely analogous to that of natural clouds, as evidenced in Figs. 11 and 12. Notably, in the vicinity of the site of artificial ice crystal diffusion, there was marked increase in the updraft area. This phenomenon is primarily attributed to the disturbance in the dynamic field of the supercooled layer, triggered by latent heat released during the seeding process. Consequently, the vertical wind transitioned from a downdraft to an updraft, and existing updrafts were intensified. Furthermore, the concentration of snow crystals near the seeding site was substantially elevated compared with that of natural clouds, reaching a value of up to 0.1 g kg−1. This increase is largely attributable to dissemination of a substantial quantity of artificial ice crystals, which facilitated the automatic conversion or collision-induced formation of additional snow crystal particles. Examining the broader impact of artificial ice crystal seeding over the entire layer of the seeding operation, it is evident that the seeding affected an area of < 30 km within 15 min. The evident response to seeding, characterized by enhanced development of precipitation particles within the clouds initiated a slight increase in surface snowfall, demonstrating the localized efficacy of the seeding process in altering weather patterns.
Fig
11.
Vertical profiles of (a) water vapor (g kg−1), (b) cloud water (g kg−1), (c) ice crystals (g kg−1), (d) snow crystals (g kg−1), (e) graupel (g kg−1), and (f) vertical velocity (m s−1) from point A to point B in a natural cloud at 1215 UTC.
Fig
12.
Vertical profiles of (a) water vapor (g kg−1), (b) cloud water (g kg−1), (c) ice crystals (g kg−1), (d) snow crystals (g kg−1), (e) graupel (g kg−1), (f) vertical velocity (m s−1), (g) the entire influence region of artificial ice crystals (m−3), and (h) the affected region of artificial ice crystals (kg−1), at the current moment in the seeding cloud from point A to point B at 1215 UTC. Panels (g) and (h) are characterized by particle concentration distribution.
According to the results obtained 1 h post-seeding, as depicted in Figs. 13 and 14, the seeding of artificial ice crystals influenced an area over a range of up to 70 km, with the primary impact zone lying between 20 and 70 km. Within this area, notable enhancement in snowfall occurred. In comparison with naturally occurring supercooled cloud water, discernible reduction in cloud water occurred at the altitude corresponding to the seeding position. This resulted in notable disruption in the supercooled cloud water, reducing its concentration to zero in the affected area post-seeding. Conversely, increase in water vapor occurred near ground level, attributable to evaporation of descending snowflakes.
Near the seeding site, the simulation of natural clouds shows fewer ice crystals. The introduction of a substantial quantity of artificial ice crystals during the experiment produced a unique environment within the cloud layer, characterized by temperatures ranging from −11 to −15°C and the coexistence of supercooled water, ice crystals, and water vapor. Under these conditions, the Bergeron process facilitates rapid growth of ice crystals by consuming supercooled cloud water, which eventually results in the formation of snow crystals. This is further accelerated by collisions between ice crystals, which results in marked increase in snow crystal particles near the seeding location, with a peak concentration of 0.3 g kg−1, indicating a substantial snow enhancement effect.
The addition of artificial ice crystals appears also to suppress the riming process, leading to reduction in seeding graupel particles. Slight reduction in ice crystal numbers is also observed downstream from the seeding site. Changes in the vertical wind speed near the seeding location, transitioning from downdrafts to updrafts, are conducive to growth of additional ice crystal particles through condensation. The observed increase in near-surface subsidence within the area affected by seeding is primarily attributable to the descent of snowflakes.
This study simulated the trajectory of influence of artificial ice crystals, and revealed the evolution process from seeded artificial ice crystals to the formation of snow particles and occurrence of snowfall on the ground, which was similar to that observed (French et al., 2018). In the late stages of seeding, despite the depletion of supercooled cloud water, the ice and snow crystal particles continued to grow through aggregation. Over time, these enlarged snowflake particles descended to the ground, as evidenced by radar reflectivity and illustrated in Fig. 15. Post-seeding, the seeding impact extended across the entire operational cloud region. The simulated seeding effect endured for over 60 min in the cloud, indicating that the artificial ice crystals maintained their influence downstream of the initial seeding event. These findings underscore that seeding stratiform clouds with supercooled water using artificial ice crystals could augment natural precipitation processes within seeded clouds, thereby inducing rainfall in areas where it might not have occurred naturally.
Fig
15.
Vertical cross sections of radar reflectivity (dBZ) from point A to point B, as shown in Fig. 8: (a) natural cloud at 1215 UTC, (b) seeded cloud at 1215 UTC, (c) natural cloud at 1230 UTC, (d) seeded cloud at 1230 UTC, (e) natural cloud at 1300 UTC, and (f) seeded cloud at 1300 UTC.
5.
Summary
Utilizing a three-dimensional mesoscale cold cloud seeding model based on the WRF framework, this study simulated the snowfall process in stratiform clouds over the Bayanbulak test area (China) on 11 February 2021. The research focused on examining the effects of seeding on both the macroscopic and the microscopic attributes of cloud formation and precipitation. It is acknowledged that owing to various objective factors, the numerical simulations might not have perfectly replicated the actual conditions of clouds and precipitation. However, despite any spatiotemporal discrepancies, comparative analysis between the simulations and the observational data indicated that the model could successfully captures the general state of the atmosphere and the primary characteristics of the precipitation field during this weather event. The derived results, relevant specifically to the case studied, do enhance our general understanding of cloud seeding processes.
(1) Numerical simulation experiments, conducted at various altitudes and with varying seeding doses in the Bayanbulak test area, indicate that optimal cloud seeding locations are characterized by a vertically accumulated supercooled water content of > 0.5 mm in the northwest of the region, and vertically accumulated supersaturated water vapor over the ice surface of > 0.3 mm, with updrafts prevailing in the operation area. The ideal seeding altitude was identified at 3.5–4.0 km (temperature: −11 to −15°C), near the cloud top, where the supercooled cloud water content was 0.0026 g kg−1. The prime window for seeding occurs in the early stages of system development. Under consistent conditions, greater catalyst quantities enhance effectiveness, with an optimal seeding dose of artificial ice crystals determined as 1.0 × 107 kg−1. This approach resulted in a precipitation increase across the entire operational area of 150.29 mm.
(2) Introduction of artificial ice crystals in regions with high content of supercooled water and low abundance of native ice crystals has been observed to augment surface snowfall. Approximately 15 min post-seeding, the cold cloud hydrometeors began transforming, noticeably increasing the number concentration of snowflake particles in the catalyzed clouds compared with that in natural clouds. The area affected by artificial ice crystal 1 h post-seeding spanned 15–70 km, and the supercooled cloud water content in the catalyzed area was substantially lower than that in natural clouds. Ice crystals consume a large amount of supercooled water through the Bergeron process, forming more snow, which in turn consumes more supercooled water via collision–coalescence with cloud water droplets. The seeding process also impedes the riming process, resulting in reduction in the number of graupel particles.
(3) Influenced by the dynamic conditions within the operational cloud area, the horizontal dispersion of artificial ice crystals can extend beyond tens of kilometers post-seeding. Vertically, most artificial ice crystal particles remain concentrated within the region approximately 1 km below the operational layer (height: 3.5–4.0 km), although a small number can descend to 2.0 km. The limited velocity of vertical ascent in stratiform clouds hinders upward transport of artificial ice crystals. Increased snowfall was observed primarily in the operational area and regions downstream 3 h post-seeding, extending over 100 km; reduced snowfall was found at areas beyond 100 km downstream.
(4) The entire cloud region was impacted by the seeding operation. Simulations indicated that the effects of seeding persisted for over 60 min in the cloud, suggesting continued influence of the ice crystals downwind from the initial seeding point. The seeded clouds exhibited increased snowfall, with total hourly snowfall demonstrating a unimodal temporal variation, peaking 2–3 h post-seeding. This increase is attributed to the widespread distribution of artificial ice crystals within the clouds, which accelerated the growth of snow particles. The growth of ice and snow crystals, which consumes a large amount of supercooled cloud water, led to a gradual slowdown of the growth rate of snow particles as the amount of available supercooled cloud water diminished.
The seeding conditions considered in this study have certain limitations: they are applicable only to the process of light–moderate snow in the Bayanbulak region in winter under the influence of cold air. Moreover, performing effective artificial snow enhancement is limited by sufficient supercooled cloud water content and lack of ice crystal particles.
Acknowledgments
We thank the reviewers for their helpful comments and suggestions, which have effectively improved the quality and depth of this paper.
Fig.
9.
Change in hourly increase in snowfall (mm) over time in each of the 10 test seeding cases.
Fig.
2.
Synoptic situation (from NCEP) at 500 hPa: (a) observations at 1200 UTC 11 February 2021 and (b) observations at 0000 UTC 12 February 2021. Blue contours represent geopotential height (m), red contours indicate temperature (°C), and green shading denotes relative humidity (%).
Fig.
4.
Comparison of (a) observed (gray dots are the locations of surface station) and (b) simulated 24-h cumulative precipitation (colored shading; mm).
Fig.
5.
Horizontal distribution of vertically accumulated supersaturated water vapor over the ice surface from 1200 UTC 11 to 1200 UTC 12 February 2021 (natural cloud; colored shading; mm).
Fig.
6.
Horizontal distribution characteristics of vertically accumulated supercooled water from 1200 UTC 11 to 1200 UTC 12 February 2021 (natural cloud; colored shading; mm. d01 region: 43.7°–44.2°N, 81.8°–83.0°E; d02 region: 43.7°–44.0°N, 83.0°–84.5°E).
Fig.
7.
Comparison of the vertical distribution characteristics of supercooled water (colored shading; g m−3) in the experimental area: (a) d01 and (b) d02.
Fig.
8.
The cloud band (the vertical accumulation of total hydrometeors; colored shading; mm) in the experimental seeding area at 1200 UTC 11 February 2021 (d03: 43.7°–43.9°N, 82.4°–82.6°E; d04: 43.7°–44.0°N, 83.4°–83.7°E; point A: 43.8°N, 82.5°E; point B: 43.5°N, 84.5°E). The red arrow is the vertical section from point A to point B in Figs. 11–15.
Fig.
10.
Variation in accumulative surface snowfall (seeding cloud test minus natural cloud test; color shading; mm) after seeding for the S6 scheme during 2000–2300 UTC. The black box indicates the seeding location.
Fig.
11.
Vertical profiles of (a) water vapor (g kg−1), (b) cloud water (g kg−1), (c) ice crystals (g kg−1), (d) snow crystals (g kg−1), (e) graupel (g kg−1), and (f) vertical velocity (m s−1) from point A to point B in a natural cloud at 1215 UTC.
Fig.
12.
Vertical profiles of (a) water vapor (g kg−1), (b) cloud water (g kg−1), (c) ice crystals (g kg−1), (d) snow crystals (g kg−1), (e) graupel (g kg−1), (f) vertical velocity (m s−1), (g) the entire influence region of artificial ice crystals (m−3), and (h) the affected region of artificial ice crystals (kg−1), at the current moment in the seeding cloud from point A to point B at 1215 UTC. Panels (g) and (h) are characterized by particle concentration distribution.
Fig.
15.
Vertical cross sections of radar reflectivity (dBZ) from point A to point B, as shown in Fig. 8: (a) natural cloud at 1215 UTC, (b) seeded cloud at 1215 UTC, (c) natural cloud at 1230 UTC, (d) seeded cloud at 1230 UTC, (e) natural cloud at 1300 UTC, and (f) seeded cloud at 1300 UTC.
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Wei, L., Y. Q. Shi, W. G. Liu, et al., 2024: Snow-enhancement conditions and seeding simulation of stratiform clouds in the Bayanbulak test area in China. J. Meteor. Res., 38(6), 1078–1092, doi: 10.1007/s13351-024-4003-3.
Wei, L., Y. Q. Shi, W. G. Liu, et al., 2024: Snow-enhancement conditions and seeding simulation of stratiform clouds in the Bayanbulak test area in China. J. Meteor. Res., 38(6), 1078–1092, doi: 10.1007/s13351-024-4003-3.
Wei, L., Y. Q. Shi, W. G. Liu, et al., 2024: Snow-enhancement conditions and seeding simulation of stratiform clouds in the Bayanbulak test area in China. J. Meteor. Res., 38(6), 1078–1092, doi: 10.1007/s13351-024-4003-3.
Citation:
Wei, L., Y. Q. Shi, W. G. Liu, et al., 2024: Snow-enhancement conditions and seeding simulation of stratiform clouds in the Bayanbulak test area in China. J. Meteor. Res., 38(6), 1078–1092, doi: 10.1007/s13351-024-4003-3.
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Manuscript History
Received: 07 January 2024
Revised: 07 July 2024
Accepted: 10 August 2024
Available online: 12 August 2024
Final form: 22 August 2024
Typeset Proofs: 19 September 2024
Issue in Progress: 18 November 2024
Published online: 19 December 2024
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Abstract
摘要
1.
Introduction
2.
Introduction to model and simulation settings
3.
Comparison of natural cloud simulation results with observations
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