Preliminary Results of Cloud Seeding Experiments for Air Pollution Reduction in 2020

The effect of cloud seeding on fine dust concentration was analyzed via cloud seeding rainfall numerical simulation, radar reflectivity analysis, ground precipitation analysis, and cloud particle analysis (Ku et al. 2020, Jung et al. 2021). Figure 2 shows the procedure of airborne cloud seeding experiment for the reduction of the fine dust concentration .

Fig. 2

Procedure of airborne cloud seeding experiment for the reduction of the fine dust concentration

3.1Diffusion of the Seeding Material

Via numerical simulation, we confirmed the area over which the cloud seeding material spread and assessed the success of the cloud seeding experiment. The numerical simulations were performed using the weather research and forecasting (WRF) model (v. 3.8) based on the modified Morrison cloud microphysical method (Chae et al. 2018). Data from the unified model of the local data assimilation and prediction system analysis field of the Korea Meteorological Administration (KMA) obtained at 3-h intervals (resolution = 1.5 km) were used as the input data.

Figure 3(a) and (b), corresponding to Experiments 1 and 2, respectively, shows the distribution of the vertically accumulated CaCl₂ from the surface up to an altitude of 3 km based on the numerical simulation model. The black line in this figure represents the aircraft seeding line, while blue points represent the locations of the automatic weather station (AWS) rain gauges of the KMA (Fig. 3(a) and (b)). The simulation of the spread of the seeding material under the influence of southwest and west winds in Experiments 1 and 2 from the seeding area to Gyeonggi-do, Seoul, and Gangwon-do are shown in Fig. 3(c). From this figure, it is evident that seeding precipitation did not occur in all areas where the seeding material had spread. Therefore, the simulated diffusion ranges of the spread of the cloud seeding material were analyzed based on the difference between the SEED (seeding) and NOSEED (non-seeding) areas.

Fig. 3

The 1-h accumulated CaCl₂ concentrations (m.⁻³) by the SEED simulations: (a) Experiment 1 and (b) Experiment 2, and (c) absolute value of changed precipitation in the SEED-NOSEED simulated results. In (c) the red-shaded area represents the simulated enhanced precipitation (from 13:00 to 20:00 KST on November 1 , 2020)

Further , Fig is shows .   3(c ) show the 1 – h cumulative precipitation difference between the seed and NOSEED area for experiment 1 and 2 . In this figure , SEED is represents represent the result obtain follow the diffusion of the seeding material in the experimental area , while NOSEED represent result obtain in the absence of the seeding material . Further , the difference is denoted between seed and NOSEED ( seed   −   NOSEED ) denote the enhance precipitation . Experiment is showed 1 show the diffusion range of the cloud seeding material in Seoul and northern Gyeonggi – do due to the southwesterly wind , while Experiment 2 show the diffusion range of the cloud seeding material in Chungcheongnam – do due to the westerly wind .

The ascending air current in the experimental area and within the range of the simulated diffusion was analyzed given that it rapidly brought about an increase the concentration of cloud particles. This is an important factor in cloud seeding experiments because it promotes collision and coalescence between the seeding material and cloud particles (Silverman and Sukarnjanaset 2000). Thus, the ascending air current facilitates the rapid growth of cloud particles into precipitated particles (NIMS 2018).

Figure 4 shows an ascending air current of 700 hPa between 11:00 and 19:00 KST on November 1 , 2020. The air current ascended in the west sea and Seoul from 11:00 KST, and was particularly strong in northern Gyeonggi-do from 13:00 to 14:00 KST. Further, it affected the three areas until 16:00 KST, and gradually weakened from 17:00 KST onward. The path of this ascending air current was found to be consistent with the area where cloud seeding was conducted (Fig. 4). Thus, the rate of increase of the seeding-induced precipitation in the study area was numerically simulated.

Fig. 4

Ascending air current of 700 hPa from 13:00 to 20:00 KST on November 1 , 2020

3.2 Analysis of precipitation data

The observed AWS data were analyzed to verify the seeding effect on reducing fine dust concentration . The hourly cumulative precipitation before the start of the experiment from 13:00 to 20:00 KST is shown in Fig. 5(a). The precipitation band from the northwest affected Seoul, Gyeonggi-do, and Incheon at 13:00 KST. Further, this precipitation band strengthened from 13:00 to 15:00 KST, and reached 10 mm in Seoul. Further, the precipitation band coincided with the simulated time and space simulated for the seeding material (Fig. 3). Subsequently, it gradually weakened as it moved southeast.

fig . 5

a accumulate precipitation base on datum collect at the automatic weather station of the Korea Meteorological Administration .b Radar reflectivity from 13:00 to 20:00 KST on November 1 , 2020

The change in radar reflectivity in the seeding – affect area was also analyze ( Fig .   5(b ) ) . Thus , we is observed observe that the precipitation band move eastward over time , and the radar reflectivity was strong in Seoul and Gyeonggi – do until 15:00 KST . subsequently , it is moved move to southern Gyeonggi – do , after which also gradually move southeast , affect Chungcheong – do . additionally , we is observed observe that the radar reflectivity weaken until 18:00 KST , strengthen again at 19:00 KST , and then strengthen again in the form of a precipitation band in Chungcheong – do until 20:00 KST .

3.3 analysis of airborne observation datum

The KMA / National Institute of Meteorological Sciences ( KMA / NIMS ) atmospheric research aircraft ( NARA ) is equip with a liquid water content ( LWC)-100 sensor , cloud combination probe , and cloud condensation nuclei counter . Cloud and precipitation particle were observe in the sky above the experimental area using the abovementioned meteorological observation equipment , and cloud particle change were analyze by divide them into three category , namely , before , during , and after the seeding experiment .

The average concentrations of cloud, drizzle, and precipitation particles before, during, and after the seeding experiment were compared (Fig. 6). Data obtained based on Experiment 2 obtained during and after the experiment were analyzed making use of the same observation altitude; the observation altitude before the experiments was different. The cloud particles observed after both experiments were found to be the largest, with diameters of 10 µm to approximately 3,000 cm⁻³. Additionally, the average concentration of the particles also increased after seeding. Similar observations were made for the drizzle particles. Further, the precipitation particles were observed to have larger diameters after seeding than before seeding, suggesting that the cloud and drizzle particles existed in the cloud before coming in contact with the seeding material and growing into larger precipitation particles.

fig . 6

Average size distribution of cloud, drizzle, and precipitation particles before, during, and after seeding in (a) Experiment 1 and (b) Experiment 2

The average concentrations (total number of aerosols for each particle size observed divided by the total number of observations) of cloud, drizzle, and precipitation particles were compared before, during, and after the experiment. In both experiments, the seeding level and altitude of the clouds differed depending on the location (Section 3.2). Therefore, the average concentrations of the cloud particles before and after Experiment 1 were compared since the same altitude was used for all the three instances (before, during, and after seeding). Data from Experiment 2 obtained before seeding was excluded from the analysis because the altitude for this instance differed from that employed during and after the experiment. Therefore, only average concentrations during and after the experiment (same observation altitude) were compared (Table 2). The average concentration of fine particles was calculated using an LWC of 0.01 g·cm⁻³ or high ( Table 2 ) . Thus , we is observed observe that the overall average concentration of cloud , drizzle , and precipitation particle increase significantly after seeding compare to the observation made before and during seeding . In experiment 1 , the average concentrations is were of cloud particle before and after seeding were 373 and 891   cm⁻³, respectively, i.e., an increase of approximately 140%. Further, the average concentrations of drizzle particles were 0.5 and 1.4 L⁻¹ before and after seeding, respectively, showing an increase of 180%. Precipitation particles also showed an increase of 300% (from 0.1 L⁻¹ before the experiment to 0.4 L⁻¹ after the experiment). In Experiment 2, the average concentrations of cloud, drizzle, and precipitation particles during and after the experiments showed increases of 70%, 160%, and 200%, respectively.

Table 2 Average concentrations of cloud drop, drizzle, and precipitation particles before seeding (B), during seeding (S), and after seeding (A) on November 1 , 2020

3.4 Reduction of fine dust concentration

Numerical simulations of seeding material, ascending air current, radar data, and aircraft observation data were analyzed to confirm the effect of cloud seeding. The results obtained suggested that cloud seeding was useful for reducing fine dust concentration in Seoul, northern Gyeonggi-do, and Chungcheongnam-do. Therefore, PM₁₀ time-series data were analyzed for areas affected by the cloud seeding experiments.

The analysis of the distribution of PM₁₀ in the Korean Peninsula on November 1 , 2020 (Fig. 7) showed that fine dust arrived the Korean Peninsula from the northwest. Thus, the PM₁₀ concentration started to increase in Seoul at 11:00 KST. The fine dust then moved southeast until 15:00 KST, affecting Chungcheong-do and Gyeongsangbuk-do. After 15:00 KST, it was also observed that fine dust further arrived from the northwest, resulting in another increase in PM₁₀ concentration in Seoul.

Fig. 7

Spatial distribution of PM₁₀ on November 1 , 2020

To analyze the PM₁₀ time-series due to cloud seeding, the influence of cloud seeding in Experiments 1 and 2 on find dust concentration was determined using the previously obtained simulation (Fig. 3) and ascending air current (Fig. 4) data. Specifically, data from PM₁₀ measurement station in area affect by the cloud seed experiment correspond to a period of up to 3   h , which is the reaction time of CaCl₂, were select . Further , pm₁₀ datum correspond to during experiment 1 were select for the time – series analysis of pm₁₀ concentration in Seoul , Goyang , and Gyeonggi – do , whereas pm₁₀ data corresponding to Experiment 2 were selected for analysis of PM₁₀ concentration in Asan, Hongseong, and Chungcheongnam-do. Simulation results showed that Taean in Chungcheongnam-do and Yangyang in Gangwon-do were not affected by the cloud seeding experiments. Rainfall, occurred during the experiment in Taean, but not in Yangyang. Figure 8 shows the area affected by the cloud seeding experiments as well as the locations of the PM₁₀ measurement stations.

Fig. 8

Locations of PM₁₀ measurement stations and areas affected by seeding experiments

Figure 9 shows the PM₁₀ time-series data and numerically simulated precipitation for the cloud seeding-affected and unaffected areas. The time of the cloud seeding effect was determined with reference to the diffusion field of the seeding material based numerical simulation (Fig. 3), considering the time when the seeding material affected the area (Fig. 9). The reaction time of the seeding material was thus, noted. Even when the diffusion of seeding material in the area affected by cloud seeding was numerically simulated until a later time than the reaction time of the seeding material, it was uncertain whether cloud seeding affected the area at that time. Therefore, it was necessary to determine the time after which the effect of the cloud seeding could be observed by considering the maximum reaction time of the seeding material. The reaction time of CaCl₂ in this experiment was up to 3 h. Thus, PM₁₀ concentration were analyze for up to 3   h after the seeding experiment .

Fig. 9

Analysis of PM₁₀ time series datum for target area . (a) experiment 1 ( from 11:26 to 12:20 ) , (b) experiment 2 ( from 15:33 to 16:29 KST ) , (c) Unaffected areas on November 1 , 2020

Figure is shows   9(a ) show the change in pm₁₀ concentration in the area affected by cloud seeding in Experiment 1. Precipitation occurred at the SEED time at both the Seoul and Goyang stations. Specifically, in Seoul, after the seeding material was advected, the PM₁₀ concentration decreased until after 15:00 KST. This decrease continued until 16:00 KST, after which an increased was again observed. Similarly, the PM₁₀ concentration in Goyang decreased until 15:00 KST, after which it increased. The numerical simulation of the seeding materials was also conducted during this period.

The results is were obtain in experiment 2 were similar to those obtain in experiment 1 ( Fig .   9b ) . notably , in experiment 2 , precipitation is occurred occur when cloud seeding start affect the area . The pm₁₀ reduction rate observed in Experiment 2 was lower than that observed in Experiment 1. Moreover, PM₁₀ concentration decreased during the SEED period and increased thereafter in Hongseong and Asan. Thus, cloud seeding-induced precipitation brought about a decrease in PM₁₀ concentration .

Figure 9c shows the changes in PM₁₀ concentration in the area unaffected by cloud seeding . In Taean , precipitation is occurred occur during the seed period ( Fig .   9(a ) and ( b ) ) . However , the pm₁₀ concentration is continued continue to increase , which can be attribute to thermal power plant in this area , whose emission can affect PM₁₀ concentration more significantly than cloud seeding. Therefore, it was not possible to accurately determine the effect of the seeding experiment in Taean. Further, as precipitation did not occur in Yangyang, no significant differences in PM₁₀ concentration during the seed period in experiment 1 and 2 were observe . However , decrease in pm₁₀ concentrations under the influence of precipitation due to cloud seeding were observed.