Raindrop size distribution (DSD) is an important parameter in rainfall research and can be used for quantitative precipitationestimation (QPE) in meteorology and hydrology. DSD also improves the understanding of the uncertainty of cloudmicrophysical processes (CMPs) such as ice-based and warm rain growth during climate change. Changes in CMPs impactthe generation of precipitation. However, the estimation of CMPs based on in situ observation is difficult because of thecomplexity of microphysics processes, and most previous studies on the CMP involved approximations to predict the typesof microphysical processes affecting precipitation generation based on in situ observations performed in real-time. Therefore,we developed a simple method for understanding the CMPs of precipitation generation using a conceptual model of CMPsand in situ observation DSD data. We employed previously observed DSD parameters and a CMP conceptual model of theDSD observation-based microphysical process. As case studies, we applied DSD observation data obtained in Korea andEast Asia to estimate the CMPs. For example, the major CMP of megacities was vapor deposition in Beijing (< 1 mm h−1)and Seoul (< 5 mm h−1), as the strong updraft of the urban heat island effect in megacities results in increased liquid watercontent, leading to the formation of large number of supersaturated clouds at higher altitudes.

Recently, interest in the possibility of a washout effect using artificial rain enhancementtechnology to reduce high-concentration fine dust is growing. Therefore, in this study, thereduction rate of PM10 concentration according to the amount of artificial rain enhancement wascalculated during Asian Dust event which occurred over the Korean Peninsula on March 29,2021 using air quality model [i.e., Community Multiscale Air Quality (CMAQ)] combined withthe mesoscale model for artificial rain enhancement (i.e., WRF-MMS). According to WRFMMS,the washout effect lasted 5 hours, and the maximum precipitation rate was calculatedto be 1.5 mm hr–1. According the CMAQ results, the PM10 reduction rate was up to 22%, andthe affected area was calculated to be 6.4 times greater than that of the artificial rain enhancementarea. Even if the maximum amount of precipitation per hour is lowered to 0.8 mm hr–1(about 50% level), the PM10 reduction rate appears to be up to 16%. In other words, it is believedthat this technique can be used as a direct method for reducing high-concentration finedust even when the artificial rain enhancement effect is weak.

Quality control methods for the first G-band vapor radiometer (GVR) mounted on a weather aircraft in Korea were developed using the GVR Precipitable Water Vapor (PWV). The aircraft attitude information (degree of pitch and roll) was applied to quality control to select the shortest vertical path of the GVR beam. In addition, quality control was applied to remove a GVR PWV ≥20 mm. It was found that the difference between the warm load average power and sky load average power converged to near 0 when the GVR PWV increased to 20 mm or higher. This could be due to the high brightness temperature of the substratus and mesoclouds, which was confirmed by the Communication, Ocean and Meteorological Satellite (COMS) data (cloud type, cloud top height, and cloud amount), cloud combination probe (CCP), and precipitation imaging probe (PIP). The GVR PWV before and after the application of quality control on a cloudy day was quantitatively compared with that of a local data assimilation and prediction system (LDAPS). The Root Mean Square Difference (RMSD) decreased from 2.9 to 1.8 mm and the RMSD with Korea Local Analysis and Precipitation System (KLAPS) decreased from 5.4 to 4.3 mm, showing improved accuracy. In addition, the quality control effectiveness of GVR PWV suggested in this study was verified through comparison with the COMS PWV by using the GVR PWV applied with quality control and the dropsonde PWV.
국내에서 처음으로 도입한 기상 항공기에 탑재한 G-band 수증기 라디오미터(GVR) 관측으로 산출된 가강수량의품질 관리 방법을 제안하였다. GVR 빔의 연직 최단 경로 자료만 사용하기 위해 기상 항공기의 자세 정보(pitch와 roll 각도)를 활용하였고, GVR 가강수량이 20 mm 이상의 자료를 제거하는 방법을 품질 관리에 적용하였다. GVR 가강수량이 20 mm 이상으로 증가할 때, 웜로드(Warm load) 평균 전력과 스카이로드(Sky load) 평균 전력의 차이가 0에 가까이수렴하는 특성을 확인하였고, 이는 COMS (Communication, Ocean and Meteorological Satellite)의 운형, 운정고도, 운량자료와 구름통합관측기기(CCP), 강수입자 측정기(PIP)로 측정된 강수 및 구름 입자 크기로 확인한 하층운과 중층운에의한 높은 밝기온도 때문으로 판단된다. 구름 많은 날의 품질 관리 적용 전후의 GVR 가강수량을 LDAPS (Local Data Assimilation and Prediction System) 가강수량과 정량적으로 비교하였는데 RMSD (Root Mean Square Difference)는2.9 mm에서 1.8 mm로 감소하였고, KLAPS (Korea Local Analysis and Prediction System)와의 RMSD는 5.4 mm에서4.3 mm로 감소하여 향상된 정확도를 보였다. 또한 품질 관리를 적용한 GVR 가강수량과 드롭존데 가강수량 관측 자료을 활용하여 COMS 가강수량과도 정량적으로 비교평가함으로써 본 연구에서 제안한 GVR 가강수량의 품질 관리 방법의 유효성을 확인하였다.

In this study, the observational characteristics of dual-polarization radar for studying the heavy rainfall in the capital area were evaluated. To this end, three rainfall events that had caused significant flood damage due to the heavy rainfall from June to August 2020 were selected. The application of dual-polarization radar data revealed a significant change in precipitation during the analysis of observation characteristics of the affected capital area’s heavy rainfall. Moreover, the physical characteristics of reflectivity, differential reflectivity, specific differential phase, and copolar-correlation coefficient and their temporal dynamics in radar rainfall were compared to rain gauge rainfall. The radar reflectivity has been identified at the frequency of ≥ 40 dBZ due to the effects of heavy rainfall in the capital area, while the differential reflectivity has confirmed that the particle size was larger. The specific differential phase seemingly exhibited the most similar distribution to rain gauge rainfall, while exhibiting local characteristics of heavy rainfall in the capital area, according to the differential reflectivity result. The copolar-correlation coefficient confirmed the consistent observational performance of the dual-polarization radar for evaluation of heavy rainfall. Overall, the radar rainfall demonstrated good performance in simulating the peak time, and CSU-HIDRO estimated the rainfall with up to 32.4% more accuracy than JPOLE.
본 연구에서는 수도권 지역의 집중호우 현상에 대한 연구용 이중편파 레이더의 관측 특성을 분석하였다. 이를 위해 2020년 6~8월 집중호우로 인한 침수 피해가 크게 발생했던 세 개 호우사상을 선별하였다. 강우 변화가 크게 나타난 시간에 관측된 레이더 자료를 이용하여 수도권 지역에 영향을 준 집중호우의 관측 특성을 분석하였다. 이를 위해 시간별 반사도, 차등반사도, 비차등위상, 교차상관계수의 물리적 특성과 지상 강우 대비 레이더 강우의 변화를 분석하였다. 그 결과 수도권 지역에서 집중호우 영향으로 레이더 반사도는 40 dBZ 이상 감지될 수 있었고, 차등반사도는 입자 크기가 증가된 것을 확인해 주었다. 비차등위상은 지상 강우와 가장 유사한 분포로 나타났으며, 차등반사도와 같이 수도권 집중호우의 국지적 특성을 나타냈다. 교차상관계수는 집중호우 관측에 대한 연구용 이중편파 레이더의 일관된 관측 성능을 확인해 주었다. 아울러 레이더 강우는 첨두 시간을 잘 모의하였고, JPOLE보다 CSU-HIDRO가 최대 32.4% 더 정확하게 강우를 추정한 것으로 나타났다.

In this study, we investigated the characteristics of the meteorological and environmental conditions for a cloud seeding experiment over the Korean peninsula and estimated the available days for the same. The conditions of available days appropriate for a cloud seeding experiment were classified according to four purposes: water resources, drought relief, forest fire prevention, and air quality improvement. The average number of available days for a cloud seeding experiment were 91.27 (water resources), 45.93–51.11 (drought relief), 40.28–46.00 (forest fire prevention), and 42.19–44.60 days/year (air quality improvement). If six experiments were carried out per available day for a cloud seeding experiment, the number of times cloud seeding experiments could be conducted per year in a continuously operating system were estimated as 547.62 (water resources), 275.58–306.66 (drought relief), 241.68–276.00 (forest fire prevention), and 253.14–267.60 times/year (air quality improvement). From this result, it was possible to determine the appropriate meteorological and environmental conditions and statistically estimate the available days for a cloud seeding experiment. The data on the available days for a cloud seeding experiment might be useful for preparing and performing such an experiment.

In this study, we investigated the optimal meteorological conditions for cloud seeding using aircraft over the Korean Peninsula. The weather conditions were analyzed using various data sources such as a weather chart, upper air observation, aircraft observation, and a numerical model for cloud seeding experiments conducted from 2018 to 2019 by the National Institute of Meteorological Sciences, Korea Meteorological Administration. Cloud seeding experiments were performed in the seasons of autumn (37.0%) and winter (40.7%) in the West Sea and Gangwon-do. Silver iodide (70.4%) and calcium chloride (29.6%) were used as cloud seeding materials for the experiments. The cloud seeding experiments used silver iodide in cold clouds. Aircraft observation revealed relatively low temperatures, low liquid water content, and strong wind speeds in clouds with a weak updraft. In warm clouds, the cloud seeding experiments used calcium chloride. Observations included relatively high temperatures, high liquid water content, and weak wind speeds in clouds with a weak updraft. Based upon these results, we determined the comprehensive meteorological conditions for cloud seeding experiments using aircraft over the Korean Peninsula. The understanding of optimal weather conditions for cloud seeding gained from this study provide information critical for performing successful cloud seeding and rain enhancement.

Weather modification research has been actively performed worldwide, but a technology that can more quantitatively prove the research effects are needed. In this study, the seeding effect, the efficiency of precipitation enhancement in weather modification experiment, was verified using the radar data. Also, the effects of seeding material on hydrometeor change was analyzed. For this, radar data, weather conditions, and numerical simulation data for diffusion were applied. First, a method to analyze the seeding effect in three steps was proposed: before seeding, during seeding, and after seeding. The proposed method was applied to three cases of weather modification experiments conducted in Gangwon-do and the West Sea regions. As a result, when there is no natural precipitation, the radar reflectivity detected in the area where precipitation change is expected was determined as the seeding effect. When natural precipitation occurs, the seeding effect was determined by excluding the effect of natural precipitation from the maximum reflectivity detected. For the application results, it was found that the precipitation intensity increased by 0.1 mm/h through the seeding effect. In addition, it was confirmed that ice crystals, supercooled water droplets, and mixed-phase precipitation were distributed in the seeding cloud. The results of these weather modification research can be used to secure water resources as well as for future study of cloud physics.
전세계적으로 기상조절 연구가 활발히 수행되어져 왔으나 연구 효과를 보다 정량적으로 검증할 수 있는 기술이 필요하다. 본 연구에서는 기상조절 실험에 대한 강수 증가 효율인 시딩효과(seeding effect)를 레이더 자료를 이용하여 검증하였다. 또한, 시딩물질이 대기수상체 변화에 미치는 영향을 분석하였다. 이를 위해 레이더 자료, 기상조건, 확산 수치모의 자료가 사용되었다. 먼저, 시딩전, 시딩중, 시딩후의 세 단계로 시딩효과를 분석할 수 있는 방법을 제안하였다. 제안한 방법을 강원도와 서해 지역을 대상으로 수행된 세 개의 기상조절 실험 사례에 적용하였다. 그 결과, 자연강수가 없을 때는 강수 변화가 예측된 구역에서 감지된 레이더 반사도가 시딩효과로 판단되었다. 자연강수가 발생하면 관측된 최대 반사도에서 자연강수의 영향을 제외하여 시딩효과를 결정하였다. 적용사례에 대해 시딩효과로 강수강도가 0.1 mm/h 증가한 것으로 나타났다. 아울러 시딩 구름에 빙정이나 과냉각 수적, 혼합상의 수상체가 분포한 것으로 나타났다. 이러한 기상조절 연구 결과는 수자원 확보와 구름 물리 연구에 활용할 수 있을 것으로 판단된다.

The statistical characteristics of aerosol–cloud interactions over East Asia were investigated using Moderate Resolution Imaging Spectroradiometer satellite data. The long-term relationship between various aerosol and cloud parameters was estimated using correlation analysis, principle component analysis, and Aerosol Indirect Effect (AIE) estimation. In correlation analysis, Aerosol Optical Depth (AOD) was positively Correlated with Cloud Condensation Nuclei (CCN) and Cloud Fraction (CF), but negatively correlated with Cloud Top Temperature (CTT) and Cloud Top Pressure (CTP). Fine Mode Fraction (FMF) and CCN were positively correlated over the ocean because of sea spray. In principle component analysis, AOD and FMF were influenced by water vapor. In particular, AOD was positively influenced by CF, and negatively by CTT and CTP over the ocean. In AIE estimation, the AIE value in each cloud layer and type was mostly negative (Twomey effect) but sometimes positive (anti-Twomey effect). This is related to regional, environmental, seasonal, and meteorological effects. Rigorous and extensive studies on aerosol–cloud interactions over East Asia should be conducted via micro- and macro-scale investigations, to determine chemical characteristics using various meteorological instruments.