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Frequency distribution of ice phase seedable clouds (a–d) and cloud characteristics (CTH: (e–h), CTT: (i–l)) around the Korean Peninsula.
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Our study analyzed the occurrence frequency and distribution of seedable clouds around the Korean Peninsula in order to better secure water resources. Cloud products from the Communication, Ocean, and Meteorological Satellite (COMS), including cloud fraction, cloud top height, cloud top temperature, cloud phase, cloud top pressure, cloud optical th...
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... The Korean Peninsula shows various distributions and visually different characteristics of cloud cover, owing to the influence of seasonal air masses and geographical characteristics (Kim et al., 2021b). In other words, in winter, the sky is generally clear, and cloud occurrence frequency and cloud height are low, owing to the influence of the Siberian air mass; in summer, the weather is generally cloudy, and cloud occurrence frequency and cloud height are high, owing to the influence of the Okhotsk Sea and North Pacific air mass; and in spring and fall, the weather is fluid, owing to the influence of the Yangtze River air mass Kim et al., 2020aKim et al., , 2021a. In addition, the Korean Peninsula is located in the westerly wind zone, and cumulus heat clouds generated in the West Sea flow inland and develop (Kim et al., 2020b). ...
In this study, we aimed to estimate cloud cover with high accuracy using images from a camera-based imager and a convolutional neural network (CNN) as a potential alternative to human-eye observation on the ground. Image data collected at 1 h intervals from 2019 to 2020 at a staffed weather station, where human-eye observations were performed, were used as input data. The 2019 dataset was used for training and validating the CNN model, whereas the 2020 dataset was used for testing the estimated cloud cover. Additionally, we compared satellite (SAT) and ceilometer (CEI) cloud cover to determine the method most suitable for cloud cover estimation at the ground level. The CNN model was optimized using a deep layer and detailed hyperparameter settings. Consequently, the model achieved an accuracy, bias, root mean square error (RMSE), and correlation coefficient (R) of 0.92, −0.13, 1.40 tenths, and 0.95, respectively, on the test dataset, and exhibited approximately 93 % high agreement at a difference within ±2 tenths of the observed cloud cover. This result demonstrates an improvement over previous studies that used threshold, machine learning, and deep learning methods. In addition, compared with the SAT (with an accuracy, bias, RMSE, R, and agreement of 0.89, 0.33 tenths, 2.31 tenths, 0.87, and 83 %, respectively) and CEI (with an accuracy, bias, RMSE, R, agreement of 0.86, −1.58 tenths, 3.34 tenths, 0.76, and 74 %, respectively), the camera-based imager with the CNN was found to be the most suitable method to replace ground cloud cover observation by humans.
... The Korean Peninsula shows various distributions and visually different characteristics of cloud cover owing to the influence 180 of seasonal air masses and geographical characteristics (Kim et al., 2021b). In other words, in winter, the sky is generally clear, and cloud occurrence frequency and cloud height are low owing to the influence of the Siberian air mass; in summer, the weather is generally cloudy, and cloud occurrence frequency and cloud height are high owing to the influence of the Okhotsk Sea and North Pacific air mass; and in spring and autumn, the weather is fluid owing to the influence of the Yangtze River air mass Kim et al., 2020a;Kim et al., 2021a). In addition, the Korean Peninsula is located in the westerly 185 wind zone, and cumulus heat clouds generated in the West Sea flow inland and develop (Kim et al., 2020b). ...
In this study, we aimed to estimate cloud cover with high accuracy using images from a camera-based imager and a convolutional neural network (CNN) as a potential alternative to human-eye observation on the ground. Image data collected at 1 h intervals from 2019 to 2020 at a manned weather station, where human-eye observations were performed, were used as input data. The 2019 dataset was used for training and validating the CNN model, whereas the 2020 dataset was used for testing the estimated cloud cover. Additionally, we compared satellite (SAT) and ceilometer (CEI) cloud cover to determine the method most suitable for cloud cover estimation at the ground level. The CNN model was optimized using a deep layer and detailed hyperparameter settings. Consequently, the model achieved an accuracy, bias, root mean square error (RMSE), and correlation coefficient (R) of 0.92, −0.13, 1.40 tenths, and 0.95, respectively, on the test dataset, and exhibited approximately 93 % high agreement at a difference within ± 2 tenths of the observed cloud cover. This result demonstrates an improvement over previous studies that used threshold, machine learning, and deep learning methods. In addition, compared with the SAT (with an accuracy, bias, RMSE, R, and agreement of 0.89, 0.33 tenths, 2.31 tenths, 0.87, and 83 %, respectively) and CEI (with an accuracy, bias, RMSE, R, agreement of 0.86, −1.58 tenths, 3.34 tenths, 0.76, and 74 %, respectively), the camera-based imager with the CNN was found to be the most suitable method to replace ground cloud cover observation by humans.
... 기상조절 실험은 요오드화은(AgI) 또는 염화칼슘 (CaCl 2 )과 같은 연소탄을 구름에 인위적으로 살포하여 미세물리 과정을 부여함으로써 구름의 강수 효율을 증 가시켜 증우시키는 기술을 일컫는다 (Kim et al., 2020a). 또한 기상항공기를 이용한 인공증우 기술은 환경문제를 최소화하며, 비교적 적은 비용으로 강수를 유발시킬 수 있는 방안 중 하나이다 (Korneev et al., 2017). ...
Recently, interest in the possibility of a washout effect using artificial rain enhancement technology to reduce high-concentration fine dust is growing. Therefore, in this study, the reduction rate of PM10 concentration according to the amount of artificial rain enhancement was calculated 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 with the 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 calculated to be 1.5 mm hr–1. According the CMAQ results, the PM10 reduction rate was up to 22%, and
the affected area was calculated to be 6.4 times greater than that of the artificial rain enhancement area. 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 believed that this technique can be used as a direct method for reducing high-concentration fine dust even when the artificial rain enhancement effect is weak.
... (Jung et al., 2018;Li et al., 2019;Kim et al., 2020;대한 분포를 나타낸다. Fig. 10 ...
In this study, we improved the water-based condensation particle counter in Atmospheric Research Aircraft NARA and investigated the condensation particle number concentration over the Korean peninsula. Pump and set point information were changed to improve the instrument used by aircraft for observation. Ground-based observational result showed that the error between two instruments, which are water-based condensation particle counter and butanol-based condensation particle counter, was 4.7%. Aerial observational result revealed that the number concentration before improvement indicate large variation with unstable condition, whereas the number concentration after improvement indicate a reasonable variation. After improvement, the number concentration was 706±499 particle/㎤ in the West Sea and 257±80 particle/㎤ in Gangwon-do, and these are similar to the concentration range reported in previous studies. Notably, this is the first attempt to use aerial observation with water-based condensation particle counter to investigate condensation particle number concentration.
... Geostationary satellites can observe clouds on the global scale at intervals of several minutes; however, their spatial resolution is as large as several kilometers Lee et al., 2018). Polar satellites have spatial resolutions of several hundred meters, i.e., high resolution; however, they can observe the same area only once or twice per day (Kim and Lee, 2019;Kim et al., 2020a). For both geostationary and polar satellites, geometric distortion problems occur during cloud cover estimation on the ground, depending on the cloud height (Mantelli Neto et al., 2010). ...
... In the Korean Peninsula, the winter cloud cover is sparse (< 5 tenths) as the weather is generally clear because of the Siberian air mass. In summer, the rainy season is concentrated under the influence of the Yangtze River and Pacific air masses, and the cloud cover is dense (> 5 tenths) until fall because of typhoons Kim et al., 2020a). Furthermore, the Korean Peninsula experiences a westerly wind, cumulus heat generated in the western sea moves inland, and the cloud cover changes rapidly and continuously (Kim et al., 2021). ...
In this study, image data features and machine learning methods were used to calculate 24 h continuous cloud cover from image data obtained by a camera-based imager on the ground. The image data features were the time (Julian day and hour), solar zenith angle, and statistical characteristics of the red–blue ratio, blue–red difference, and luminance. These features were determined from the red, green, and blue brightness of images subjected to a pre-processing process involving masking removal and distortion correction. The collected image data were divided into training,
validation, and test sets and were used to optimize and evaluate the
accuracy of each machine learning method. The cloud cover calculated by each machine learning method was verified with human-eye observation data from a manned observatory. Supervised machine learning models suitable for
nowcasting, namely, support vector regression, random forest, gradient
boosting machine, k-nearest neighbor, artificial neural network, and multiple linear regression methods, were employed and their results were compared. The best learning results were obtained by the support vector regression model, which had an accuracy, recall, and precision of 0.94, 0.70, and 0.76, respectively. Further, bias, root mean square error, and correlation coefficient values of 0.04 tenths, 1.45 tenths, and 0.93, respectively, were obtained for the cloud cover calculated using the test set. When the difference between the calculated and observed cloud cover was allowed to range between 0, 1, and 2 tenths, high agreements of approximately 42 %, 79 %, and 91 %, respectively, were obtained. The proposed system involving a ground-based imager and machine learning methods is expected to be suitable for application as an automated system to replace human-eye observations.
... In the Korean Peninsula, the winter cloud cover is sparse (<5 tenths) as the 305 weather is generally clear because of the Siberian air mass. In summer, the rainy season is concentrated under the influence of the Yangtze-River and Pacific air masses, and the cloud cover is dense (>5 tenths) until fall because of typhoons (Kim et al., 2018a(Kim et al., , 2020a. Furthermore, the Korean Peninsula experiences a westerly wind, cumulus heat generated in the western sea moves inland, and the cloud cover changes rapidly and continuously (Kim et al., 2021). ...
In this study, image data features and machine learning methods were used to calculate 24-h continuous cloud cover from image data obtained by a camera-based imager on the ground. The image data features were the time (Julian day and hour), solar zenith angle, and statistical characteristics of the red-blue ratio, blue–red difference, and luminance. These features were determined from the red, green, and blue brightness of images subjected to a pre-processing process involving masking removal and distortion correction. The collected image data were divided into training, validation, and test sets and were used to optimize and evaluate the accuracy of each machine learning method. The cloud cover calculated by each machine learning method was verified with human-eye observation data from a manned observatory. Supervised machine learning models suitable for nowcasting, namely, support vector regression, random forest, gradient boosting machine, k-nearest neighbor, artificial neural network, and multiple linear regression methods, were employed and their results were compared. The best learning results were obtained by the support vector regression model, which had an accuracy, recall, and precision of 0.94, 0.70, and 0.76, respectively. Further, bias, root mean square error, and correlation coefficient values of 0.04 tenth, 1.45 tenths, and 0.93, respectively, were obtained for the cloud cover calculated using the test set. When the difference between the calculated and observed cloud cover was allowed to range between 0, 1, and 2 tenths, high agreement of approximately 42 %, 79 %, and 91 %, respectively, were obtained. The proposed system involving a ground-based imager and machine learning methods is expected to be suitable for application as an automated system to replace human-eye observations.
... Table 2 shows monthly comparisons of VISASOS, VISLDAPS, and VISRF alongside monthly mean data for the meteorological variables used in the RF model. The Korean Peninsula experiences a rainy season from late spring to late autumn (April-October), and in summer (June-August), the temperature and RH are high, with many cloudy days due to general low-pressure conditions [68][69][70]; meteorological conditions have the opposite characteristics during the winter season (September-February). Therefore, during the rainy season, with the exceptions of SS, DU, and O3, which naturally occur due to higher RH and rainfall, the mass concentrations of anthropogenic air pollutants tend to decrease [34,71]. SS and O3 generally have high mass concentrations on islands and near coastal areas, increasing in association with precipitation, surface temperature, and wind speed during the rainy season [72]. ...
... Based on the CAMS model data analyses, these air pollutants showed strong positive correlations (R = 0.53-0.98); the VISASOS data are strongly affected by RH, precipitation, and air pollutants, alt- Table 2 shows monthly comparisons of VIS ASOS , VIS LDAPS , and VIS RF alongside monthly mean data for the meteorological variables used in the RF model. The Korean Peninsula experiences a rainy season from late spring to late autumn (April-October), and in summer (June-August), the temperature and RH are high, with many cloudy days due to general low-pressure conditions [68][69][70]; meteorological conditions have the opposite characteristics during the winter season (September-February). Therefore, during the rainy season, with the exceptions of SS, DU, and O 3 , which naturally occur due to higher RH and rainfall, the mass concentrations of anthropogenic air pollutants tend to decrease [34,71]. SS and O 3 generally have high mass concentrations on islands and near coastal areas, increasing in association with precipitation, surface temperature, and wind speed during the rainy season [72]. ...
In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the
VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.
... Most radars located in South Korea are S-band and C-band rain radars, which are not suitable for cloud detection [40,41]. Therefore, geostationary satellites are most suitable for detecting and analyzing the characteristics of clouds occurring around the Korean Peninsula [35,42]. Data used in the analysis include cloud fraction, cloud top height, cloud top temperature, cloud phase, and rainfall intensity, using the hourly data from 0900 and 1800 LST observed from January 2017 to December 2019. ...
... Therefore, seedable clouds can be detected using cloud characteristics (cloud cover, cloud height, cloud top temperature, cloud phase, etc.) and information on the presence or absence of precipitation [48]. The proportion of seedable clouds in satellite data is defined as the case in which the cloud fraction exceeds 80%, cloud-top height is within 1-4 km, cloud phase is water (or cloud top temperature −5 • C or higher with mixed-phase) or ice (or cloud top temperature less than −5 • C with mixed-phase), and rainfall intensity is less than 5 mm/h, using the algorithm presented in Kim et al. [35]. This algorithm detected seedable clouds considering the actual height at which precipitation enhancement experiments were conducted in South Korea, as well as weather conditions such as weak natural precipitation or the absence of precipitation. ...
... This algorithm detected seedable clouds considering the actual height at which precipitation enhancement experiments were conducted in South Korea, as well as weather conditions such as weak natural precipitation or the absence of precipitation. The mean frequency of occurrence of seedable clouds within a 200 km radius of the dams was also calculated [35]. This is the maximum distance presumed to cause a seeding effect in the target area through the precipitation enhancement experiment [49][50][51]. ...
This study calculated the augmentation of water resources that can be achieved through precipitation enhancement and the ensuing economic benefits by conducting precipitation enhancement experiments using atmospheric aircraft in the catchment areas of 21 multipurpose dams in Korea. The maximum number of precipitation enhancement experiments to be carried out was estimated based on the frequency of occurrence of seedable clouds near each dam, using geostationary satellite data. The maximum quantity of water that can be obtained was calculated considering the mean precipitation enhancement and probability of success, as determined from the results of experiments conducted in South Korea during 2018-2019. The effective area of seeding was assumed 300 km 2. In addition, the amount of hydroelectric power generation possible was determined from the quantity of water thus calculated. In conclusion, it was established that an approximate increase of 12.89 million m 3 (90% confidence interval: 7.83-17.95 million m 3) of water, and 4.79 (2.91-6.68) million kWh of electric power generation will be possible through approximately 96 precipitation enhancement operations in a year at the catchment area of Seomjin River (SJ) dam which has a high frequency of occurrence of seedable clouds, a large drainage area, and a high net head. An economic benefit of approximately 1.01 (0.61-1.40) million USD can be anticipated, the benefit/cost ratio being 1.46 (0.89-2.04).
Under the leadership of the National Institute of Meteorological Sciences (NIMS), the first domestic autonomous flight-type weather modification experimental drone for fog and lower-level cloud seeding was developed in 2021. This drone is designed based on a multi-cop-ter configuration with a maximum takeoff weight of approximately 25 kg, enabling the installation of up to four burning flares for seeding materials and facilitating weather observations (temperature, pressure, humidity, and wind) as well as aerosol (PM 10 , PM 2.5 , and PM 1.0) particle measurements. This research aims to introduce the construction of the drone and its recent applications over the past two years, providing insights into the experimental procedures, effectiveness verification, and improvement directions of the weather modification drone-based rain enhancement. In particular, partial confirmation of the experimental effects was obtained through the fog dissipation experiment on December 10, 2021, and the lower-level cloud seed-ing case study on October 5, 2022. To enhance the scope and rainfall amount of weather modification experiments using drones, various technological approaches, including adjustments to experimental altitude, seeding lines, seeding amount, and verification methods are necessary. Through this research, we aim to propose the development direction for weather modification drone technology, which will serve as foundational technology for practical application of domestic rain enhancement technology.
