• 한국환경과학회지
• /
• 제31권2호
• /
• pp.117-129
• /
• 2022

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.

• 한국환경과학회지
• /
• 제33권1호
• /
• pp.43-57
• /
• 2024

The possible experimental time for cloud seeding was analyzed in South Korea. Rain gauge and radar precipitation data collected from September 2017 to August 2022 in from the three main target stations of cloud seeding experimentation (Daegwallyeong, Seoul, and Boryeong) were analyzed. In this study, the assumption that rainfall and cloud enhancement originating from the atmospheric updraft is a necessary condition for the cloud seeding experiment was applied. First, monthly and seasonal means of the precipitation duration and frequency were analyzed and cloud seeding experiments performed in the past were also reanalyzed. Results of analysis indicated that the experiments were possible during a monthly average of 7,025 minutes (117 times) in Daegwallyeong, 4,849 minutes (81 times) in Seoul, and 5,558 minutes (93 times) in Boryeong, if experimental limitations such as the insufficient availability of aircraft is not considered. The seasonal average results showed that the possible experimental time is the highest in summer at all three stations, which seems to be owing to the highest precipitable water in this period. Using the radar-converted precipitation data, the cloud seeding experiments were shown to be possible for 970-1,406 hours (11-16%) per year in these three regions in South Korea. This long possible experimental time suggests that longer duration, more than the previous period of 1 hour, cloud seeding experiments are available, and can contribute to achieving a large accumulated amount of enhanced rainfall.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.461-463
• /
• 2003

The information on the cloud presence within a instantaneous field of view is the first step toward the derivation of many other geophysical parameters. Here, we first applied the current MODIS cloud detection algorithm developed by University of Wisconsin and compared the results to a visual interpretation of composite data, especially during the daytime. Most of cases, the detection algorithm performs very well, except a few cases with over-detection. One of the reasons for the false detection is due to the time independent use of land information which affects the threshold values of visible channel test. In the presentation, we show detailed analysis of the current cloud detection algorithm and suggest possible way to overcome the current shortfall.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
• /
• pp.34-37
• /
• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• 대기
• /
• 제24권1호
• /
• pp.39-48
• /
• 2014

This study provides a comparative analysis of cloud top heights observed by a Ka-band cloud radar and the Communication, Ocean and Meteorological Satellite (COMS) at Boseong National Center for Intensive Observation of severe weather (NCIO) from May 25, 2013 (1600 UTC) to May 27. The rainfall duration is defined as the period of rainfall from start to finish, and the no rainfall duration is defined as the period other than the rainfall duration. As a result of the comparative analysis, the cloud top heights observed by the cloud radar have been estimated to be lower than that observed by the COMS for the rainfall duration due to the signal attenuation caused by raindrops. The stronger rainfall intensity gets, the more the difference grows. On the other hand, the cloud top heights observed by the cloud radar have been relatively similar to that observed by the COMS for the no rainfall duration. In this case, the cloud radar can effectively detect cloud top heights within the range of its observation. The COMS indicates the cloud top heights lower than the actual ones due to the upper thin clouds under the influence of ground surface temperature. As a result, the cloud radar can be useful in detecting cloud top heights when there are no precipitation events. The COMS data can be used to correct the cloud top heights when the radar gets beyond the valid range of observation or there are precipitation events.

• 한국태양에너지학회 논문집
• /
• 제29권6호
• /
• pp.102-109
• /
• 2009

The research on meteorological data in Korea has been carried out but without much consistency and has been limited to some areas only. Of relatively more importance has been the area in the utilization of the solar energy, however, the measurement of the global solar horizontal irradiation has been quite limited. In the current study, the actually measured value of the global solar horizontal irradiation from the meteorological data and the theoretically calculated value of the global solar horizontal irradiation from the cloud amount will be analyzed comparatively. The method of analysis will employ the standard meteorological data drafted by the Korean Solar Energy Society, the standard meteorological data from the presently used simulation program and the corresponding results have been compared with the calculated value of the global solar horizontal irradiation from the cloud amount. The results of comparing the values obtained from MBE(Mean Bias Error), RMSE(Root Mean Squares for Error), t-Statistic methods and those from each of the standard meteorological data show that the actually measured value of the meteorological data which have been converted into standard meteorological data with the help of the ISO TRY method give the monthly average value of the global solar horizontal irradiation. These values compared with the monthly average value from the IWEC from the Department of Energy of the USA show that the value of the global solar horizontal irradiation in the USA is quite similar. In the case of the values obtained from calculation from the cloud amount, the weather data provided by TRNSYS, except only slight difference, which means that the actually measured values of the global solar horizontal irradiation are significant. This goes to show that in the case of Korea, the value of the global solar horizontal irradiation provided by the Korea Meteorological Administration is will be deemed correct.

• Journal of Astronomy and Space Sciences
• /
• 제33권2호
• /
• pp.137-146
• /
• 2016

The correlation between meteorological data collected at the optical wide-field patrol network (OWL-Net) Station No. 1 and the seeing of satellite optical observation data was analyzed. Meteorological data and satellite optical observation data from June 2014 to November 2015 were analyzed. The analyzed meteorological data were the outdoor air temperature, relative humidity, wind speed, and cloud index data, and the analyzed satellite optical observation data were the seeing full-width at half-maximum (FWHM) data. The annual meteorological pattern for Mongolia was analyzed by collecting meteorological data over four seasons, with data collection beginning after the installation and initial set-up of the OWL-Net Station No. 1 in Mongolia. A comparison of the meteorological data and the seeing of the satellite optical observation data showed that the seeing degrades as the wind strength increases and as the cloud cover decreases. This finding is explained by the bias effect, which is caused by the fact that the number of images taken on the less cloudy days was relatively small. The seeing FWHM showed no clear correlation with either temperature or relative humidity.

• 한국대기환경학회지
• /
• 제15권6호
• /
• pp.747-755
• /
• 1999

Effects of precipitation and cloud cover on high ozone days are examined by investigating the precipitation and average cloud cover before the ozone peak time within a day. High ozone days above 100 ppb in the Greater Seoul Area(GSA) for the ozone season from May to September are chosen for the analyses in terms of the surface meteorological data during 1990~1997. The result shows that the effect of precipitation on the rise of ozone concentration is definitely negative so that ozone concentration could not rise above 100ppb immediately after precipitation. But, the effect of cloud cover is associated with the variations of other meteorological parameters. The number of high ozone days with "zero" cloud cover is rather less than that with cloud cover of 1 to 4 since temperature is usually lower in "zero" cloud cover days. Furthermore, ozone concentration can rise above 100ppb even with full cloud cover when the wind is weak and the temperature is high.temperature is high.

• 대기
• /
• 제29권5호
• /
• pp.609-614
• /
• 2019

Cloud seeding experiment has been proposed as a way to alleviate severe air pollution problem because, if successful, artificially produced precipitation through cloud seeding could scavenge out some portion of air pollutants. As a first step to verify the practicality of such experiment, seedability of the clouds observed in Seoul is assessed by examining statistical characteristics of some relevant meteorological variables. Analyses of 9 years of Korea Meteorological Agency Seoul station data indicate that as PM₁₀ mass concentration increases, cloud amount, liquid water path, and ice water path decrease, but the difference between temperature and dew point temperature tends to increase. Such finding suggests that cloud seeding becomes less feasible as air pollution becomes more severe in the Seoul metropolitan area, at least in a statistical sense. For some individual severe air pollution events, however, seedable clouds may exist and indeed cloud seeding experiments can be successful. Therefore, detailed investigation on cloud seedability for individual severe air pollution events are highly required to make a concrete assessment of cloud seeding as a way to alleviate severe air pollution problem.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
• /
• pp.286-289
• /
• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.