• Resources Recycling
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• v.29 no.5
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• pp.28-37
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• 2020

In this study, the effect of precipitation temperature, ammonium chloride amount and addition method, vanadium and sodium hydroxide content of the solution on the precipitation of ammonium metavanadate were examined by using the sodium vanadate(NaVO₃) solution in alkali region as a starting material. As the pH of solution decreased, the addition amount of ammonium chloride and the vanadium content of the solution increased, the precipitation rate of ammonium metavanadate increased. In this research condition, the basic conditions for obtaining more than 90% of precipitation yield were 10,000mg/L of vanadium content, 2equivalents of ammonium chloride addition, room temperature, and 2 hours of precipitation time. The size of precipitated particles decreased with increasing precipitation rate. Especially when liquid ammonium chloride was injected into the solution, the precipitation rate was the slowest and the particle size of the precipitate was the largest. After the primary precipitation by adding ammonium chloride as a solid, the secondary precipitation was carried out by adding new reactants. At this time, the precipitation with added ammonium chloride solid was not affected by the precipitates present in the solution. However, when liquid ammonium chloride was added, new precipitate was deposited on the surface of the precipitate present in the solution, increasing its size. Due to the difference in ammonium metavanadate solubility to temperature, the precipitation temperature at the vanadium content of 10,000mg/L in the solution affected the precipitation rate of ammonium metavanadate and the precipitation temperature did not affect the precipitation rate at a high concentration of more than 30,000mg/L vanadium content in the solution.

• Journal of Environmental Science International
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• v.19 no.9
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• pp.1109-1118
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• 2010

Characteristics of precipitation and temperature in Ulleung-do and Dok-do were analyzed with hourly accumulated precipitation and mean temperature data obtained from Automatic Weather System(AWS) for latest four years(2005~2008). In Ulleung-do, total annual mean precipitation for this period is 1,574.4 mm, which shows larger amount than 1434.2 mm of whole Korean peninsula for latest 10 years(1999~2008) and 1,236.2 mm at Ulleung-do on common years(1971~2000), shows that the trend of precipitation gradually increases during the recent years. This amount is also 1.4 times larger than the total annual mean precipitation of 660.1 mm in Dok-do. Mean precipitation intensity(mm $h^{-1}$) at each time of a day in each month at Ulleung-do represents that the maximum values larger than $3.0\;mm\;h^{-1}$ were shown in May and on 0200 LST, whereas these were found in August and 0700 LST with $3.1\;mm\;h^{-1}$ in Dok-do. The difference of the precipitation amount and its intensity between Uleung-do and Dok-do is explained by the topological effect came from each covering area, and this fact is also identified from similar comparison of the precipitation characteristics for the islands in West Sea. The annual mean temperature of $14.0^{\circ}C$ in Dok-do is $1.2^{\circ}C$ higher than that of $12.8^{\circ}C$ in Ulleung-do. Trends of monthly mean temperature in both islands are shown to increase for the observed period.

• Atmosphere
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• v.24 no.3
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• Journal of the Korean earth science society
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• v.26 no.5
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• pp.417-428
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• 2005

The seasonal variation and frequency of precipitation phenomenon of the Korean Peninsula in summer show strong local weather phenomena because of its topographical and geographical factors in the northeastern area of Asia. The characteristics of the prevailing weather patterns in summer precipitation in Korea have great influences on the variation patterns and the appearances over a ten-day period during the summer precipitation. The purpose of this paper is to induce variation patterns over a period 10 days during the summer precipitation, clarify the variations of their space scales, and study the subdivision of precipitation regions in Korea according to the combinations of precipitation amounts and variation pattern during the period, using the mean values during the years $1991\~2003$ at 78 stations in Korea. The classified precipitation of a period of 10 days of summer precipitation, and the principal component vector and the amplitude coefficient by the principal component analysis were used for this study. The characteristics of variation pattern over the ten-day period can be chiefly divided into two categories and the accumulated contributory rate of these is $64.3\%$. The variation patterns of summer precipitation during period of 10 days in Korea are classified into 9 types from A to K. In addition, regional divisions of summer precipitation in Korea can be classified into 17 types.

• Atmosphere
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• v.30 no.4
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• pp.335-346
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• 2020

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

• Atmosphere
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• v.24 no.2
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• pp.159-171
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• 2014

This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)is Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm $day^{-1}$ or 80 mm $h^{-1}$ can occur with the return period of 100 years over the Korean Peninsula.

• Atmosphere
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• v.22 no.4
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• pp.449-454
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• 2012

This study presents the long-term variability of spring precipitation over the Korean peninsula. It is found that the significant interdecadal change in the spring precipitation has occurred around year 1991. Over the Korean peninsula the precipitation for the post-1991 period increased by about 30 mm per year in CMAP and station-measured data compared to the precipitation prior to year 1991. Due to an increased baroclinicity during the later period, the low-level negative pressure anomaly has developed with its center over northern Japan. Korea is situated at the western end of the negative pressure anomaly, receiving moisture from westerly winds and producing more precipitation. Also, we estimate the change in the near future (years 2020~2040) spring precipitation using six best performing Coupled Model Intercomparison Project 3 (CMIP3) models. These best model ensemble mean shows that spring precipitation is anticipated to increase by about 4% due to the strengthened westerlies accompanied by the northwestern enhancement of the North Pacific subtropical high.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.147-163
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• 2013

The purpose of this study is to compare precipitation distributions in precipitation data sets over South Korea produced by three interpolation methods. The differences of precipitation caused by interpolation methods is an important information when the interpolated precipitation data sets were used in researches such as ecological and hydrological modeling as well as regional climate impact studies. In this study, the precipitation data sets were produced by IDW(Inverse Distance Weighting) and Cokriging in this study and the PRISM(Precipitation-elevation Regressions on Independent Slopes Model) data set obtained from Climate Change Information Center of Korea. The spatial resolution of the precipitation data is 1km. As a result, there was a great precipitation difference caused by interpolation methods in data of mountainous watersheds in general. Especially the difference of monthly precipitation was 10~20% or more in the mountainous watersheds near the Military Demarcation Line dividing North and South Korea, Mt. Sobaik, Mt. Worak, Mt. Deogyu, Mt. Jiri and Taeback Mountain Range. It means that a final result of a research can be affected by adopted interpolation method when an interpolated precipitation data set is used in the research for the these study sites.