• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• Atmosphere
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• v.22 no.2
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• pp.279-285
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• 2012

The weighing precipitation gauge with auto-empting capability was developed in the R&D project organized by the Research Agency for Climate Science (RACS) and supported by the Korea Meteorological Administration (KMA). This project was initiated in line with the KMA's plan executed since 2010 to introduce the weighing precipitation gauges partly into of their Automatic Weather Station (AWS) network in order to upgrade the quality of precipitation data. The innovative feature of this research is that the auto-empting in weighing precipitation gauge is realized by abrupt rotation of receiving container. The prototype was tested in compliance with the relevant standards of KMA. The results of performance test on rainfall measurement in laboratory verified that the accuracies for 20 mm and 100 mm reference rainfall amount were 0.1 mm and 0.4 mm, respectively in both conditions of auto-empting and no-empting. During the rotation of container for auto-empting, the data was extrapolated smoothly by applying the same precipitation intensity of the previous 10 sec. Consequently, it was found that the auto-empting precipitation gauge developed in this research is quite enough to be used for the operational purpose of accurate measurement with 0.1 mm resolution, regardless of the precipitation intensity.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.514-523
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• 2021

Using the precipitation data observed at the Gochang Standard Weather Observatory (GSWO) during the winter seasons from 2014 to 2016, we analyzed the precipitation characteristics of the winter observation environment. For this study, we used four different types of precipitation gauges, i.e., No Shield (NS), Single Alter (SA), Double Fence Intercomparison Reference (DFIR), and Pit Gauge (PG). We analyzed the data from each to find differences in the accumulated precipitation, characteristics of the precipitation type, and the catch efficiency according to the wind speed based on the DFIR. We then classified these into three precipitation types, i.e., rain, mixed precipitation, and snow, according to temperature data from Gochang's Automated Synoptic Observing System (ASOS). We considered the DFIR to be the standard precipitation gauge for our analysis and the cumulative winter precipitation recorded by each other gauge compared to the DFIR data in the following order (from the most to least similar): SA, NS, and PG. As such, we find that the SA gauge is the most accurate when compared to the standard precipitation gauge used (DFIR), and the PG system is inappropriate for winter observations.

• 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 Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2745-2752
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• 2013

The tipping-bucket and weight measuring type precipitation gauge has long been used worldwide for measuring rainfall. However, the conventional gauge has observation errors and its measurement range is limited by the device's resolution. In this paper, a new type of precipitation gauge that uses an innovative method by applying a new ultrasonic flow measuring technique was developed. This is the first time this technique is being used to gauge rainfall. The prototype was tested in the laboratory designated by the Korea Laboratory Accreditation Scheme (KOLAS). The rainfall intensity condition was 20~420 mm/H and the Standard Correction System for Precipitation Gauges was used. Results of the laboratory experiment showed that the proposed gauge has a ${\pm}2%$ margin of error. Consequently, it was proven that the proposed gauge is quite accurate and reliable for measuring precipitation.

• Journal of Environmental Science International
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• v.24 no.4
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• pp.525-540
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• 2015

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

• Atmosphere
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• v.28 no.3
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• pp.273-289
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• 2018

This paper evaluates daily precipitation products from Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission Multisatellite (TRMM) Precipitation Analysis (TMPA), and the Climate Prediction Center Morphing Method (CMORPH), validated against gauge observation over South Korea and gauge-based analysis data East Asia during one year from June 2014 to May 2015. It is found that the three products effectively capture the seasonal variation of mean precipitation with relatively good correlation from spring to fall. Among them, IMERG and TMPA show quite similar precipitation characteristics but overall underestimation is found from all precipitation products during winter compared with observation. IMERG shows reliably high performance in precipitation for all seasons, showing the most unbiased and accurate precipitation estimation. However, it is also noticed that IMERG reveals overestimated precipitation for heavier precipitation thresholds. This assessment work suggests the validity of the IMERG product for not only seasonal precipitation but also daily precipitation, which has the potential to be used as reference precipitation data.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.62-66
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• 2017

In this paper, we present the development of communication and data processing strategy for the electromagnetic wave precipitation gauge system. The electromagnetic wave precipitation gauge system is a small system for deriving area rainfall rates within 1 km radius through dual polarization radar observation at 24GHz band. It is necessary to take consider for measurement of accurate precipitation under limited computing resources originating from small systems and to minimize the use of network for the unattended operation and remote management. To overcome computational resource limitations, we adopted the fuzzy logic for quality control to eliminate non-precipitation echoes and developed the method by weighted synthesis of various rain rate fields using multiple radar QPE formulas. Also we have designed variable data packets rules to minimize the network traffic.

• Atmosphere
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• v.29 no.3
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• pp.269-282
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• 2019

While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).

• Journal of the Korean earth science society
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• v.35 no.2
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• pp.115-130
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• 2014

The growing possibility of the disaster due to severe weather calls for disaster prevention and water management measures in South Korea. In order to prevent a localized heavy rain from occurring, the rainfall must be observed and predicted quantitatively. In this study, we developed an adjustment algorithm to estimate the radar precipitation applying to the local gauge correction (LGC) method which uses geostatistical effective radius of errors of the radar precipitation. The effective radius was determined from the errors of radar rainfall using geostatistical method, and we adjusted radar precipitation for four heavy rainfall events based on the LGC method. Errors were decreased by about 40% and 60% in adjusted hourly rainfall accumulation and adjusted total rainfall accumulation for four heavy rainfall events, respectively. To estimate radar precipitation for localized heavy rain events in summer, therefore, we believe that it was appropriate for this study to use an adjustment algorithm, developed herein.