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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.134-134
• /
• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) 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 random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Atmosphere
• /
• v.24 no.2
• /
• pp.235-243
• /
• 2014

Sources of aviation turbulence vary through the seasons, especially in the East Asia including Korean peninsula, associated primarily with the changes in the jet/front system and convective activities. For this reason, a seasonal Korean aviation Turbulence Guidance (KTG) system (seasonal-KTG) is developed in the present study by using pilot reports (PIREPs) and analysis data of the operational Unified Model (UM) of the Korea Meteorological Administration (KMA) for two years between June 2011 and May 2013. Twenty best diagnostics of aviation turbulence in each season are selected by the method of probability of detection (POD) using the PIREPs and UM data. After calculating a weighting value of each selected diagnostics using their area under curve (AUC), the 20 best diagnostics are combined with the weighting scores into a single ensemble-averaged index by season. Compared with the current operational-KTG system that is based on the diagnostics applying all seasons, the performances of the seasonal-KTG system are better in all seasons, except in fall.

• Atmosphere
• /
• v.29 no.3
• /
• pp.257-267
• /
• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.

• Atmosphere
• /
• v.26 no.4
• /
• pp.509-522
• /
• 2016

Heavy snowfall events have occurred frequently in the Yeongdong region but understanding of these events have trouble in lack of snowfall observation in this region because it is composed of complex topography like the "Taebaek mountains" and the "East sea". These problems can be solved by quantitative precipitation estimation technique using remote sensing such as radar, satellite, etc. Two radars which are able to cover over Yeondong region were installed at Gangneung (GNG) and Gwangdeoksan (GDK). This study uses radar and water equivalent of snow cover to investigate the characteristics of radar echoes and the $Z_e-R$ relations associated with the 10 Yeongdong heavy snowfall events during the last 5 years (2010~2014). It was found that the heights which the probability of detection (POD) of snow detection by GNG radar is more than 80% are 3,000 m and 1,500 m in convective cloud and stratiform cloud, respectively. The vertical gradient of radar reflectivity is less decreased in convective cloud than stratiform cloud. However, POD by GDK radar are lower than 80% at all layers because the majority of Yeondong observational stations are more than 100 km away from GDK radar site. Furthermore, we examined $Z_e-R$ relation from the 10 events using GNG radar and compared the "a" and "b" obtained from these examinations at Sokcho (SC) and Daegwallyeong (DG). These "a" and "b" are estimated from radar echo at 500 m (SC) and 1,500 m (DG). The values of "a" differ in their stations such as SC and DG are 30~116 and 6~39, respectively. But "b" is 0.4~1.7 irrespective of stations. Moreover, the value of "a" increased with surface air temperature. Therefore, quantitative precipitation estimation in heavy snowfall events by radar echo using fixed "a" and "b" is difficult because these values changed according to those precipitation characteristics.

• Journal of Korea Water Resources Association
• /
• v.52 no.7
• /
• pp.475-482
• /
• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Journal of Korea Water Resources Association
• /
• v.53 no.3
• /
• pp.225-236
• /
• 2020

It is not easy to provide sufficient lead time for flood forecast in urban and small mountain basins using on-ground rain gauges, because the time concentration in those basins is too short. In urban and small mountain basins with a short lag-time between precipitation and following flood events, it is more important to secure forecast lead times by predicting rainfall amounts. The Han River Flood Control Office (HRFCO) in South Korea produces short-term rainfall forecasts using the Mcgill Algorithm for Precipitation-nowcast by Lagrangian Extrapolation (MAPLE) algorithm that converts radar reflectance of rainfall events. The Flash Flood Research Center (FFRC) in the Korea Institute of Civil Engineering and Building Technology (KICT) installed a flash flood forecasting system using the short-term rainfall forecast data produced by the HRFCO and has provided flash flood information in a local lvel with 1-hour lead time since 2019. In this study, we addressed the flash flood forecasting system based on the radar rainfall and the assessed the accuracy of the forecasting system for the recorded flood events occurred in 2019. A total of 31 flood disaster cases were used to evaluate the accuracy and the forecast accuracy was 90.3% based on the probability of detection.

• Journal of Magnetics
• /
• v.16 no.1
• /
• pp.36-41
• /
• 2011

If gas is leaking out of gas pipelines, it could cause a huge explosion. Accordingly, it is important to ensure the integrity of gas pipelines. Traditionally, over the years, gas-operating companies have used the ILI system, which is based on axial magnetic flux leakage (MFL), to inspect the gas pipelines. Relatively, there is a low probability of detection (POD) for the axial defects with the axial MFL-based ILI. To prevent the buried pipeline from corrosion, it requires a protective coating. In addition to the potential damage to the coating by environmental factors and external forces, there could be defects on the damaged coating area. Thus, it is essential that nondestructive evaluation methods for detecting axial defects (axial cracks, axial groove) and damaged coating be developed. In this study, an electromagnetic acoustic transducer (EMAT) sensor was designed and fabricated for detecting axial defects and coating disbondment. In order to validate the performances of the developed EMAT sensor, experiments were performed with specimens from axial cracks, axial grooves, and coating disbondment. The experimental results showed that the developed EMAT sensor could detect not only the axial cracks (minimum 5% depth of wall thickness) and axial grooves (minimum 10% depth of wall thickness), but also the coating disbondment.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.9
• /
• pp.771-780
• /
• 2021

The space Debris laser ranging system is called to be a definite type of satellite laser ranging system that measures the distance to satellites. It is a system that performs POD (Precise Orbit Determination) by measuring time of flight by firing a laser. Distance precision can be measured in mm-level units, and it is the most precise system among existing systems. Currently, KASI has built SLR in Sejong and Geochang, and utilized SLR data to verify the precise orbits of the STSAT-2C and KOMASAT-5. In recent years, due to the fall or collision of space debris, its satellites have been threatened, and in terms of security, laser tracking of space objects is receiving great interest in order to protect their own space assets and protect the safety of the people. In this paper, a 1.5m-class main mirror was applied for the system design of a multipurpose laser tracking system that considers satellite laser ranging and space object laser tracking. System preliminary performance analysis was performed based on Link Budget analysis considering specifications of major components.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.35 no.6
• /
• pp.1269-1276
• /
• 2015

The methodology of utilizing Intensity-Duration-flood Quantity (IDQ) curve for flood alert and warning was introduced and its performance was evaluated. For this purpose the lumped parameter model was calibrated and validated for gauged basin data set and the index precipitation equivalent to alert and warning flood was estimated. The index precipitation and IDQ curves associated by three different Antecedant Moisture Conditions (AMCs) are made provision for various possible flood scenarios. The test basin is Wonju-cheon basin ($94.4km^2$) located in Gangwon province, Korea. The IDQ curves corresponding to alert (50% of design flood level) and warning (70% of design flood level) level was estimated using the Clark unit hydrograph based lumped parameter model. The performance evaluation showed 0.704 of POD (Probability of Detection), 0.136 of FAR (False Alarm Ratio), and 0.633 of CSI (Critical Success Index), which is improved from the result of IDQ with single fixed AMC.