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Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examined four critical factors in DL-QPN, focusing on their impact on forecasting performance. These factors are the deep learning model (U-Net, as well as a convolutional long short-term memory, or ConvLSTM), input past sequence length (1, 2, or 3 h), loss function (mean squared error, MSE, or balanced MSE, BMSE), and ensemble aggregation. A total of 24 schemes were designed to measure the effects of each factor using weather radar data from South Korea with a maximum lead time of 2 h. A long-term evaluation was conducted for the summers of 2020–2022 from an operational perspective, and a heavy rainfall event was analyzed to examine an extreme case. In both evaluations, U-Net outperformed ConvLSTM in overall accuracy metrics. For the critical success index (CSI), MSE loss yielded better results for both models in the weak intensity range (≤ 5 mmh-1), whereas BMSE loss was more effective for heavier precipitation. There was a small trend where a longer input time (3 h) gave better results in terms of MSE and BMSE, but this effect was less significant than other factors. The ensemble by averaging results of using MSE and BMSE losses provided balanced performance across all aspects, suggesting a potential strategy to improve skill scores when implemented with optimal weights for each member. All DL-QPN schemes exhibited problems with underestimation and overestimation when trained by MSE and BMSE losses, respectively. All DL models produced blurry results as the lead time increased, while the non-DL model retained detail in prediction. With a comprehensive comparison of these crucial factors, this study offers a modeling strategy for future DL-QPN work using weather radar data. [ABSTRACT FROM AUTHOR]

Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called process identification based on vertical gradient signs (PIVSs), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup, based on the sign of the local vertical gradients of the reflectivity ZH and the differential reflectivity ZDR. The method is then applied to data from two frontal snowfall events, namely one in coastal Adélie Land, Antarctica, and one in the Taebaek Mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations, using a multi-angle snowflake camera, and with the output of a hydrometeor classification, based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. In particular, we are able to automatically derive and discuss the altitude and thickness of the layers where each process prevails for both case studies. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g., ZH and ZDR distribution for each process). We, finally, highlight the potential for extensive application to cold precipitation events in different meteorological contexts. [ABSTRACT FROM AUTHOR]

Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed datadriven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examines four critical factors in the DL-QPN, focusing on their effect on forecasting performance. The four key factors were the prediction design (single, recursive, and multiple predictions), deep learning model (U-Net and convolutional long short-term memory; ConvLSTM), input past sequence length (60 and 120 min), and output future sequence length (60 and 120 min). Twelve schemes were designed to measure the effects of each factor using weather radar data from South Korea. A long-term evaluation for 2018-2020 was conducted from an operational perspective, and a summer heavy rainfall event was analyzed to examine the extreme case. In both cases, U-Net demonstrated the best performance in the critical score index (CSI) with a multiple prediction design. U-Net performed better with shorter input sequences (i.e., 60 min) than with longer input sequences (i.e., 120 min), whereas ConvLSTM did not demonstrate a clear CSI difference over the input sequence length. Future sequence length did not have a significant effect on model performance. All the DL-QPN schemes exhibited underestimation and blurry results as the lead time increased. Through sensitivity analysis, U-Net was highly dependent on the most recent input time (i.e., 0 past minutes), whereas ConvLSTM was more sensitive to various time steps. With an explicit comparison of crucial factors, this study can provide a modeling strategy and contingency plan for future DL-QPN using weather radar data. [ABSTRACT FROM AUTHOR]

This article describes a four-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.pangaea.de/10.1594/PANGAEA.918315, Gehring et al. (2020a)). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85% of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before. [ABSTRACT FROM AUTHOR]

Numerical weather prediction models and probabilistic extrapolation methods using radar images have been widely used for precipitation nowcasting. Recently, machine-learning-based precipitation nowcasting models have also been actively developed for relatively short-term precipitation predictions. This study was aimed at developing a radar-based precipitation nowcasting model using an advanced machine-learning technique, conditional generative adversarial network (cGAN), which shows high performance in image generation tasks. The cGAN-based precipitation nowcasting model, named Rad-cGAN, developed in this study was trained with the radar reflectivity data of the Soyang-gang Dam basin in South Korea with a spatial domain of 128 × 128 pixels, spatial resolution of 1 km, and temporal resolution of 10 min. The model performance was evaluated using previously developed machine-learning-based precipitation nowcasting models, namely convolutional long short-term memory (ConvLSTM) and U-Net. In addition, Eulerian persistence model and pySTEPS, a radar-based deterministic nowcasting system, are used as baseline models. We demonstrated that Rad-cGAN outperformed reference models at 10 min lead time prediction for the Soyang-gang Dam basin based on verification metrics: Pearson correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), critical success index (CSI), and fraction skill scores (FSS) at an intensity threshold of 0.1, 1.0, and 5.0 mm h -1. However, unlike low rainfall intensity, the CSI at high rainfall intensity in Rad-cGAN deteriorated rapidly beyond the lead time of 10 min; however, ConvLSTM and baseline models maintained better performances. This observation was consistent with the FSS calculated at high rainfall intensity. These results were qualitatively evaluated using typhoon Soulik as an example, and through this, ConvLSTM maintained relatively higher precipitation than the other models. However, for the prediction of precipitation area, Rad-cGAN showed the best results, and the advantage of the cGAN method to reduce the blurring effect was confirmed through radially averaged power spectral density (PSD). We also demonstrated the successful implementation of the transfer learning technique to efficiently train the model with the data from other dam basins in South Korea, such as the Andong Dam and Chungju Dam basins. We used the pre-trained model, which was completely trained in the Soyang-gang Dam basin. Furthermore, we analyzed the amount of data to effectively develop the model for the new domain through the transfer learning strategies applying the pre-trained model using data for additional dam basins. This study confirmed that Rad-cGAN can be successfully applied to precipitation nowcasting with longer lead times and using the transfer learning approach showed good performance in dam basins other than the originally trained basin. [ABSTRACT FROM AUTHOR]

Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called Process Identification based on Vertical gradient Signs (PIVS), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition, and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup based on the sign of the local vertical gradients of the reflectivity ZH and the differential reflectivity ZDR. The method is then applied to data from two frontal snowfall events: one in coastal Adélie Land, Antarctica and one in the Taebaeck mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations using a Multi-Angle Snowflake Camera and with the output of a hydrometeor classification based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. For the Antarctic case study, we show that crystal growth by vapor deposition dominates above 2500 m a.g.l., aggregation and riming prevail between 1500 and 2500 m a.g.l. and snowflake sublimation by low-level katabatic winds occurs below 1500 m a.g.l.. For the event in South Korea, aggregation and riming dominate between 4000 and 4800 m a.g.l., with local sublimation below and vapor deposition above. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g. ZH and ZDR distribution for each process). We finally highlight the potential for extensive application to cold precipitation events in different meteorological contexts. [ABSTRACT FROM AUTHOR]

Numerical weather prediction models and probabilistic extrapolation methods using radar images have been widely used for precipitation nowcasting. Recently, machine-learning-based precipitation nowcasting models have also been actively developed for relatively short-term precipitation predictions. This study aimed to develop a radar-based precipitation nowcasting model using an advanced machine learning technique, conditional generative adversarial network (cGAN), which shows high performance in image generation tasks. The cGAN-based precipitation nowcasting model, named Rad-cGAN, developed in this study was trained with a radar reflectivity map of the Soyang-gang Dam region in South Korea with a spatial domain of 128 × 128 km, spatial resolution of 1 km, and temporal resolution of 10 min. The model performance was evaluated using previously developed machine-learning-based precipitation nowcasting models, namely convolutional long short-term memory (ConvLSTM) and U-Net, as well as the baseline Eulerian persistence model. We demonstrated that Rad-cGAN outperformed other models not only for the chosen site but also for the entire domain across the Soyang-gang Dam region. Additionally, the proposed model maintained good performance even with lead times up to 80 min based on the critical success index at the intensity threshold of 0.1 mm h-1, while RainNet and ConvLSTM achieved lead times of 70 and 40 min, respectively. We also demonstrated the successful implementation of the transfer learning technique to efficiently train model with the data from other dam regions in South Korea, such as the Andong and Chungju Dam regions. We used pre-trained model, which was completely trained in the Soyang-gang Dam region. This study confirms that Rad-cGAN can be successfully applied to precipitation nowcasting with longer lead times, and using the transfer learning approach it shows good performance in regions other than the originally trained region. [ABSTRACT FROM AUTHOR]

There are many potential sources of bias in the radar rainfall estimation process. This study classified the biases from the rainfall estimation process into the reflectivity measurement bias and QPE model bias and also conducted the bias correction methods to improve the accuracy of the Radar-AWS Rainrate (RAR) calculation system operated by the Korea Meteorological Administration (KMA). For the Z bias correction, this study utilized the bias correction algorithm for the reflectivity. The concept of this algorithm is that the reflectivity of target single-pol radars is corrected based on the reference dual-pol radar corrected in the hardware and software bias. This study, and then, dealt with two post-process methods, the Mean Field Bias Correction (MFBC) method and the Local Gauge Correction method (LGC), to correct rainfall-bias. The Z bias and rainfall-bias correction methods were applied to the RAR system. The accuracy of the RAR system improved after correcting Z bias. For rainfall types, although the accuracy of Changma front and local torrential cases was slightly improved without the Z bias correction, especially, the accuracy of typhoon cases got worse than existing results. As a result of the rainfall-bias correction, the accuracy of the RAR system performed Z bias_LGC was especially superior to the MFBC method because the different rainfall biases were applied to each grid rainfall amount in the LGC method. For rainfall types, Results of the Z bias_LGC showed that rainfall estimates for all types was more accurate than only the Z bias and, especially, outcomes in typhoon cases was vastly superior to the others. [ABSTRACT FROM AUTHOR]

The simulation of fast-reacting hydrological systems often requires sub-hourly precipitation data to develop appropriate climate adaptation strategies and tools, i.e. upgrading drainage systems and reducing flood risks. However, these sub-hourly data are typically not provided by measurements and atmospheric models, and many statistical disaggregation tools are applicable only up to an hourly resolution. Here, two different models for the disaggregation of precipitation data from a daily to sub-hourly scale are presented. The first one is a conditional disaggregation model based on first-order Markov chains and copulas (WayDown) that keeps the input daily precipitation sums consistent within disaggregated time series. The second one is an unconditional rain generation model based on a double Poisson process (LetItRain) that does not reproduce the input daily values but rather generates time series with consistent rainfall statistics. Both approaches aim to reproduce observed precipitation statistics over different timescales. The developed models were validated using 10 min radar data representing 10 climate stations in Germany and South Korea; thus, they cover various climate zones and precipitation systems. Various statistics were compared, including the mean, variance, autocorrelation, transition probabilities, and proportion of wet period. Additionally, extremes were examined, including the frequencies of different thresholds, extreme quantiles, and annual maxima. To account for the model uncertainties, 1000-year-equivalent ensembles were generated by both models for each study site. While both models successfully reproduced the observed statistics, WayDown was better (than LetItRain) at reproducing the ensemble median, showing strength with respect to precisely refining the coarse input data. In contrast, LetItRain produced rainfall with a greater ensemble variability, thereby capturing a variety of scenarios that may happen in reality. Both methods reproduced extremes in a similar manner: overestimation until a certain threshold of rainfall and underestimation thereafter. Finally, the models were applied to climate projection data. The change factors for various statistics and extremes were computed and compared between historical (radar) information and the climate projections at a daily and 10 min scale. Both methods showed similar results for the respective stations and Representative Concentration Pathway (RCP) scenarios. Several consistent trends, jointly confirmed by disaggregated and daily data, were found for the mean, variance, autocorrelation, and proportion of wet periods. Further, they presented similar behaviour with respect to annual maxima for the majority of the stations for both RCP scenarios in comparison to the daily scale (i.e. a similar systematic underestimation). [ABSTRACT FROM AUTHOR]

This study utilizes Ground Penetrating Radar (GPR) data to examine the disturbance of subsurface structure, which had been caused by liquefaction resulting from the magnitude 5.4 earthquake on 15 November 2017, South Korea. Soil liquefaction was observed for the first time in Korea. Liquefaction occurs when saturated sandy soil is subjected to a large shock like earthquake, which rearranges sand particles and results in excessive pressure of pore water. At this time, the effective stress and shear resistance of the soil layers become diminished and hence the ground behaves like a liquid. During liquefaction, therefore, the secondary damage like ground subsidence or building collapse can follow. We collected the GPR data using the pulseEKKO PRO 100 MHz GPR system, 27 days after the Pohang earthquake. The GPR survey was designed as a grid consisted of three 400 m long east-west lines with 50 m spacing and five 100 m long north-south lines with 100 m spacing. The measurement locations were determined from the GPS readings. For the post-processing, we applied a de-wow filter to remove low-frequency noise, bandpass filter to remove the frequency contents outside the main frequency range of the used antennas, and Spreading and Exponential Calibrated Compensation (SEC2) filter to compensate for the damping effect of waveform with respect to time. However, the near-surface geology of the study area consists of a mud-dominant layer at the top and sand-dominant layer at the bottom. Thus, the GPR penetration was not deep enough to image the sand-dominant layers (> 3 m depth), which appear to be the source of sand volcano observed along the surface. Nonetheless, the GPR data could visualize the evidence of the soil liquefaction from the disturbed upper silty mud layer characterized by a sharp discontinuity in the substrate. In order to enhance the subsurface image, we apply a Double-Sided Sliding Paraboloid (DSSP) technique to remove background noise and correct the given data for DC-shift and wow noise. Here we present the preliminary results from the GPR data collected at the earthquake-induced liquefaction site, which can be improved by advanced signal processing techniques. [ABSTRACT FROM AUTHOR]

On 28 February 2018, 57 mm of precipitation associated with a warm conveyor belt (WCB) fell within 21 h over South Korea. To investigate how the large-scale circulation influenced the microphysics of this intense precipitation event, we used radar measurements, snowflake photographs and radiosounding data from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018). The WCB was identified with trajectories computed with analysis wind fields from the Integrated Forecast System global atmospheric model. The WCB was collocated with a zone of enhanced wind speed of up to 45 ms-1 at 6500 m a.s.l., as measured by a radiosonde and a Doppler radar. Supercooled liquid water (SLW) with concentrations exceeding 0.2 gkg-1 was produced during the rapid ascent within the WCB. During the most intense precipitation period, vertical profiles of polarimetric radar variables show a peak and subsequent decrease in differential reflectivity as aggregation starts. Below the peak in differential reflectivity, the specific differential phase shift continues to increase, indicating early riming of oblate crystals and secondary ice generation. We hypothesise that the SLW produced in the WCB led to intense riming. Moreover, embedded updraughts in the WCB and turbulence at its lower boundary enhanced aggregation by increasing the probability of collisions between particles. This suggests that both aggregation and riming occurred prominently in this WCB. This case study shows how the large-scale atmospheric flow of a WCB provides ideal conditions for rapid precipitation growth involving SLW production, riming and aggregation. Future microphysical studies should also investigate the synoptic conditions to understand how observed processes in clouds are related to large-scale circulation. [ABSTRACT FROM AUTHOR]

Recently, a small rain radar in the 24GHz band is under development in Korea. This instrument is intended for the measurement of quantitative rainfall within a radius of 1 Km. Quantitative rainfall measurements for small watersheds are predicated on flood forecasting. The purpose of this device development was focused on the amount of rainfall reaching the ground rather than the amount of rainfall in the atmosphere. The device is currently being developed as a prototype and is undergoing various performance verification with the aim of commercialization.In this presentation, we show the comparison result of performance verification of small rain radar and show the performance superiority. In order to verify the performance of this device, it took a long time from the selection of the comparative verification site to the design and installation of a pit gauge. The comparative site was equipped with 2 parsivels, one pit gauge(composed of one weighing type of rain gauge and 12 tipping bucket type rain gauges) and 17 tipping bucket type gauges. The comparative analysis method of the measurement result converts the scanning result of this device into the reflectance and compares it with the verification device. [ABSTRACT FROM AUTHOR]

This study estimated the Flood-Inducing-Rainfall (FIR) and the Flood-Inducing-Runoff (FIRO) to be used as the basis of the flood warning for the highly urbanized area of Seoul, Korea. For this, we derived the depth-duration relationship of rainfall and runoff for the 261 flood events during 2010 and 2011 based on the 10-minute 1km-1km radar-gauge composite rainfall field and flooded area GIS data. We found that FIR and FIRO vary at the range of 37mm/hr-63mm/hr and the range of 10mm/hr-42mm/hr, respectively. The large variability residing in the FIR and FIRO was well explained by the flooded area proportion in the watershed (FP) and the rainfall coefficient of variation (CV): As the FP increases, FIR and FIRO increased too, suggesting that the greater rainfall leads to greater flooded area; as the rainfall CV increases, FIR and FIRO decreased suggesting that the temporally concentrated rainfall requires less total rainfall to cause flooding. The 21 flood events that occurred for the period of 2012 through 2015 at the study area was used for the validation of the derived FIR and FIRO. When the 5 percent of the flooded area can be tolerated, the ratio of hit and miss of the warning system based on the rainfall was 44.2 percent and 9.5 percent, respectively. The ratio of hit and miss of the warning system based on the runoff was 67 percent and 4.7 percent, respectively. Lastly, we show the importance of considering the radar-gauge composite rainfall data as well as rainfall and temporal variability in flood warning by comparing our results to the ones based on the gauge-only or radar-only rainfall data and to the one that does not consider the temporal variability.AcknowledgementThis research was supported by a grant(MOIS-DP-2015-05) of Disaster Prediction and Mitigation Technology Development Program funded by Ministry of Interior and Safety(MOIS, Korea).This study was also supported by the Basic Research Laboratory Program through the NRF of Korea funded by the Ministry of Education. [ABSTRACT FROM AUTHOR]

To understand the different environment and morphology for heavy rainfall during 9-10 July 2007, over the Korean Peninsula, mesoscale convective systems (MCSs) that accompanied the Changma front in two different regions were investigated. The sub-synoptic conditions were analysed using mesoscale analysis data (MANAL), reanalysis data, weather charts and Multi-functional Transport Satellite (MTSAT-IR) data. Dual-Doppler radar observations were used to analyse the wind fields within the precipitation systems. During both the case periods, the surface lowpressure field intensified and moved northeastward along the Changma front. A low-level warm front gradually formed with an east-west orientation, and the cold front near the low pressure was aligned from northeast to southwest. The northern convective systems (meso-α-scale) were embedded within an area of stratiform cloud north of the warm front. The development of low-level pressure resulted in horizontal and vertical wind shear due to cyclonic circulation. The wind direction was apparently different across the warm front. In addition, the southeasterly flow (below 4 km) played an important role in generating new convective cells behind the prevailing convective cell. Each isolated southern convective cell (meso-β-scale) moved along the line ahead of the cold front within the prefrontal warm sector. These convective cells developed when a strong southwesterly low-level jet (LLJ) intensified and moisture was deeply advected into the sloping frontal zone. A high equivalent potential temperature region transported warm moist air in a strong southwesterly flow, where the convectively unstable air led to updraft and downdraft with a strong reflectivity core [ABSTRACT FROM AUTHOR]

In the past, Aerial cables were indiscriminately installed due to rapid urban development in South Korea. As a result, aerial cables in old downtown areas had negative impacts on urban fine view and frequently hindered the safety of pedestrians. In particular, damaged telegraph poles or fallen of aerial cables due to natural disasters such as typhoons, earthquakes, and torrential rains caused a great amount of danger to the safety of citizens. In recent years, the South Korean Government has established and executed the "Total Plan of Aerial cables Arrangement" as a government policy project to resolve the problems associated with overcrowded aerial cables. However, the Road Act in Korea states that when electric and telecommunication cables are buried, 1.2m of excavation width and 0.8m of excavation depth should be arranged. Due to this compliance the cost of underground work for aerial cable increases and underground work has not progressed as expected. To resolve this problem, a micro-trenching method that has been applied in the cables laying at a low depth in overseas has been introduced to Korea. When the micro-trenching method is applied, more than 70% of the cost can be reduced as compared to that of existing laying method of electric and telecommunication cables, which will be a significant help to the aerial cables arrangement plan. So, the studies have been conducted on the development of safe backfill materials for trenching sections and the evaluations on the resistance against ground vehicle loads considering the Korean geological features have been conducted as well. When the micro-trenching method is applied, the first thing to do is accurately survey underground utilities in the construction area. the most important step in the micro-trenching is the design of the optimal route, where trenching can be possible after accurate survey for underground utilities. This study used GPR(Ground Penetrating Radar) in the survey, which has been widely utilized in underground cavity surveys in Korea recently, and has been investigated as a measure to survey underground utilities accurately in the trenching area. To do this, the GPR applicability was investigated through the analyses on current technology and application cases in Korea and overseas. GPR exploratios with various frequencies were conducted to find arbitrarily laid cables in the test-bed site, and the survey accuracy by frequency and the effective analysis depth were evaluated in this study. In addition, this study performed GPR surveys in old downtown roads that existing aerial cables were present, thereby investigating underground utilities in various environments and conditions. The underground utilities were surveyed by using vehicle-assembled multi-channel GPR equipment with various frequencies and the results were compared with existing underground utilities map of the government department. The micro-trenching applied in Korea is planned to have 0.3m maximum excavation depth and 0.3m maximum excavation width. In this study, the optimal path and design of micro-trenching were proposed by the GPR survey results considering the micro-trenching characteristics and underground situation in Korea. [ABSTRACT FROM AUTHOR]

Snowfall in the northeastern part of South Korea is the result of complex snowfall mechanisms due to a highly contrasting terrain combined with nearby warm waters and three synoptic pressure patterns. All these factors together create unique combinations, whose disentangling can provide new insights into the microphysics of snow on the planet. This study focuses on the impact of wind flow and topography on the microphysics drawing of 20 snowfall events during the ICE-POP 2018 (International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic winter games) field campaign in the Gangwon region. The vertical structure of precipitation and size distribution characteristics are investigated with collocated MRR (micro rain radar) and PARSIVEL (particle size velocity) disdrometers installed across the mountain range. The results indicate that wind shear and embedded turbulence were the cause of the riming process dominating the mountainous region. As the strength of these processes weakens from the mountainous region to the coastal region, riming became less significant and gave way to aggregation. This study specifically analyzes the microphysical characteristics under three major synoptic patterns: air–sea interaction, cold low, and warm low. Air–sea interaction pattern is characterized by more frequent snowfall and vertically deeper precipitation systems on the windward side, resulting in significant aggregation in the coastal region, with riming featuring as a primary growth mechanism in both mountainous and coastal regions. The cold-low pattern is characterized by a higher snowfall rate and vertically deep systems in the mountainous region, with the precipitation system becoming shallower in the coastal region and strong turbulence being found in the layer below 2 km in the mountainous upstream region (linked with dominant aggregation). The warm-low pattern features the deepest system: precipitation here is enhanced by the seeder–feeder mechanism with two different precipitation systems divided by the transition zone (easterly below and westerly above). Overall, it is found that strong shear and turbulence in the transition zone is a likely reason for the dominant riming process in the mountainous region, with aggregation being important in both mountainous and coastal regions. [ABSTRACT FROM AUTHOR]

The WISSDOM (Wind Synthesis System using Doppler Measurements) synthesis scheme was developed to derive high-resolution 3-dimensional (3D) winds under clear-air conditions. From this variational-based scheme, detailed wind information was obtained from scanning Doppler lidars, automatic weather stations (AWSs), sounding observations, and local reanalysis datasets (LDAPS, Local Data Assimilation and Prediction System), which were utilized as constraints to minimize the cost function. The objective of this study is to evaluate the performance and accuracy of derived 3D winds from this modified scheme. A strong wind event was selected to demonstrate its performance over complex terrain in Pyeongchang, South Korea. The size of the test domain is 12×12 km 2 extended up to 3 km a.m.s.l. (above mean sea level) height with a remarkably high horizontal and vertical resolution of 50 m. The derived winds reveal that reasonable patterns were explored from a control run, as they have significant similarity with the sounding observations. The results of intercomparisons show that the correlation coefficients between derived horizontal winds and sounding observations are 0.97 and 0.87 for u - and v -component winds, respectively, and the averaged bias (root mean square deviation, RMSD) of horizontal winds is between -0.78 and 0.09 (1.77 and 1.65) m s -1. The correlation coefficients between WISSDOM-derived winds and lidar QVP (quasi-vertical profile) are 0.84 and 0.35 for u - and v -component winds, respectively, and the averaged bias (RMSD) of horizontal winds is between 2.83 and 2.26 (3.69 and 2.92) m s -1. The statistical errors also reveal a satisfying performance of the retrieved 3D winds; the median values of wind directions are -5 to 5 (0 to 2.5) ∘ , the wind speed is approximately -1 to 3 m s -1 (-1 to 0.5 m s -1), and the vertical velocity is -0.2 to 0.6 m s -1 compared with the lidar QVP (sounding observations). A series of sensitivity tests with different weighting coefficients, radius of influence (RI) in interpolation, and various combination of different datasets were also performed. The results indicate that the present setting of the control run is the optimal reference to WISSDOM synthesis in this event and will help verify the impacts against various scenarios and observational references in this area. [ABSTRACT FROM AUTHOR]

\begin{document}In this study we investigate the microphysics of an intense snowfall event during the ICE-POP 2018 campaign, which took place in South Korea to support the PyeongChang2018 Winter Games. We aim to show how the dynamics and microphysics interacted in this extreme event. An X-band polarimetric scanning radar was deployed in Gangneung at sea level, while a W-band cloud profiler and a Multi-Angle Snowflake Camera (MASC) were installed in PyeongChang at 680 m asl. \parOn 28 February 2018, 57 mm of equivalent liquid precipitation were measured in 24 hours. It represents 77\% of the 2018 winter precipitation in PyeongChang, showing the significance one single event can have on the seasonal precipitation in this region. A potential vorticity streamer led to the intensification of an existing surface cyclone, such that an active warm front reached the PyeongChang region. Doppler radar measurements suggest the presence of a warm conveyor belt (WCB) and a low level jet (LLJ) with altitude and speed that agree with radio-soundings. Profiles of polarimetric radar variables reveal an increase in differential reflectivity ($Z_{DR}$) at 5500 m altitude just below the WCB. We interpret it as anisotropic growth of crystals by vapor deposition and early aggregation. Hydrometeor classification based on polarimetric data shows an increase in the proportion of rimed particles just below 4800 m where riming sets on. The LLJ is bounded by a shear layer, which separates it from the WCB. In this shear layer a peak in specific differential phase shift ($K_{dp}$) collocated with a maximum in proportion of rimed particles is observed at 2500 m. We advocate that this peak in $K_{dp}$ is due to secondary ice generation by the Hallet-Mossop process. \par This case study reveals the role of the WCB, the LLJ and its associated wind shear in snowfall enhancement by aggregation and riming. This shows the importance of the interactions between dynamics and microphysics in the considered snowfall event. \end{document} [ABSTRACT FROM AUTHOR]

Microwave radiometers are widely used for the retrieval of liquid water path (LWP) and integrated water vapor (IWV) in the context of cloud and precipitation studies. This paper presents a new site-independent retrieval algorithm for LWP and IWV, relying on a single-frequency 89 GHz ground-based radiometer. A statistical approach is used based on a neural network, which is trained and tested on a synthetic dataset constructed from radiosonde profiles worldwide. In addition to 89 GHz brightness temperature, the input features include surface measurements of temperature, pressure, and humidity, as well as geographical information and, when available, estimates of IWV and LWP from reanalysis data. An analysis of the algorithm is presented to assess its accuracy, the impact of the various input features, its sensitivity to radiometer calibration, and its stability across geographical locations. While 89 GHz brightness temperature is crucial to LWP retrieval, it only moderately contributes to IWV estimation, which is more constrained by the additional input features. The algorithm is shown to be quite robust, although its accuracy is inevitably lower than that obtained with state-of-the-art multi-channel radiometers, with a relative error of 18 % for LWP (in cloudy cases with LWP >30 g m -2) and 6.5 % for IWV. The highest accuracy is obtained in midlatitude environments with a moderately moist climate, which are more represented in the training dataset. The new method is then implemented and evaluated on real data that were collected during a field deployment in Switzerland and during the ICE-POP 2018 campaign in South Korea. [ABSTRACT FROM AUTHOR]

An extreme-rainfall-producing mesoscale convective system (MCS) associated with the Changma front in southeastern South Korea was investigated using observational data. This event recorded historic rainfall and led to devastating flash floods and landslides in the Busan metropolitan area on 7 July 2009. The aim of the present study is to analyse the influences for the synoptic and mesoscale environment, and the reasons that the quasi-stationary MCS causes extreme rainfall. Synoptic and mesoscale analyses indicate that the MCS and heavy rainfall occurred in association with a stationary front which resembled a warm front in structure. A strong southwesterly low-level jet (LLJ) transported warm and humid air and supplied the moisture toward the front, and the air rose upwards above the frontal surface. As the moist air was conditionally unstable, repeated upstream initiation of deep convection by back-building occurred at the coastline, while old cells moved downstream parallel to the convective line with training effect. Because the motion of convective cells nearly opposed the backward propagation, the system as a whole moved slowly. The back-building behaviour was linked to the convectively generated cold pool and its outflow boundary, which played a role in the propagation and maintenance of the rainfall system. As a result, the quasi-stationary MCS caused a prolonged duration of heavy rainfall, leading to extreme rainfall over the Busan metropolitan area. [ABSTRACT FROM AUTHOR]

One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating the subgrid-scale physical processes. For more accurate regional weather and climate prediction by improving physics parameterizations, it is important to optimize a combination of physics schemes and unknown parameters in NWP models. We have developed an interface system between a micro-genetic algorithm (µ -GA) and the WRF model for the combinatorial optimization of cumulus (CU), microphysics (MP), and planetary boundary layer (PBL) schemes in terms of quantitative precipitation forecast for heavy rainfall events in Korea. The µ -GA successfully improved simulated precipitation despite the nonlinear relationship among the physics schemes. During the evolution process, MP schemes control grid-resolving-scale precipitation, while CU and PBL schemes determine subgrid-scale precipitation. This study demonstrates that the combinatorial optimization of physics schemes in the WRF model is one possible solution to enhance the forecast skill of precipitation. [ABSTRACT FROM AUTHOR]

Accurate boundary layer structure and height are critical in the analysis of the features of air pollutants and local circulation. Although surface-based remote sensing instruments provide a high temporal resolution of the boundary layer structure, there are numerous uncertainties in terms of the accurate determination of the atmospheric boundary layer heights (ABLHs). In this study, an algorithm for an integrated system for ABLH estimation (ISABLE) was developed and applied to the vertical profile data obtained using a ceilometer and a microwave radiometer in Seoul city, Korea. A maximum of 19 ABLHs were estimated via the conventional time-variance, gradient, wavelet, and clustering methods using the backscatter coefficient from the ceilometer. Meanwhile, several stable boundary layer heights were extracted through near-surface inversion and environmental lapse rate methods using the potential temperature from the microwave radiometer. The ISABLE algorithm can find an optimal ABLH from post-processing, such as k -means clustering and density-based spatial clustering of applications with noise (DBSCAN) techniques. It was found that the ABLH determined using ISABLE exhibited more significant correlation coefficients and smaller mean bias and root mean square error between the radiosonde-derived ABLHs than those obtained using the most conventional methods. Clear skies exhibited higher daytime ABLH than cloudy skies, and the daily maximum ABLH was recorded in summer because of the more intense radiation. The ABLHs estimated by ISABLE are expected to contribute to the parameterization of vertical diffusion in the atmospheric boundary layer. [ABSTRACT FROM AUTHOR]

The emergence of innovative technologies in the museum environment has influenced the development of guide application tools for personalization, greatly contributing to the enhancement of collection accessibility and enriching the museum experience. Most Korean museums have thus developed strategic objectives for the application of evolved technological advances in their guide applications over the past decade. Beyond such endeavors, the critical analysis and understanding of technical aspects, the information and interactive approach of the applications, and their personalization strategies, are still not broadly explored. This study aims to investigate the various types of technologies that museums use to envisage the personalization of museums exhibits in light of a range of classifications and their correlations. In addition to providing an overview of personalization, the paper aims to access the level and degree of user experience and engagement found in different forms of digital storytelling to investigate the realization of virtual museum(VM) within the museum sector of Korea. To this end, this paper contributes toward filling the gap in the literature by addressing how museum communities can do critical thinking on the reflection of resources when creating their application guides in developing the appropriate digital strategies. [ABSTRACT FROM AUTHOR]

The impact of regional emissions (e.g., domestic and international) on surface particulate matter (PM) concentrations in the Seoul metropolitan area (SMA), South Korea, and its sensitivities to meteorology and emissions inventories are quantitatively estimated for 2014 using regional air quality modeling systems. Located on the downwind side of strong sources of anthropogenic emissions, South Korea bears the full impact of the regional transport of pollutants and their precursors. However, the impact of foreign emissions sources has not yet been fully documented.We utilized two regional air quality simulation systems: (1) a Weather Research and Forecasting and Community Multi-Scale Air Quality (CMAQ) system and (2) a United Kingdom Met Office Unified Model and CMAQ system. The following combinations of emissions inventories are used: the Intercontinental Chemical Transport Experiment-Phase B, the Intercomparison Study for Asia 2010, and the National Institute of Environment Research Clean Air Policy Support System. Partial contributions of domestic and foreign emissions are estimated using a brute force approach, adjusting South Korean emissions to 50 %. Results show that foreign emissions contributed ~60% of SMA surface PM concentration in 2014. Estimated contributions display clear seasonal variation, with foreign emissions having a higher impact during the cold season (fall to spring), reaching ~70% in March, and making lower contributions in the summer, ~45% in September. We also found that simulated surface PM concentration is sensitive to meteorology, but estimated contributions are mostly consistent. Regional contributions are also found to be sensitive to the choice of emissions inventories. [ABSTRACT FROM AUTHOR]

The ground-based microwave sounding radiometers installed at 9 weather stations of Korea Meteorological Administration alongside with the wind profilers have been operated for more than 4 years. Here we introduce a process to assess the characteristics of the instrument calibration by comparing the measured brightness temperature (Tb) with the theoretical reference data, which are prepared by the radiative transfer simulation with the temperature and humidity profiles from the numerical weather prediction model. Based on the three years of data, from 2010 to 2012, we were able to characterize the effects of the absolute calibration, the thick clouds, and the frequency calibration to the quality of the measured Tb. When the three effects are properly considered, including the frequency adjustment which is estimated using the simulated Tb, the measured and simulated Tb show an excellent agreement. The regression coefficients are better than 0.97 along with the bias value of better than 0.5 K. However, the variability given as the SD of difference between the measured and simulated Tb, show a relatively large value at the lower observation frequencies, as large as 2.6 K at the 51.28 GHz channel, while they improve with the increasing frequency. [ABSTRACT FROM AUTHOR]