Stations In South Korea Research Articles

Characteristics of STILT footprints driven by KIM model simulated meteorological fields: implication for developing near real-time footprints

This study presents an analysis of the atmospheric footprint sensitivities and CO2 enhancements measured at three in situ stations in South Korea (Anmyeondo (AMY), Gosan (JGS), Ulleungdo (ULD)) using the KIM-STILT and WRF-STILT atmospheric transport models. Monthly aggregated footprints for each station were compared between the models for July and December 2020. The footprints revealed major source regions and the sensitivity of atmospheric mole fractions at the receptor to upstream surface fluxes. In July, both models showed similar major source regions for the AMY station, including Korea, the Yellow Sea, and Japan. However, a discrepancy was observed in the Eastern Pacific Ocean, with KIM-STILT showing larger sensitivity compared to WRF-STILT. In December, both models indicated strong sensitivity over Northeast and Eastern China, but KIM-STILT exhibited smaller sensitivities towards Northwestern China and Mongolia compared to WRF-STILT. At station ULD in July, both models exhibited comparable source regions, but a notable difference was found in Southeast China, where KIM-STILT showed stronger sensitivity. For the JGS station, both models agreed on major sources, but WRF-STILT demonstrated stronger sensitivity over North and Northeastern China. Regarding CO2 enhancements, both models generally underestimated the amplitude of CO2 enhancements, especially in July. However, in December, there was better agreement with observed data. The models were able to reproduce the phase of measured ΔCO2 reasonably well despite the underestimation of CO2 amplitudes. The contribution of biospheric CO2 to the observed enhancements, along with fossil-fuel emissions, was highlighted. In specific cases with significant CO2 enhancements, the models provided varying estimates of CO2ff values, particularly in the source regions of Eastern China. The differences in sensitivity estimations emphasize the need for further investigation to understand the underlying factors causing disparities. Overall, this study provides valuable insights into the potential advantages of each model in capturing dispersion patterns in specific regions, highlighting the importance of understanding these differences to improve the accuracy of atmospheric transport models. Further work is necessary to address the observed disparities and enhance our understanding of the transport models in the studied regions.

Prediction of Gasoline Orders at Gas Stations in South Korea Using VAE-Based Machine Learning Model to Address Data Asymmetry

South Korea has developed road-based transportation and uses a lot of gasoline. South Korea imports gasoline since it is not produced domestically. So, fluctuations in gasoline prices have a significant impact on the national economy. Currently, gasoline orders, which are based on gasoline consumption, are analyzed in relation to fluctuations in gasoline prices. However, gasoline orders can also change due to various non-price factors. Therefore, to understand the trend of gasoline orders, it is important to identify additional factors that gas stations consider when determining orders. We collected 180 monthly samples of data on 167 variables. Sudden international issues lead to rapid fluctuations in gasoline orders, which can lead to outliers. A class imbalance occurs because outliers are generally fewer in number than the normal data points. Therefore, to address the class imbalance, we proposed a method that grouped the data samples into 11 clusters using the K-means clustering algorithm and then augmented the data into 85 datasets in each cluster through the Variational Auto-Encoder. We evaluated the augmented datasets through the R-Squared, Root Mean Squared Errors, and accuracy of various regression models. Based on the experimental results, when predicting gasoline orders at gas stations in South Korea using augmented datasets, linear regression showed the best performance.

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle to improving the accuracy and efficiency of hydrological analysis. To address this issue, we developed a machine learning algorithm-based precipitation data recovery tool to detect and predict missing precipitation data at observatories. This study investigated 30 weather stations in South Korea, evaluating the applicability of machine learning algorithms (artificial neural network and random forest) for precipitation data recovery using environmental variables, such as air pressure, temperature, humidity, and wind speed. The proposed model showed a high performance in detecting the missing precipitation occurrence with an accuracy of 80%. In addition, the prediction results from the models showed predictive ability with a correlation coefficient ranging from 0.5 to 0.7 and R2 values of 0.53. Although both algorithms performed similarly in estimating precipitation, ANN performed slightly better. Based on the results of this study, we expect that the machine learning algorithms can contribute to improving hydrological modeling performance by recovering missing precipitation data at observation stations.

수소충전소 설비운영 안전관리 상용화를 위한 CBM+활용 방안 연구

Purpose: This study aims to meet the functional safety requirements using condition-based maintenance plus (CBM+) to ensure the safe operation of equipment, such as chillers, compressors, and dispensers, used in hydrogen refueling facilities. BRMethods: For the commercialization of the safety management of hydrogen refueling stations, we demonstrate an approach for detecting equipment abnormalities during operation by measuring sound, vibration, and current. CBM+ is used for fault diagnosis; the results are compared with those of the traditional fault diagnosis methods.BRResults: At hydrogen refueling stations in South Korea, 90.48% of accidents occurred due to hydrogen leakage from 2005 to 2014. During the same period, 7 (31.82%) out of 22 such accidents were caused by human errors in the United States. Through structural analysis of vertical axis, lateral sides, and height of the hydrogen charging stations, we propose a method that uses sensor nodes, sensor systems, and communication systems for CBM+.BRConclusion: Only a few empirical studies have reported the use of CBM+ for hydrogen refueling stations in South Korea. Effective strategies for CBM+ have also not been presented. Through technical research and institutional improvement, including this study, it is necessary to secure functional safety and PHM technology for CBM for the safe operation of hydrogen refueling stations in South Korea. In addition, data based on several factors, including the equipment component type, failure, and safety risk level, must be acquired in a standard format. However, to eliminate the safety risks of hydrogen refueling stations, hydrogen leakage accidents must be primarily avoided.

Development of a surrogate model to improve the efficiency of groundwater level fluctuation pattern-based hydrologic properties evaluation

In this study, a method of aquifer hydrologic property estimation incorporating the deep learning method was developed to improve the estimation efficiency of a process-based model based on groundwater level fluctuation (GLF) patterns. As a reference study, a data-driven method suggested by Jeong et al. (2020) was considered; the uncertainty of the GLF patterns resulting from different yearly patterns of precipitation, which were considered as noise in the previous study, was effectively discarded using the newly proposed method of applying the conditional variational autoencoder (CVAE). The CVAE was used to acquire the specific GLF patterns under certain identical precipitation patterns for all the monitoring stations. The data-driven hydrologic property estimation model was developed to predict two hydrologic parameters (ρ and k) of the process-based model using the generated GLF patterns from the CVAE network as the input variables. The actual GLF and precipitation data that were acquired from nationwide groundwater monitoring stations in South Korea were applied to validate the developed method. It was found that the estimated and target hydrologic properties were highly correlated (correlation coefficients [CC]: 0.9833 and 0.9589 for ρ and k, respectively), which significantly improved the results when compared to the previous study (CC: 0.7207 and 0.8663 for α/n and k, respectively). Consequently, the developed model can contribute to a more accurate hydrologic property estimation of aquifers. Additionally, it can facilitate efficient groundwater development planning since the manual fitting of the process-based model by an expert is not required.

Improved seasonal definition and projected future seasons in South Korea

AbstractSeasons are fundamental climatological phenomena, and their changes have significantly affected the sustainability of nature and human society. Suitable criteria to define the seasonal indicators of start date and length of seasons are required for the timely planning of socio‐economic activities. However, unsuitable cases of seasonal indicators have been identified in the seasonal definition used by Korea Meteorological Administration (KMA). We devised the data processing method to calculate three seasonal indicators: start date, length, and abnormal days of seasons. The past (1921–2010) and future (2021–2100) climate data of daily mean temperature were used in estimating the three seasonal indicators; they were obtained from the weather stations in South Korea and under the Representative Concentration Pathway (RCP) scenarios, respectively. The modified definitions with the moving averages of 15‐, 31‐, 45‐, 61‐, and 91‐day improved in estimating the start date and length of seasons compared with the current KMA definition. Additionally, abnormal days of seasons in the modified definitions were found to reduce significantly. Under the RCP8.5 scenario, the earlier advance of spring and summer by 4.9 and 3.9 days per decade, respectively, and the delayed autumn and winter by 3.2 and 2.0 days per decade, respectively, were expected over the 21st century. Correspondingly, the extended summer and the shortened winter by 7.1 days per decade and 5.1 days per decade were projected, respectively. The estimated future changes in seasons using the improved seasonal definition can be used for socio‐economic sectors and the general public to adapt to a warmer climate.

A multivariate Chain-Bernoulli-based prediction model for cyanobacteria algal blooms at multiple stations in South Korea

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data

• A data-driven ensemble time-series data estimation model is developed. • Statistical variability and the exogenous factor are considered in the estimation. • The range of normal data is calculated based on the ensemble estimation result. • Contextual and collective outliers in time-series data were effectively identified. In this study, a method to estimate the normal range of groundwater level time-series data was developed to identify outliers in terms of the global, contextual, and collective sense. To evaluate the normal range of groundwater level time-series data, the statistical characteristics of the data and the patterns of the precipitation time-series data were incorporated into the LSTM (Long Short-Term Memory)-based ensemble regressor (i.e., the LER model). Based on the LER model, multiple possible trends of the groundwater level were generated, and the general rules of outlier identification methods (i.e., σ and Tukey’s fences (TF) rules) were applied to the LER ensemble estimation result to finally define the range of the normal data. For outlier identification performance validation, the actual groundwater level acquired from three groundwater monitoring stations in South Korea (i.e., the Pohang–Gibuk (PG), Namwon–Dotong (ND), and Jeju–Sangyae (JS) monitoring wells) and the corresponding precipitation data acquired from the nearest weather stations were applied to the study. As the reference method for comparative performance validation, simple applications of the σ and TF rules were used. For the monitoring data, the developed LER-based outlier identification method evaluates the range of the data that might be explained by the modelled influences of the interest (i.e., normal data range). The developed method showed an outlier identification performance of >70% in general while the performance of the σ and TF rules was mostly <50%. In particular, as the method effectively estimated the seasonal trend and the variability of the groundwater level with consideration of the precipitation patterns and statistics on the groundwater level variation, it is superior for identifying the contextual or collective outliers compared to the simple σ and TF rules. Through in-depth analysis, it can be concluded that the developed LER-based outlier identification method is effective for discriminating the abnormal data by considering the intrinsic statistical characteristics of the original data trend and the exogenous factors. In the aspect of the practical applicability, as the result can be automatically acquired based on real-time monitoring data, the developed method is expected to apply for more efficient maintenance of the monitoring devices by embedding the model as the management software into the monitoring network system.

Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea

This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985–2014). The changes and uncertainty associated with the changes in PET in the near (2031–2060) and far (2071–2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2–4.5 and 5–8.5. As a result, PETs estimated using the three methods for the historical period showed high performance in terms of five evaluation metrics. Overall, PETs showed an increase for both the future periods and the SSPs. The PET estimated using the PM method showed the greatest increase, while that estimated using HS showed the most modest increase in the future. The PM method also showed the highest reliability and lowest uncertainty in the PET estimations, while the opposite was true for HS. This study contributes to our understanding of rational PET methods by which to calculate hydrological factors such as drought indexes for future periods via GCM simulations.

Spatiotemporal Rainfall Variability and Drought Assessment during Past Five Decades in South Korea Using SPI and SPEI

About 41% of the earth is drought-affected, which has impacted nearly 2 billion people, and it is expected that more than 90% of terrestrial areas will be degraded by 2050. To evade and mitigate the harmful impacts of drought, it is necessary to study the rainfall variability and assess the drought trend at a global and regional level. This study utilized 70 meteorological stations in South Korea to evaluate the rainfall variability, drought, and its trend during the past five decades using the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI). Rainfall data normality was assessed with mean, standard deviation, skewness, and kurtosis. The highest amount of rainfall was observed in the months of June, July, and August. The SPI and SPEI 12-month results revealed that 1982, 1988, 2008, 2015, and 2017 were dry years throughout the country, while from 2013 to 2017 mixed drought events were observed for the 6-month time series. The Mann-Kendall trend test was applied to the 1- and 12-month time series, and the results revealed that the months of January, March, April, May, June, and August had a significant negative trend, which means drought is increasing in these months, while the months of September, October, and December had a significant positive trend, which means wetter conditions prevailed in these months during the study period. It was observed in the 12-month time series that only two met stations had a significant negative trend, while only one had a significant positive trend. It was found that January and March were the driest months, and October was the wettest month. The detected drought events in this research are consistent with ENSO events. We have observed differences in drought characteristics (duration and frequency) for both indices. Climatic data revealed that South Korea has faced drought conditions (rainfall deficit) due to a shortened monsoon season. This study can provide guidance on water management strategies under the changing pattern of drought in South Korea.

The effects of price competition on firms' operations and market price: Evidence from a retail gasoline market

We study the effects of price competition induced by entries of thrifty gas stations on incumbent gas stations' operational probabilities and gasoline prices. Using administrative panel data of the entire population of retail gas stations in South Korea, we document that entries of thrifty gas stations decreased incumbent gas stations' operational probability but marginally reduced gasoline prices. Our heterogeneity analysis indicates that the operational probability impacts were greater when the market competition was already intense or when the pre-entry profit margins were low. The previous studies mainly focused only on price impacts of market competition. This study provides novel evidence that price competition might not always decrease market prices if incumbent firms stop operating their business. The results imply that a more complete understanding of the effects of price competition can be possible by jointly estimating its impacts on incumbent firms' operational probabilities and product prices.

Undersampled Ionospheric Irregularity Threat Parameterization Using a Three-Dimensional Model for Satellite-Based Augmentation Systems

Single-frequency Satellite-based Augmentation Systems (SBASs) compute and broadcast estimates of vertical ionospheric delays and integrity bounds on the estimates called the Grid Ionospheric Vertical Errors (GIVEs) at Ionospheric Grid Points (IGPs). The dominant contribution of the GIVE comes from the undersampled ionospheric irregularity threat model. This paper presents a methodology for the undersampled ionospheric threat parameterization to reduce the magnitude of the GIVE. A threat model metric that measures the uniformity of angular separation of measurements was designed and incorporated into the current metric set, the fit radius and the relative centroid metric (RCM), such that threat geometries were parameterized rigorously based on the extended metric set. An undersampled threat model was constructed with the proposed three-dimensional metric set and historical ionospheric storm data from the global navigation satellite system stations in South Korea. We also simulated SBAS availability in the Korean region to demonstrate the benefit of the presented threat model methodology. In our preliminary assessment, the implementation of the proposed method was found to improve the 99.9&#x0025; availability of the approach procedure with vertical guidance I by up to 12&#x0025; when the approach was applied to the GIVE monitor algorithm.

A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions With Sparse Ground Data Networks

AbstractThis study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regional climate models (RCMs), coupled to the proposed QDM‐spatial bias‐correction, in which the distribution parameters are first interpolated onto a fine grid (rather than the observed daily rainfall). The proposed model is validated through a cross‐validatory (CV) evaluation using rainfall data from a set of weather stations in South Korea and climate change scenarios simulated by three alternate RCMs. The results demonstrate the efficacy of the proposed model to simulate the bias‐corrected daily rainfall sequences over large regions at fine resolutions. A discussion of the potential use of the proposed approach in the field of hydrometeorology is also offered.

Deep Learning-Based, Real-Time, False-Pick Filter for an Onsite Earthquake Early Warning (EEW) System

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

An Analysis of Spatio-temporal Characterisrice of Drought Using Non-stationary Time Dependent-standardized Precipitation

The SPI index was developed based on the fact that when drought occurs when reduced precipitation causes water shortage relative to the required water demand. However, the SPI index works under the assumption of stationarity or normality in which the probabilistic properties of the time series data do not change over time. It has limitations in reflecting information changing over time sufficiently, such as climate change. In this study, we used SPIt, a new frequency analysis method considering non-stationarity, which is different from the existing SPI index calculation. We first used daily precipitation data to construct the time series data of 7-day precipitation. For the duration of drought, both SPI and SPIt indices for 3 months were calculated on a monthly and weekly basis, and the reproducibility of each index was assessed for the areas experiencing restricted water supply. In addition, both indices were calculated for all national meteorological stations in South Korea, which was used to create a spatial distribution map and confirm their spatial reproducibility.

Impact of the precise ephemeris on accuracy of GNSS baseline in relative positioning technique

For advanced geodesy tasks that require high-accuracy, such as tectonics, surveying services usually use not only long-baselines but also the duration of tracking GNSS satellites in a long (e.g., 24/7). The accuracy of these baselines in baseline analysis is dominated by inaccuracy satellite positioning and orbit, leading to specified accuracy may not be adequate. One way to overcome this problem is to use the final precise ephemeris, provided by IGS. The objective of this study is to investigate the impact of precise ephemeris on the accuracy of GNSS baselines in relative positioning techniques in two aspects: baseline length and duration of tracking GNSS satellites. To this end, 197 baselines were generated from a total of 88 CORS stations in South Korea, and then thirteen testing cases were constructed by grouping baseline lengths from under 10 km to over 150 km. Besides, data for one day of each CORS was divided into the different duration, such as 1, 2, 3, 6, and 24 hours. The GNSS measurements have been processed by TBC software with an application of the broadcast and precise ephemerides. The precision of the baseline processing from two types of ephemeris was analyzed about baseline lengths and time of data. The obtained results showed that using precise ephemeris significantly improved the accuracy of baseline solutions when the length of the baseline larger than 50km. In addition, this accuracy is independent of the length of baselines in the case of the precise ephemeris. Finally, the result of the testing baselines was enhanced when the duration of tracking data increases.

Temperature trend analysis associated with land-cover changes using time-series data (1980–2019) from 38 weather stations in South Korea

We analyzed the relationship between changes in land cover and the time-series trend of temperature using 40 years’ worth of data (1980–2019) from 38 weather stations in South Korea to establish mitigation strategies for the rising temperature and heat island effect in urban areas. Changes in temperature were estimated by analyzing the time-series trend slope using daily mean temperature data. Stations with a high trend slope showed a mean temperature rise of 2 °C over the study period, and those with a low trend slope had a mean temperature rise of 0.5 °C. In land cover change, each study site was classified into three categories: areas with heavy urbanization, areas with few land-cover changes, and decreased rural and forest areas. A correlation analysis between the land-cover change and temperature trend slope revealed a positive correlation between urban areas and the trend slope and a negative correlation between forest areas and the trend slope. The expansion of urban areas resulting from the development of forest areas was identified as the cause of the long-term increase in the urban temperature. These results will serve as useful data in the urban and environmental planning phase for mitigating urban heat island effects.

Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data

Fog affects transportation due to low visibility and also aggravates air pollutants. Thus, accurate detection and forecasting of fog are important for the safety of transportation. In this study, we developed a decision tree type fog detection algorithm (hereinafter GK2A_FDA) using the GK2A/AMI and auxiliary data. Because of the responses of the various channels depending on the time of day and the underlying surface characteristics, several versions of the algorithm were created to account for these differences according to the solar zenith angle (day/dawn/night) and location (land/sea/coast). Numerical model data were used to distinguish the fog from low clouds. To test the detection skill of GK2A_FDA, we selected 23 fog cases that occurred in South Korea and used them to determine the threshold values (12 cases) and validate GK2A_FDA (11 cases). Fog detection results were validated using the visibility data from 280 stations in South Korea. For quantitative validation, statistical indices, such as the probability of detection (POD), false alarm ratio (FAR), bias ratio (Bias), and equitable threat score (ETS), were used. The total average POD, FAR, Bias, and ETS for training cases (validation cases) were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively. In general, validation results showed that GK2A_FDA effectively detected the fog irrespective of time and geographic location, in terms of accuracy and stability. However, its detection skill and stability were slightly dependent on geographic location and time. In general, the detection skill and stability of GK2A_FDA were found to be better on land than on coast at all times, and at night than day at any location.

Prognostic and diagnostic assessment of hydrological drought using water and energy budget-based indices

Long-term meteorological drought can lead to hydrological drought by restricting water resources required by humans, such as streamflow and reservoir storage. We developed two new indices for monitoring hydrologic drought-based satellite-derived evapotranspiration, a major factor in hydrological drought occurrence. The Water Budget-based Drought Index (WBDI) estimates potential runoff by the differences between precipitation and evapotranspiration using water budget analysis while the Energy-based Water Deficit Index (EWDI) is an index of available water capacity through evapotranspiration and soil moisture based on solar radiation using energy budget analysis. We used these, along with the existing Standardized Precipitation-Evapotranspiration Index (SPEI, based on precipitation, atmospheric temperature, and evapotranspiration), to map the spatiotemporal patterns of drought on the Korean Peninsula from 2001 to 2014. For validation against actual drought conditions, we compared the results with streamflow data from five gauging stations in South Korea. EWDI—the diagnostic approach—performed best when assessing current hydrological drought conditions, while WBDI and SPEI—prognostic approach—best captured drought conditions after 2–3 months of lag time. Our results confirmed that evapotranspiration is a major factor affecting hydrological drought; the new methods demonstrated here make it possible to evaluate drought through diagnostic and prognostic perspectives depending on the situation, thereby improving scientific drought evaluation capacity.

Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea

This study compared the historical and future simulations of precipitation in South Korea from INM-CM4 of Coupled Model Intercomparison Project (CMIP) 5 and INM-CM5 of CMIP6 to identify their differences for the projections of corresponding scenarios by three timeframes (annual, summer and winter) and four regions (NW, NE, SW and SE). Six performance indicators were used to quantify the models' reproducibility to precipitation at 22 stations in South Korea for the historical period (1970–2005). Then, the change rates of precipitations in near and far futures (2020–2059 and 2060–2099) were calculated for two representative concentration pathway (RCP) 4.5 and 8.5 and socioeconomic shared pathway (SSP) 2–4.5 and 5–8.5. Their uncertainties were also quantified using standard deviations and interquartile ranges. As a result, CM5 clearly showed a 7.4% improvement in six performance indicators. The change rates in far future were larger but the uncertainties were smaller. But both the rates and uncertainties in NW were the largest. Also, the uncertainties in INM-CM5 were also smaller than in INM-CM4 for all timeframes and the differences between RCP4.5 and SSP2-4.5 were absolutely larger than those between RCP8.5 and SSP5-8.5.