Korean Meteorological Administration Research Articles

Climate temperature and seasonal influences on the prevalence of temporomandibular disorders in South Korea

This study aimed to explore seasonal variations in temporomandibular disorder (TMD) prevalence in South Korea, utilizing nationwide population-based big data. Data corresponding to the Korean Standard Classification of Diseases code of K07.6, which identifies TMD, were extracted from the Health Insurance Review and Assessment Service online platform for the period from 2010 to 2022. Additionally, we integrated these data with climate temperature records from the Korean Meteorological Administration. We subsequently conducted a statistical analysis of TMD patient data on a monthly and seasonal basis over the past 13 years to assess prevalence. Over the past 13 years, the number of TMD patients in Korea has steadily increased. The prevalence of TMD rose from 0.48% (224,708 out of a total population of 50,515,666) in 2010 to 0.94% (482,241 out of a total population of 51,439,038) in 2022, marking a 1.96-fold increase. Among children under 10 years of age, no significant differences were observed in TMD prevalence between boys and girls. However, a distinct female predominance emerged after the age of 10, with an average female-to-male ratio of 1.51:1. The peak prevalence of TMD occurred in individuals in their 20 s, followed by adolescents in their late 10 s. The majority of TMD patients were concentrated in Seoul and Gyeonggi province, with metropolitan areas accounting for 50% of the total patient count. Seasonally, TMD patient numbers showed no significant increase in winter compared with spring or summer. The temperature difference, defined as the absolute difference between the highest and lowest temperatures for each month, showed a positive correlation with TMD patient counts. A greater temperature difference was associated with higher patient counts. The strongest correlation between temperature differences and TMD patient numbers was observed in winter (r = 0.480, p < 0.01), followed by summer (r = 0.443, p < 0.01), and spring (r = 0.366, p < 0.05). Temperature differences demonstrated a significantly stronger correlation with the increase in the number of TMD patients than absolute climate temperatures. This aspect should be a key consideration when examining seasonal trends in TMD prevalence in South Korea.

Latitudinal distribution and sources analysis of greenhouse gases and air pollutants observed during the 2021 Yellow Sea Air Quality campaign aboard a research vessel

The Yellow Sea serves as a pathway for air pollution transport from the Asian continent into Korea and is annually monitored by the Korean Meteorological Administration (KMA) research vessel during the spring season. This study investigated the spatio-temporal variation of long-range transported greenhouse gases (CO2 and CH4) and air pollutants (CO, NOx, SO2, O3) in the Yellow Sea during the Yellow Sea Air Quality (YES-AQ) campaign. Our analysis reveals that GHGs and air pollutants exhibit a clear latitudinal distribution. Lower concentrations are observed in the range of 31.2–34.5°N, primarily influenced by air masses traversing the ocean and the western part of Korean Peninsula. In contrast, higher concentrations occur more frequently at 34.5–37.8°N, primarily affected by continental air masses. The regression slopes of CO and CO2 vary depending on regional characteristics, particularly in air originating from Korea and China. Regression slope analysis between CO, CO2, and CH4, except for high plume of CH4 (>2.05 ppm), indicates that fossil fuel sources play a significant role. A high plume of CH4 (>2.05 ppm) was observed on April 22nd, coinciding with the maximum O3 concentrations during the period of highest temperatures throughout the voyage. The potential source regions were identified in the west-southern coast of Korea, using footprint analysis from Stochastic Time-Inverted Lagrangian Transport (STILT) and column-averaged dry-air mole fraction of CH4 (XCH4) retrieval from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite. The high CH4 enhancements are most likely due to biogenic emissions triggered by favorable near-surface temperatures for microbial activities in soil. Furthermore, the high CH4 enhancements were not correlated with CO and CO2, indicating that the source was not related to fossil fuel combustion. On March 29–30, a high plume of NOx was detected and is likely attributed to the Bohai Sea, which is located just above the Yellow Sea and surrounded by highly industrialized and semi-enclosed areas.

Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow

Precipitation nowcasting is critical for preventing damage to human life and the economy. Radar echo tracking methods such as optical flow algorithms have been widely employed for precipitation nowcasting because they can track precipitation motions well. Thus, this method, including the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE), was implemented for operational precipitation nowcasting. However, advection-based methods struggle to predict the nonlinear motions of precipitation fields and dynamic processes, such as the growth and decay of precipitation. This study proposes an enhanced optical flow model using a multi-temporal optical flow field and a conditional generative adversarial network (cGAN). We trained the proposed model using a 3-year radar dataset provided by the Korean Meteorological Administration and performed forecast skill evaluations using both qualitative and quantitative methods. In particular, the model featuring multi-temporal optical flow enhances prediction accuracy for the nonlinear motion of precipitation fields, and the model’s accuracy can be further improved through the use of the cGAN structure. We have verified that these improvements hold for 0–3 h lead times. Based on this performance enhancement, we conclude that the multi-temporal optical flow model with cGAN has a potential role in operational precipitation nowcasting.

Assessment of growing condition variables on alfalfa productivity.

This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa L.) productivity. A total of 197 alfalfa yield results were acquired from the alfalfa field trials conducted by the South Korean National Agricultural Cooperative Federation or Rural Development Administration between 1983 and 2008. The corresponding climate and soil data were collected from the database of the Korean Meteorological Administration. Twenty-three growing condition variables were developed as explaining variables for alfalfa forage biomass production. Among them, twelve variables were chosen based on the significance of the partial-correlation coefficients or potential agricultural values. The selected partial correlation coefficients between the variables and alfalfa forage biomass ranged from -0.021 to 0.696. The influence of the selected twelve variables on yearly alfalfa production was summarized into three dominant factors through factor analysis. Along with the accumulated temperature variables, the loading scores of the daily mean temperature higher than 25°C were over 0.88 in factor 1. The sunshine duration at temperature between 0°C-25°C was 0.939 in factor 2. Precipitation days were 0.82, which was the greatest in factor 3. Stepwise regression applied with the three dominant factors resulted in the coefficients of factors 1, 2, and 3 for 0.633, 0.485, and 0.115, respectively, and the R-square of the model was 0.602. The environmental conditions limiting alfalfa growth, such as daily temperature higher than 25°C or daily mean temperature affected annual alfalfa production most substantially among the growing condition variables. Therefore, future cultivar selection should consider the capability of alfalfa to be tolerant to extreme summer weather along with biomass production potential.

Toward a ground-based and long-term meteorological forcing dataset for South Korea

The Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) is a well-established reanalysis dataset and is widely used for driving global-scale hydrological models. However, owing to its relatively coarse spatial resolution (0.5°), the capability of MERRA-2 is repeatedly challenged in regional-scale studies, especially for smaller areas of interest. In addition, the availability of in situ observation data is a pressing issue for generating meteorological forcing. We developed a grid-based high spatial (0.125°) and temporal (hourly) resolution meteorological forcing dataset, which can evaluate hydrological processes in South Korea using state-of-the-art meteorological observations from 1980 to 2020. The forcing dataset was created by combining Automated Synoptic Observing System (ASOS) in situ measurement data from the Korean Meteorological Administration and MERRA-2 reanalysis datasets. Five meteorological variables were provided in the ASOS-MERRA2 (precipitation, air temperature, surface pressure, specific humidity, and wind speed). The study demonstrates that the region-based and high spatial resolution of ASOS-MERRA2 is superior to the existing MERRA-2 with improvements of all five weather variables, for example, from 5.6 to 2.8 mm root mean square error of precipitation. The ASOS-MERRA2 was more capable of reducing the biases and root mean squared error by improving the coefficient of determination compared with MERRA-2 for all five variables. The newly developed ASOS-MERRA2 provides an opportunity to drive land surface models to evaluate the hydroclimatic conditions in South Korea.

Study on the Slant-Path Effect in the Simulation of Clear-Sky Thermal Radiance for the GK2A AMI

Abstract Taking the slanted satellite viewing geometry into account is important in the simulation of satellite radiances, which vary with the atmospheric conditions along the line of sight. As a first step to take the slanted satellite viewing geometry into account in the numerical weather prediction system operated in the Korean Meteorological Administration, the slant-path modeling is applied for the simulation of clear-sky thermal radiances of a geostationary satellite imager, the Advanced Meteorological Imager (AMI) on board the Geo-KOMPSAT-2A (GK2A). The observations minus simulations (O − B) and the Jacobians before and after the slant-path calculation are compared. Since most infrared channels of AMI are not sensitive to the atmosphere above the tropopause, the size of slant-path effect for AMI is overall smaller than the effect shown in the microwave sounders. Still, the slant-path modeling is found to have a noticeable effect on the three water vapor absorption channels of AMI peaking between 300 and 600 hPa, particularly at large satellite zenith angles and on the regions with high water vapor variabilities in the model field along the line of sight. On average, the slant-path interpolation reduces the standard deviation of O − B of the water vapor channels by around 2.0% on land and 1.4% over the ocean for zenith angles 40°–60°, and it also influences not only the shape and magnitude but also the height of the peak of the Jacobians. In addition, the retrieval experiment based on the optimal estimation also demonstrates the impact of this new approach in the retrieved moisture field, though the improvement is not as significant as shown in the simulation experiment.

Horizontal seismic wave at ground surface from transfer function based on ambient noise

Earthquake detection can be improved by ensuring that seismometer sites experience little artificial noise in the surrounding environment. To minimize noise, seismological stations should be positioned in rocky mountainous areas without nearby valleys, away from significant human activity. However, such surface sites may be scarce when constructing dense monitoring networks, necessitating the use of underground sites to ensure low noise levels. The Korean Meteorological Administration is currently installing new underground seismometers to increase seismic monitoring capacity. However, seismic data on the ground surface are also required for engineering technological developments (to reduce damage to structural components). Therefore, borehole seismic stations without surface seismometers need to estimate ground surface motion from borehole record data. We propose a transfer function that converts motion within boreholes to surface seismic waves using ambient noise, thereby facilitating estimation of ground surface motions using borehole seismometers. As a result, predicting ground surface motion from borehole record data becomes possible.

Analysis of the Skyscraper Wind around High-Rise Buildings in Coastal Region, South Korea, during Typhoon ‘Hinnamnor’

High-rise buildings in cities adversely affect wind regimes by changing the air currents in their surrounding areas. In particular, extreme climate phenomena caused by climate change are stronger and more frequent, causing damage in cities. To better understand skyscraper wind behaviors around high-rise buildings, actual measurements are necessary to determine the environmental assessment of the wind effect. In this study, field measurements were performed with five anemometers at five points in the vicinity of a skyscraper called the LCT residential complex (411.6 m tall) surrounded by high-rise buildings in the coastal city of Busan, South Korea during Typhoon Hinnamnor. The gust was 3.7 times stronger, while the maximum 1-min mean wind speed was 3.1 times stronger than those measured at a nearby reference weather station operated by the Korean Meteorological Administration. The characteristics of downward and canyon winds were shown to depend on the spatiotemporal characteristics of the five points. The turbulence intensity declined as the wind speed increased and converged to a certain value. The gust factor also dropped as the wind speed increased and converged to 2.0, and was considered to be the parameter that best represents the intensity of instantaneous gust caused by the skyscraper wind effect. These results suggest that high-rise buildings should be designed with the consideration of gusts twice as strong as the average wind speed. In addition, field measurements should be accompanied in order to respond to the skyscraper wind effect.

A Deep Convolutional Neural Network Model for Improving WRF Simulations.

Advancements in numerical weather prediction (NWP) models have accelerated, fostering a more comprehensive understanding of physical phenomena pertaining to the dynamics of weather and related computing resources. Despite these advancements, these models contain inherent biases due to parameterization of the physical processes and discretization of the differential equations that reduce simulation accuracy. In this work, we investigate the use of a computationally efficient deep learning (DL) method, the convolutional neural network (CNN), as a postprocessing technique that improves mesoscale Weather Research and Forecasting (WRF) one-day simulation (with a 1-h temporal resolution) outputs. Using the CNN architecture, we bias-correct several meteorological parameters calculated by the WRF model for all of 2018. We train the CNN model with a four-year history (2014-2017) to investigate the patterns in WRF biases and then reduce these biases in simulations for surface wind speed and direction, precipitation, relative humidity, surface pressure, dewpoint temperature, and surface temperature. The WRF data, with a spatial resolution of 27 km, cover South Korea. We obtain ground observations from the Korean Meteorological Administration station network for 93 weather station locations. The results indicate a noticeable improvement in WRF simulations in all station locations. The average of annual index of agreement for surface wind, precipitation, surface pressure, temperature, dewpoint temperature, and relative humidity of all stations is 0.85 (WRF:0.67), 0.62 (WRF:0.56), 0.91 (WRF:0.69), 0.99 (WRF:0.98), 0.98 (WRF:0.98), and 0.92 (WRF:0.87), respectively. While this study focuses on South Korea, the proposed approach can be applied for any measured weather parameters at any location.

Associations of cold exposure with hospital admission and mortality due to acute kidney injury: A nationwide time-series study in Korea

BackgroundEmerging evidence supports an association between heat exposure and acute kidney injury (AKI). However, there is a paucity of studies on the association between cold exposure and AKI. ObjectiveWe aimed to investigate the associations of cold exposure with hospital admission and mortality due to AKI and to explore whether these associations were influenced by age and sex. MethodsInformation on daily counts of hospital admission and mortality due to AKI in 16 regions of Korea during the cold seasons (2010–2019) was obtained from the National Health Insurance Service (a single national insurer providing universal health coverage) and Statistics Korea. Daily mean temperature and relative humidity were calculated from hourly data obtained from 94 monitoring systems operated by the Korean Meteorological Administration. Associations of low temperatures (<10th percentile of daily mean temperature) and cold spells (≥2 consecutive days with <5th percentile of daily mean temperature) up to 21 days with AKI were estimated using quasi-Poisson regression models adjusted for potential confounders (e.g., relative humidity and air pollutants) with distributed lag models and univariate meta-regression models. ResultsLow temperatures were associated with hospital admission due to AKI [relative risk (RR) = 1.12, 95 % confidence interval (CI): 1.09, 1.16]. Cold spells were associated with hospital admission (RR = 1.87, 95 % CI: 1.46, 2.39) and mortality due to AKI (RR = 4.84, 95 % CI: 1.30, 17.98). These associations were stronger among individuals aged ≥65 years than among those aged <65 years. ConclusionOur results underscore the need for the general population, particularly the elderly, physicians, and other healthcare providers to be more vigilant to cold exposure, given the risk of AKI. Government agencies need to develop specific strategies for the prevention and early detection of cold exposure-related AKI.

Spatial portability of random forest models to estimate site-specific air temperature for prediction of emergence dates of the Asian Corn Borer in North Korea

The air temperature estimates derived from satellite data products would facilitate site-specific prediction of pest outbreaks. Here we propose an approach to estimate air temperature with spatial portability using both land surface temperature (LST) and atmospheric profile (AP) products based on MODIS satellites. In the approach, quantitative and qualitative variables were used as inputs to assess and improve the spatial portability of random forest (RF) models. Daily temperature data at sites in the Korean peninsula were collected from a weather database operated by the Korean Meteorological Administration. Sets of input variables were defined to represent temperature data from the LST (LT) and AP (AT) products, geographical properties (GE), data quality and cloud conditions (QC), land cover type (LC), and auxiliary properties (AX), respectively. The combinations of these sets were used as inputs to the RF models, which were cross-validated using satellite data and weather observation data in South Korea from 2013 to 2019. Concordance Correlation Coefficient (CCC) and Spatial Portability Index (SPI) were determined to compare the accuracy of the RF models across the sites of interest. The RF model that uses AT, LT, and QC variable sets as inputs (RFALQ) tended to have high values of CCC among test sites in South Korea, which ranged from 0.91 to 0.99. RFALQ also had higher values of SPI at sites in North Korea than the other models in which either AT or LT was excluded. Furthermore, the emergence date of Asian Corn Borer (Ostrinia furnacalis) was predicted with high values of CCC (0.94–0.97) across the study sites when RFALQ was used to prepare the input data to an insect phenology model. Our results suggest that both LST and AP products would contribute to higher spatial portability of air temperature estimation. This also hints that the temperature estimation model with high spatial portability would facilitate the pest models, which can be used to reduce the risk of food insecurity in developing countries with sparse weather station networks.

Development of model output statistics based on the least absolute shrinkage and selection operator regression for forecasting next‐day maximum temperature in South Korea

AbstractRegression models for model output statistics (MOS) based on least absolute shrinkage and selection operator methods were developed to forecast next‐day maximum surface air temperature (TMAX) during the warm season in South Korea. The forecast fields from the operational numerical weather prediction (NWP) system of the Korean Meteorological Administration for global and local forecasts and the observed TMAX data in 225 observation stations were used as input variables for the MOS. The training period was July and August (JA) from 2015 to 2018, and the regression models were tested using data from JA 2019. As a result of hindcasting for the test period, the MOS models performed significantly better for next‐day TMAX forecasting over South Korea than the numerical models during JA 2019. The mean TMAX errors were reduced by over 1°C in MOSs compared to those in the numerical models. However, the TMAX forecast performance was generally lower in the higher‐resolution NWP Local Data Assimilation and Prediction System (LDAPS)‐based MOS (LMOS) than in the lower‐resolution NWP Global Data Assimilation and Prediction System (GDAPS)‐based MOS. This pattern was dominant when LDAPS simulated the TMAX more accurately than average. In particular, the random TMAX error of LDAPS was larger than that of GDAPS during the training period, and a positive random error of TMAX was magnified in LMOS. Because the other predictors forecasted from LDAPS can be associated with lower TMAX forecast performance of LMOS, in addition to TMAX effects as a predictor, a new MOS was developed using both LDAPS and GDAPS outputs. The forecast accuracy was improved by up to 0.3°C when the forecast fields from the GDAPS substituted several LMOS predictors, even though TMAX was the primary predictor for LMOS.

Estimating the Economic Value of Improving the Asian Dust Aerosol Model in the Korean Household Sector: A Choice Experiment

Asian dust (AD), a seasonal phenomenon in East Asia, refers to dust winds that blow from inland China and the Gobi Desert. Generally, AD occurs in the spring, but due to climate change and the desertification of AD sources, it has recently started occurring in every season except for midsummer. The occurrence of AD adversely affects human health and damages various industries. Accordingly, the Korean Meteorological Administration has provided an AD forecast service to minimize damage. The most essential part of providing the AD forecast service is the Asian Dust Aerosol Model (ADAM). This study tries to provide useful information for policymaking and research and development by deriving the economic value of ADAM improvement. To this end, a choice experiment (CE) was applied. The CE can derive the marginal value of or the marginal willingness to pay (MWTP) of improvement in attribute. All the MWTP estimates obtained in this study were statistically significant. The household MWTP estimates for a 1% improvement in ADAM accuracy and a one-level subdividing were obtained as USD 0.04 and USD 0.12 per household. This finding indicates that people have a greater preference for improving AD forecast segmentation than for improving ADAM accuracy.

Hadley Circulation in the Present and Future Climate Simulations of the K-ACE Model

Hadley circulation (HC) is a planetary-scale overturning circulation in the tropics that transports momentum, heat, and moisture poleward. In this study, we evaluate the strength and extent of the HC in the historical and future climate simulations of the Korean Meteorological Administration (KMA) Advanced Community Earth system model (K-ACE), which was recently developed by the National Institute of Meteorological Sciences of Korea. Compared with a reanalysis product, the overall structure of the HC is reasonably reproduced by the K-ACE. At the same time, it is also found that the Northern Hemisphere HC in the K-ACE is shifted southward by a few degrees, while the strength of the Southern Hemisphere (SH) HC is under-represented by approximately 20%. These biases in the strength and extent of the HC can be explained by biases in the eddy momentum flux and precipitation in the tropics. In the future climate simulations under the Shared Socioeconomic Pathway 5-Representative Concentration Pathway 8.5 scenario, the HCs in the K-ACE show a weakening and widening trend in both hemispheres, which is consistent with the projections of many Coupled Model Intercomparison Project Phase 6 models. A notable feature of the K-ACE is the widening of the SH HC, which takes place at a rate that is about double the multi-model mean. Climate models that share the component models with the K-ACE, such as UKESM, HadGEM3-GC31-LL, and ACCESS-CM2/ESM1, also show enhanced poleward expansion of the HC in the SH. This strong expansion is shown to be dominated by the expansion of the regional HC over the Pacific.

Identification of heatwave hotspots in Seoul using high-resolution population mobility data

This study proposes the methodology to identify heatwave hotspots in Seoul, the metropolis of Korea, using high-resolution data. Resident credit data, population mobility data, and temperature observation data are analyzed to determine vulnerable regions to heatwaves. Potentially vulnerable regions are derived in two ways: static vulnerable regions (SVRs) and dynamic vulnerable regions (DVRs), depending on their characteristics. SVRs are determined by low-income (lower 20% income quantile) residential areas fixed on time. In contrast, DVRs vary with the time and day of the week. DVRs are defined by the place less responsive to heatwaves, where are with low population variability and low correlation with temperature. The final vulnerable regions, so-called hotspots, are determined by the high temperature predicted area where the SVRs and DVRs intersect. We examine how to remove commuting-related displacement signals, which are represented as noise when analyzing population mobility. An example of the hotspots identification result is also shown using temperature hindcast data generated by the Korean Meteorological Administration short-range forecast system. Applying the vulnerability information can improve the quality of disaster planning and decision-making by highlighting the time and area of need for resources in the implementation of short- and long-term disaster response.

Skill assessment of TIGGE precipitation forecasts in the four basins of Iran

In atmospheric sciences, forecast assessment, the process of examining the quality of forecasts, is very important. For this reason, we carried out a comprehensive evaluation of the most important global numerical weather forecast (NWP) models for the Iranian basins of Karkheh, Karun, Urmia, and Sefidrood. This study aims to evaluate the output of six different NWP models from the TIGGE database in important basins of Iran, to determine the most appropriate model for each basin. The assessment period took place between 2008 and 2018 and evaluated probabilistic and non-probabilistic methods. In the Karun Basin, the global NWP models were shown to perform better than in the other three basins. Overall, the UKMO (United Kingdom Met Office) and ECMWF (European Medium-Range Weather Forecast) performed best, while the Korean Meteorological Administration (KMA) showed the poorest performance among the models in the four basins. In the 25 mm threshold, most models underestimated precipitation events. The ECMWF model showed reliable and sharp forecasts and was better able to discriminate between low, medium, and high precipitation events compared to other models. Compared to those of other models, the UKMO model demonstrated greater brier skill scores. In general, the ECMWF, UKMO, ECCC (Environment and Climate Change Canada), and National Center for Environmental Forecast (NCEP) models performed relatively satisfactory in all four basins, although they forecasted better in basins with higher mean precipitation. These models are recommended for flood warning systems, and water resource decision making in Iran. However, all forecasts will need post-processing for operational applications.

Intensity Reassessment of the 2017 Pohang Earthquake Mw = 5.4 (South Korea) Using ESI-07 Scale

The earthquake environmental effects (EEEs) around the epicentral area of the Pohang earthquake (Mw-5.4) that occurred on 15 November 2017 have been collected and classified using the Environmental Seismic Intensity Scale (ESI-07 scale) proposed by the International Union for Quaternary Research (INQUA) focus group. The shallow-focus 15 November Pohang earthquake did not produce any surface rupture, but caused extensive secondary environmental effects and damage to life-line structures. This earthquake was one of the most damaging earthquakes during the instrumental seismic era of the Korean Peninsula. The EEEs included extensive liquefaction, ground cracks, ground settlement, localized rockfall, and variation of the water table. The main objective of this paper was to carry forward a comparative assessment of the Pohang earthquake’s intensity based on traditional macroseismic scales and the ESI-07 scale. With that objective, this study will also make a substantial contribution to any future revision of the ESI-07 scale, which mostly comprises case studies from Europe and South America. The comparison of the ESI-07 scale with traditional intensity scales similar to the intensity scale used by the Korean Meteorological Administration for the epicentral areas showed 1–2-degree differences in intensity. Moreover, the ESI scale provided a clearer picture of the intensity around the epicentral area, which is mostly agricultural land with a lack of urban units or buildings. This study urges the integration of the traditional and ESI-07 scale for such small magnitude earthquakes in the Korean Peninsula as well as around the world in future. This will predict seismic intensity more precisely and hence provide a more-effective seismic hazard estimation, particularly in areas of low seismic activity. The present study will also provide a useful and reliable tool for the seismic hazard assessment of similar earthquakes around the study area and land-use planning at a local scale considering the secondary effects.

Characteristics of LDAPS-Predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories (urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains) based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s−1) and underestimated temperature (average TE = −0.63 °C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s−1, average TE = 0.12 °C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

In Situ Aircraft Measurements of CO2 and CH4: Mapping Spatio-Temporal Variations over Western Korea in High-Resolutions

A cavity ring-down spectroscopy (CRDS) G-2401m analyzer onboard a Beechcraft King Air 350, a new Korean Meteorological Administration (KMA) research aircraft measurement platform since 2018, has been used to measure in situ CO2, CH4, and CO. We analyzed the aircraft measurements obtained in two campaigns: a within-boundary layer survey over the western Republic of Korea (hereafter Korea) for analyzing the CO2 and CH4 emission characteristics for each season (the climate change monitoring (CM) CM mission), and a low altitude survey over the Yellow Sea for monitoring the pollutant plumes transported into Korea from China (the environment monitoring (EM) mission). This study analyzed CO2, CH4, and CO data from a total of 14 flights during 2019 season. To characterize the regional combustion sources signatures of CO2 and CH4, we calculated the short-term (1-min slope based on one second data) regression slope of CO to CO2 and CH4 to CO enhancements (subtracted with background level, present as ∆CO, ∆CO2, and ∆CH4); slope filtered with correlation coefficients (R2) (&lt;0.4 were ignored). These short-term slope analyses seem to be sensitive to aircraft measurements in which the instrument samples short-time varying mixtures of different air masses. The EM missions all of which were affected by pollutants emitted in China, show the regression slope between ∆CO and ∆CO2 with of 1.8–6% and 0.3–0.7 between ∆CH4 and ∆CO. In particular, the regression slope between ∆CO and ∆CO2 increased to &gt;4% when air flows from east-central China such as Hebei, Shandong, and Jiangsu provinces, etc., sustained for 1–3 days, suggesting pollutants from these regions were most likely characterized by incomplete fossil fuel combustions at the industries. Over 80% of the observations in the Western Korea missions were attributed to Korean emission sources with regression slope between ∆CO and ∆CO2 of 0.5–1.9%. The CO2 emissions hotspots were mainly located in the north-Western Korea of high population density and industrial activities. The higher CH4 were observed during summer season with the increasing concentration of approximately 6% over the background level, it seems to be attributed to biogenic sources such as rice paddies, landfill, livestock, and so on. It is also noted that occurrences of high pollution episodes in North-Western Korea are more closely related to the emissions in China than in Korea.

The Association between the Frequency of Rhegmatogenous Retinal Detachment and Atmospheric Temperature

Rhegmatogenous retinal detachment (RRD) frequency was observed to be higher with an increase in the daily temperature range. This showed that a wide daily range of temperature, rather than the absolute value of the temperature, is associated with the occurrence of RRD. Purpose. To investigate the association between the frequency of rhegmatogenous retinal detachment (RRD) and the atmospheric temperature. Method. A retrospective review of consecutive eyes that had undergone primary RRD surgery from 1996 to 2016 at Chungbuk National University Hospital was conducted. Temperature data (highest, lowest, and mean daily temperatures and daily temperature range) in Chungbuk Province were obtained from the Korean Meteorological Administration database. We investigated the relationship between the daily temperature range and the frequency of RRD surgery. We also analyzed the association between various temperature data and the frequency of RRD surgery. Result. There were 1,394 RRD surgeries from 1996 to 2016. Among them, 974 eyes were included in this study. The monthly average number of RRD operations showed a bimodal peak (in April and October) throughout the year. With the same tendency as the frequency of RRD, the monthly average of the daily temperature range over 1 year also showed a bimodal peak in April and October. There was a significant positive correlation between the monthly average of the daily temperature range and the number of RRD surgeries (r = 0.297, P < 0.001). However, there were no associations between RRD frequency and the mean temperature, highest temperature, and lowest temperature. Conclusion. The higher the daily temperature range, the higher was the RRD frequency observed. We speculated that dynamic changes in temperature during the day may affect degrees in chorioretinal adhesion and liquefaction of the vitreous, which may eventually result in retinal detachment. Therefore, further experimental studies on the correlation between temperature changes and retinal detachment are needed.