Korea Meteorological Administration Research Articles

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula.

Synoptic Analysis and Subseasonal Predictability of an Early Heatwave in the Eastern Mediterranean

Greece and the surrounding areas experienced an early warm spell with characteristics of a typical summer Mediterranean heatwave in mid-May 2020. The maximum 2 m temperature at Kalamata (southern Greece) reached 40 °C on May 16th and at Aydin (Turkey), it was 42.6 °C on May 17th. There was a 10-standard deviation positive temperature anomaly (relative to the 1975–2005 climatology) at 850 hPa, with a southwesterly flow and warm advection over Greece and western Turkey from May 11 to 20. At 500 hPa, a ridge was located over the Eastern Mediterranean, resulting in subsidence. The aims of this study were (a) to investigate the prevailing synoptic conditions during this event in order to document its occurrence and (b) to assess whether this out-of-season heatwave was predictable on subseasonal timescales. The subseasonal predictability is not a well-researched scientific topic in the Eastern Mediterranean Sea. The ensemble global forecasts from six international meteorological centres (European Centre for Medium-Range Weather Forecasts—ECMWF, United Kingdom Met Office—UKMO, China Meteorological Administration—CMA, Korea Meteorological Administration—KMA, National Centers for Environmental Prediction—NCEP and Hydrometeorological Centre of Russia—HMCR) and limited area forecasts using the Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF) forced by Climate Forecast System version 2 (CFSv.2; NCEP) forecasts were evaluated for lead times ranging from two to six weeks using statistical scores. WRF was integrated using two telescoping nests covering Europe, the Mediterranean basin and large part of the Atlantic Ocean, with a grid spacing of 25 km, and Greece–western Turkey at 5 km. The results showed that there were some accurate forecasts initiated two weeks before the event’s onset. There was no systematic benefit from the increase of the WRF model’s resolution from 25 km to 5 km for forecasting the 850 hPa temperature, but regarding the prediction of maximum air temperature near the surface, the high resolution (5 km) nest of WRF produced a marginally better performance than the coarser resolution domain (25 km).

Operational Aviation Icing Forecast Algorithm for the Korea Meteorological Administration

Abstract In this study, we developed and evaluated the Korean Forecast Icing Potential (K-FIP), an in-flight icing forecast system for the Korea Meteorological Administration (KMA) based on the simplified forecast icing potential (SFIP) algorithm. The SFIP is an algorithm used to postprocess numerical weather prediction (NWP) model forecasts for predicting potential areas of icing based on the fuzzy logic formulations of four membership functions: temperature, relative humidity, vertical velocity, and cloud liquid water content. In this study, we optimized the original version of the SFIP for the global NWP model of the KMA through three important updates using 34 months of pilot reports for icing as follows: using total cloud condensates, reconstructing membership functions, and determining the best weight combination for input variables. The use of all cloud condensates and the reconstruction of these membership functions resulted in a significant improvement in the algorithm compared with the original. The weight combinations for the KMA’s global model were determined based on the performance scores. While several sets of weights performed equally well, this process identified the most effective weight combination for the KMA model, which is referred to as the K-FIP. The K-FIP demonstrated the ability to successfully predict icing over the Korean Peninsula using observations made by research aircraft from the National Institute of Meteorological Sciences of the KMA. Eventually, the K-FIP icing forecasts will provide better forecasts of icing potentials for safe and efficient aviation operations in South Korea. Significance Statement In-flight aircraft icing has posed a threat to safe flights for decades. With advances in computing resources and an improvement in the spatiotemporal resolutions of numerical weather prediction (NWP) models, icing algorithms have been developed using NWP model outputs associated with supercooled liquid water. This study evaluated and optimized the simplified forecast icing potential, an NWP model–based icing algorithm, for the global model of the Korean Meteorological Administration (KMA) using a long-term observational dataset to improve its prediction skills. The improvements shown in this study and the SFIP implemented in the KMA will provide more informative predictions for safe and efficient air travel.

Development of Interpretable Probability Ellipse in Tropical Cyclone Track Forecasts Using Multiple Operational Ensemble Prediction Systems

AbstractMost tropical cyclone (TC) forecasting centers have implemented a probabilistic circle to represent track uncertainty at a specified lead time. Recent studies suggest that probability ellipses constructed from ensemble prediction systems can convey the anisotropy of track predictability. In this study, a new probability ellipse model is developed to interpret the extent of forward speed and heading uncertainties in ensemble forecasts by selecting an equal proportion of members in the along‐ and cross‐track directions. This method is validated using the 2019–2021 western North Pacific (WNP) TC track forecasts from the ensemble predictions of the European Centre for Medium‐Range Weather Forecasts, the United States National Centers for Environmental Prediction, and the Korea Meteorological Administration. When the proportion of ensemble members in the ellipse is set to 70%, more than one‐half (50.0%–73.6%) of the forecasts, depending on the lead time, indicate reduced area compared with that of the circle. The mean areas of the probability ellipses are 4.9%, 7.0%, 10.0%, and 11.5% smaller than those of the circle in 48‐, 72‐, 96‐, and 120‐hr forecasts, respectively. The forward speed shows greater uncertainty than the heading, as evidenced by the along‐track radii being larger than the cross‐track counterpart in ∼60% of the samples, regardless of the lead time. In addition, the regional distribution of the along‐track/cross‐track ratio in the probability ellipses can explain the dominant direction of the track error in a particular location. The proposed probability ellipse shows potential for application in operational TC track predictions.

Development of a prediction system for precipitation- and wind-causing typhoons affecting the Korean peninsula using observational data

Introduction: When the forecasted typhoon track differs from the numerical model’s prediction, the estimated precipitation and wind from the model may not be reliable. Typically, forecasters receive numerical model forecasts with a delay of 4 h or more in calculation time. However, a more timely reference of precipitation and wind forecasts is required in an emergency with an approaching typhoon. Analyses of the observational data of typhoon-related characteristics, such as heavy rainfall and strong winds, from 1997 to 2021 revealed that their distribution areas are considerably affected by typhoon tracks. In this study, we developed a precipitation and wind prediction system based on the observational data of the typhoons that affected the Korean Peninsula.Methods: Typhoon tracks were categorized into west-coast landfalls, southeast landfalls, and those passing the Korea Strait. Each category affects the Korean Peninsula differently in terms of rainfall and wind. We devised a system that predicts these patterns based on incoming typhoon tracks. We can make forecasts by comparing the approaching typhoons to previous instances and analyzing their center, movement direction, and size. Observations from these past typhoons were averaged to produce a forecast grid for each new typhoon.Results: Our system, validated from 2019 to 2022, showed a wind speed root-mean-square error of 3.37 m/s and a precipitation accuracy index of 0.72. For comparison, traditional numerical models yielded 5.04 m/s and 0.75, respectively. This indicates that our system is comparably efficient and computationally less demanding.Discussion: Our system’s strength is its ability to offer real-time typhoon forecasts, often faster than numerical models. However, its dependence on historical data limits its predictive power for atypical weather scenarios. It is essential to consider integrating ensemble models with these observations for enhanced accuracy. Since 2022, this system has been operational at the Korea Meteorological Administration, showing consistent reliability in forecasting.

Utilization of the Winkler scale of plants using big data temperature presented by the Korea Meteorological Administration.

Rice is an important food source that can provide a stable supply of calories for most people around the world. However, owing to the recent rapid temperature rise, we are facing social issues related to the increase in the Winkler scale. In this study, a strategy for screening potential candidate genes related to the yield according to the Winkler scale is presented, and the possibility of using a candidate gene identified through sequence haplotype and homology analysis as a breeding source is suggested. QTL for the Winkler scale was identified using a population of 120 double haploids derived from a cross between Cheongchoneg, Indica, and Nagdong, Japonica. A total of 79 candidate genes were detected in the identified QTL region, and OsHAq8 was finally screened. Through haplotype analysis, OsHAq8 was derived from the Indica group and orthologous to Graminae's activator of Hsp90 ATPase, suggesting that it is a candidate gene involved in yield according to temperature during the growing period. The expression level of OsHAq8 increased as the Winkler scale increased. The findings of this study can serve as a crucial indicator for predicting harvest time and grain quality while achieving a stable yield through marker selection and adaptation to climate change. Climate change occurs more frequently. In these situations, it is very important to predict harvest time and apply relevant candidate genes to breeding. The candidate genes presented in this study can be effectively applied to rice breeding in preparation for climate change.

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Abstract We developed an advanced postprocessing model for precipitation forecasting using a microgenetic algorithm (MGA). The algorithm determines the optimal combination of three general circulation models: the Korean Integrated Model, the Unified Model, and the Integrated Forecast System model. To measure model accuracy, including the critical success index (CSI), probability of detection (POD), and frequency bias index, the MGA calculates optimal weights for individual models based on a fitness function that considers various indices. Our optimized multimodel yielded up to 13% and 10% improvement in CSI and POD performance compared to each individual model, respectively. Notably, when applied to an operational definition that considers precipitation thresholds from three models and averages the precipitation amount from the satisfactory models, our optimized multimodel outperformed the current operational model used by the Korea Meteorological Administration by up to 1.0% and 6.8% in terms of CSI and false alarm ratio performance, respectively. This study highlights the effectiveness of a weighted combination of global models to enhance the forecasting accuracy for regional precipitation. By utilizing the MGA for the fine-tuning of model weights, we achieved superior precipitation prediction compared to that of individual models and existing standard postprocessing operations. This approach can significantly improve the accuracy of precipitation forecasts. Significance Statement We developed an optimized multimodel for predicting precipitation occurrence using advanced techniques. By integrating various weather models with their optimized weights, our approach outperforms the method of using an arithmetic average of all models. This study underscores the potential to enhance regional precipitation forecasts, thereby facilitating more precise weather predictions for the public.

Prediction Model for Annual Variation in Total Pollen by Allergenic Trees in Korean Cities.

Pollen forecasting systems can provide information for coping with respiratory allergies. They estimate daily pollen production, dispersal, deposition, and removal based on daily weather conditions to predict daily pollen concentrations and provide allergy warnings. As of 2023, the Korea Meteorological Administration (KMA) provides 2-day forecast of allergenic pollens. However, unlike these models, long-term analysis of annual observations of tree pollen reveal annual variations. Therefore, in this study, we aimed to develop annual prediction models for allergenic tree pollens based on long-term multi-site pollen and meteorological data. Daily pollen concentrations were observed using Hirst-type volumetric spore traps at nine sites in Korea from 1998 to 2021, and daily weather data from the closest KMA stations were utilized. Models were developed to predict the seasonal pollen integral of seven tree species based on monthly mean temperature, wind speed, and total precipitation using three variable selection methods: 1) the t-test based key variable screening followed by linear regression with stepwise procedure (TM), 2) direct linear regression with stepwise procedure from the full variable model (FM), and 3) LASSO regression from the full variable model (LM). Data obtained during 1998-2017 and 2018=2021 were utilized for model development and validation, respectively. The root mean squared error, mean absolute error, mean error, and coefficient of determination (R²) revealed that the TM models were best suited for actual forecasting, even though R² in the TM model was lower than those of the FM and LM models. The annual variation model in this study can be integrated with the daily pollen forecast model by controlling the annual pollen potential, and the accuracy of the daily forecast can be improved accordingly.

Effect of Temperature Variation on the Incidence of Acute Myocardial Infarction.

Harsh temperature exposure has been associated with a high risk of cardiovascular events. We sought to investigate the influence of temperature change on long-term incidence of acute myocardial infarction (AMI) in Korean patients. From the National Health Insurance Service (NHIS) customized health information database (from 2005 to 2014), data from a total of 192,567 AMI patients was assessed according to the International Classification of Disease 10th edition code and matched with temperature reports obtained from the Korea Meteorological Administration database. We analyzed data for a 10-year period on a monthly and seasonal basis. The incidence rate per 100,000 year of AMI exhibited a downward trend from 69.1 to 56.1 over the period 2005 to 2014 (P < 0.005), and the seasonal AMI incidence rate per 100,000 year was highest in spring (63.1), and winter (61.3) followed by autumn (59.5) and summer (57.1). On a monthly basis, the AMI incidence rate per 100,000 year was highest during March (64.4) and December (63.9). The highest incidence of AMI occurred during temperature differences of 8-10° in each season. Moreover, AMI incidence tended to increase as the mean temperature decreased (r = -0.233, P = 0.001), and when the mean daily temperature difference increased (r = 0.353, P < 0.001). The AMI incidence rate per 100,000 year has a decreasing trend over the 10-year period, derived from the Korean NHIS database. Modest daily temperature differences (8-10°) and the spring season are related to higher AMI incidence, indicating that daily temperature variation is more important than the mean daily temperature.

Wind resource assessment and potential development of wind farms along the entire coast of South Korea using public data from the Korea meteorological administration

The feasibility of a wind farm locations should be evaluated on a thorough analysis and assessment of the available wind resources which necessitates wind data measurement for a minimum period of one year. Nevertheless, several limitations arise during this process, including challenges related to the site selection, cost considerations such as the installation of meteorological masts subsequent to marine physical and geotechnical surveys, and the requirement for ongoing maintenance. Therefore, several studies have been carried out to determine wind energy potential using reanalysis data. However, in areas with distinct terrain characteristics, such as maritime and coastal areas, uncertainties exist in the use of reanalysis data. Therefore, an analysis of wind resources based on measurement data is necessary in such areas. In this study, wind data measured by the Korea Meteorological Administration's automatic weather system and light house automatic weather system for 56 points along the Korean coast is obtained and analyzed. Additionally, the expected annual energy production and capacity factor were predicted when offshore wind turbines are installed using the power curve of a reference model of large offshore wind turbines in the 15–20 MW class. As a results, five points at which a capacity factor of over 35% is predicted as promising sites for future wind farm development. These public data from the Korea Meteorological Administration are also limited in their ability to provide accurate wind resource analysis for wind power generation due to the wind speed measurement instrument's height being lower than that of the wind turbine hub and environmental influences. However, these results can be useful in identifying possible candidate sites for wind farms in South Korea.

Establishment of a Dynamic Factor Prediction Module for Risk Assessment in Coastal Activity Sites

Recent persistent coastal developments have expanded recreational areas and enhanced accessibility. However, this growth has also led to a rise in safety incidents. These accident factors can be divided into human-made and natural types. The latter is comprised of dynamic factors like waves, tides, sea fogs, and winds. While institutions like the Korea Meteorological Administration and the Korea Hydrographic and Oceanographic Agency already offer data on these dynamic factors, the resolution is often insufficient for a precise assessment of localized risks. In this study, to overcome these limitations, we utilized the dynamic information from existing open systems to construct a high-resolution numerical simulation. Through this, we developed an automated module to predict dynamic factors in localized coastal activity areas. Particularly during the module's construction, we compared and reviewed the numerical prediction results for waves with observed wave heights.

함수형 시계열 자료를 위한 추정 및 예측모형과 응용

Carbon dioxide (CO2) in the atmosphere is a greenhouse gas that absorbs radiant energy emitted from the earth's surface and maintains a suitable temperature for life on the earth. However, human activities such as rapid industrialization have recently increased the concentration of CO2, which has become a cause of global warming. In order to understand and monitor changes in CO2 level, many studies are learning through numerical models related to current and past CO2 level and predicting future CO2 level. In this study, we consider the CO2 data measured by the Korea Meteorological Administration as a functional time-series data model, incorporate the functional data method with the time-series method. Existing studies have used traditional time-series and longitudinal data approaches after fixing seasonal and spatial criterion to entire CO2 data. However, assuming that the CO2 level is a continuous function over the time space looks more reasonable than assuming the CO2 level is a scalar at individual time point. Therefore, a part from the traditional methods, we assume the observed data as functional data and use a functional data method to infer the dependent relationship between the functions and apply such a approach to the CO2 data collected from 2001 to 2021 in Korea. Also, we introduce forecasting method that can predict the CO2 level for a specific future time.

Study on Estimation of Typhoon Damage Thresholds in Coastal Region

Typhoon damage in coastal areas is a complex disaster characterized by heavy rain, strong winds, and turbulent waves. The strength of these strong winds and waves is determined by the intensity of the typhoon. In this study, a variable representing strong winds and waves was developed. A damage prediction model using rainfall, wind, and the developed variable was then proposed and thresholds for each variable were calculated across cities, counties, and districts located in coastal areas. Two damage prediction models were compared based on variables. Standard normal variables related to strong winds and rainfall by duration were calculated using Empirical Copula. The first logistic regression model was constructed using these variables. The second model used a multiple logistic regression model, considering strong winds and rainfall by duration. Although there are more cities, counties, and districts where the multiple logistic regression model exhibited better accuracy, there have been several instances where the typhoon damage induction threshold suggested by this model was abnormal. On the other hand, a relatively stable critical value was yielded using standard normal variables by Empirical Copula. This is because standard normal variables are calculated based on the probability according to the order of rainfall and strong wind magnitude. Hence, the critical value falls within the minimum and maximum range of observed values. According to the research results, it is possible to predict whether typhoon damage will occur in cities, counties, and districts located in coastal areas using the typhoon information provided by the Korea Meteorological Administration during a typhoon event, which can aid in preparing for typhoon disasters.

Vector Generalized Additive Models for Extreme Rainfall Data Analysis: A case study in South Korea

Extreme value theory (EVT) is the branch of statistics concerned with inference about the tails of the distribution function. Accurate estimation of the year return levels of climate extremes is a critical step in the projection of future climate and in engineering design for disaster response. The study of non-stationary behavior in the extremes is important to analyze data in environmental sciences, climate, finance, or sports. The largest order statistics is an extension of the block maxima approach that is used in extreme value modeling. However, in Korea, the scientific literature usually has used the method based on fitting a distribution to an annual maximum. We focus on daily rainfall amounts data sets collected from the Korea Meteorological Administration for Seoul, Busan and Daegu, from 1961 to 2020. First, we fit the GEV df to block maxima data, i.e., annual maximum rainfall. Second, we use the top 10 annual precipitation events. We model, in which both mean and scale parameters of extreme value distribution are modeled as linear or smooth functions of covariate, YEAR, using the vector generalized linear models (VGLMs) and vector generalized additive models (VGAMs).

A Study on Analyses of the Production Data of Feed Crops and Vulnerability to Climate Impacts According to Climate Change in Republic of Korea

According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change.

Lower tropospheric states revealed in high vertical-resolution radiosonde data in Korea and synoptic patterns for instability based on a self-organizing map

Seasonal and spatial characterizations of wind and stability in the lower troposphere below 1500 m are investigated using 1 s high-resolution operational radiosonde data observed at five stations (Baengnyeongdo, Heuksando, National Typhoon Center (NTC), Bukgangneung, and Pohang) in South Korea for 4 years (July 2016–June 2020), concentrating on four variables: horizontal wind speed (HWS), vertical wind shear (VWS), squared Brunt–Väisälä frequency (N2), and Richardson number (Ri). Significant seasonal variations are identified in these variables due to the heterogeneity of the geographical locations as well as seasonal variations in the synoptic patterns. VWS is high in spring and summer at two island stations on the west of the Korean Peninsula, Baengnyeongdo, and Heuksando, whereas it is high in winter at NTC, Bukgangneung, and Pohang, leading to frequent Kelvin–Helmholtz (KH) instability (KHI; 0 ≤ Ri < 1/4). Below z = 1000 m, static stability is lower in winter than in summer at all stations, resulting in a higher convective instability (Ri < 0) in winter, which is attributed to the inflow of the marine boundary layer. The frequent convective instability observed in spring at Bukgangneung suggests a potential relationship with mountain wave breaking and downslope windstorms. The microstructures of the lower tropospheric instabilities revealed in the current radiosonde observations are not properly represented in the high-resolution local numerical weather prediction and analysis system, namely local Data Assimilation and Prediction System (LDAPS) of Korea Meteorological Administration. In particular, the observed convective and KH instabilities, which are approximately 45% for the whole period, are rarely (approximately 15%) represented in LDAPS. The synoptic patterns favorable for the convective and KH instabilities cases are identified based on a self-organizing map clustering algorithm: (1) west-high and east-low patterns with extended Siberian high in winter, (2) strong low-pressure systems susceptible to KHI from spring to fall, (3) weak high-pressure systems with potential convective instability, accompanying northerly or north-westerly flows with cold advection at 925 hPa.

A Method for Predicting Termite Damage in Wooden Cultural Properties Using a Machine Learning Model

In this study, a machine learning model was established using termite damage and meteorological data on wooden cultural properties, and the prediction performance was evaluated. The data were divided into termite damage data, the location of wooden cultural properties, and meteorological data. Three observatories were searched based on the location of the wooden cultural properties, and meteorological data for about 8 years from 2010 to 2018 were combined to make a total of 491 data sets. As a result, it was confirmed that the value of small evaporation as a meteorological factor that directly affects termite damage is the time series independent variable that best explains the model, and showed an accuracy of 72.8% when the Linear SVM algorithm model was used. The value of small evaporation is the synoptic meteorological data of the Korea Meteorological Administration, and it is collected only at specific stations, not meteorological factors observed at all stations. Therefore, It is difficult to obtain enough data to make a predictive model. Since machine learning models can improve accuracy when the number of data is sufficient, prediction performance can be improved if more termite damage data and the value of small evaporation are obtained.

Association between non-optimal temperature and cardiovascular hospitalization and its temporal variation at the intersection of disability

BackgroundWhile previous research has identified populations susceptible to non-optimal temperatures, disability has been largely overlooked. Given the growing number of persons with disabilities (PwD) and their social and health disadvantages, understanding how disability intersects with temperature-related health effects is crucial. This study aimed to investigate the associations between non-optimal temperatures and cardiovascular disease (CVD) hospitalization and examine how these associations vary over time considering the existence of disability. MethodsWe used the National Health Insurance Service-National Sample Cohort to investigate the association between non-optimal temperatures and CVD hospitalization in South Korea, 2002–2019. We obtained daily mean temperature from the Korea Meteorological Administration's automated synoptic observing system. We applied a space-time-stratified case-crossover design using a conditional quasi-Poisson regression with a distributed lag non-linear model, adjusting for relative humidity, wind speed, and public holidays. We examined temporal variations in temperature-CVD hospitalization associations using a time window approach. All analyses used the minimum hospitalization temperature (20.0 °C) as reference and were stratified by disability status. ResultsThe cumulative exposure-response curve in persons without disabilities showed a J-shape with a relative risk (RR) of 1.07 (95 % confidence interval [CI]: 0.99, 1.15) at extreme heat (99th percentile) and 1.09 (95 % CI: 0.97, 1.23) at extreme cold (1st percentile). The cumulative exposure-response curve in PwD showed an M-shape with the highest RR at chill (1.22 [95 % CI: 1.13, 1.32]) and moderate cold temperature (1.11 [95 % CI: 1.01, 1.21]), defined as the 30th and 5th percentiles, respectively. The impacts of heat and cold decreased over time for persons without disabilities but increased for PwD. ConclusionsOur study found differential temperature-related impacts on CVD hospitalization based on disability status, and PwD were maladapted to heat and cold over time. This suggests the importance of considering disability when investigating temperature-related health disparity and adopting disability-inclusive adaptation strategies.

Development of Alarm Grit Ratio for Drought Forecasting and Its Application to the 2021–2023 Drought

The frequency and severity of droughts are increasing due to climate change and environmental disturbances brought on due to human development, making drought warning an essential tool in mitigating their effects. Drought warning can, however, be easily overlooked if people disregard its significance or repress water managers with the result of the drought warning. Therefore, in the current study, we developed an Alarm Grit Ratio (AGR) for drought warning to assess how active these have been thus far, and was estimated the ratio for the current drought in Korea from 2021 to 2023. The Korea Meteorological Administration issued three drought warnings, which were classified as agricultural, meteorological, and residential and industrial droughts. We proposed two indices to indicate how an institution issues a drought warning as a forecast for the actual drought warning that follows. These indices were AGR and Transitional Alarm Grit Ratio (TAGR). AGR measures how frequently an institute issues a drought warning forecast based on the number of actual warning conditions. Analysis of the current drought (2021–2023) indicates the issuance of warnings, while TAGR indicates the ratio of the drought warning issuance at each drought. The AGR and TAGR for the agricultural and meteorological droughts indicate underissuance of drought warnings, whereas warnings for residential and industrial droughts were appropriately issued due to the drought impact on residences and industries. Overall, we concludes that AGR and TAGR can be useful indicators of how actively an institution issues drought warnings. An institution in charge of drought warning can track these measurements and control the frequency of drought warnings by including AGR and TAGR data.

Night icing prediction using vehicle-mounted pavement temperature sensor and atmospheric data

Faced with high accident rates on ice-covered roads at night, notifying road managers of the most dangerous locations would allow them the opportunity to treat the icy roads by applying de-icing chemicals. In addition, drivers can be better prepared for the impending danger. In this regard, this study developed a web-based night icing potential information system and a deep learning-based night icing forecasting model. Input data included pavement temperature, collected by patrol cars operating between 23:00 and 07:00 on a daily basis in the winter season, and atmospheric data provided by the Korea Meteorological Administration. For evaluating the forecasting model, baseline data were generated based on the physical principle that ice is formed when pavement temperature is negative and lower than the dew point temperature. The forecasting model was assessed using pavement temperature and atmospheric data obtained on a 40 km stretch of a rural highway, passing through a mountainous region in South Korea. As a result, the model showed favorable performances, with 94% and 90% accuracies for bridge and roadway segments, respectively. Considering the increasing emphasis on preventive maintenance, the developed forecasting model can be applied to a preventive anti-icing measure, in turn, increasing traffic safety on winter roads.