Climate change has altered the frequency, intensity, and timing of mean and extreme precipitation. Extreme precipitation has caused tremendous socio-economic losses, and severe impacts on human life, livelihood, and ecosystems. In recent years, heavy rainfall events occurred during the boreal summer (June-to-August) frequently and sporadically over South Korea. Given that its severity, a call for an urgent investigation of summer extreme rainfall is needed. Although many previous studies have addressed daily extreme precipitation, hourly extreme rainfall still needs to be thoroughly investigated. Therefore, in this study, we investigated the trends, spatio-temporal variability, and long-term variations in mean and extreme precipitation over South Korea during the boreal summertime using daily and hourly observational data through various analysis methods. During the past 50 years (1973-2022), there has been a notable escalation in maximum hourly precipitation, although the boreal summer mean precipitation has increased only marginally. Regionally, an increase in mean and extreme rainfall occurred in the northern part of the central region and the southern coast of the Korean peninsula. Moreover, the increase in intensity and frequency of extreme precipitation as well as in dry day have contributed more to the total summer precipitation in recent years. Our findings provide scientific insights into the progression of extreme summer precipitation events in South Korea.

The National Institute of Environmental Research, under the Ministry of Environment of Korea, provides two-day forecasts, through AirKorea, of the concentration of particulate matter with diameters of ≤ 2.5 μm (PM) in terms of four grades (low, moderate, high, and very high) over 19 districts nationwide. Particulate grades are subjectively designated by human forecasters based on forecast results from the Community Multiscale Air Quality (CMAQ) and artificial intelligence (AI) models in conjunction with weather patterns. This study evaluates forecasts from the long short-term memory (LSTM) algorithm relative to those from CMAQ-solely and AirKorea using observations from 2019. The skills of the one-day PM forecasts over the 19 districts were 39-70% for CMAQ, 72-79% for LSTM, and 73-80% for AirKorea; the AI forecasts showed comparable skills to the human forecasters at AirKorea. The one-day forecast skill levels of high and very high PM pollution grades are 31-98%, 31-74%, and 39-81% for the CMAQ-solely, the LSTM, and the AirKorea forecasts, respectively. Despite good skills for forecasting the high and very high events, CMAQ-solely forecasts also generate substantially higher false alarm rates (up to 86%) than the LSTM and AirKorea forecasts (up to 58%). Hence, applying only the LSTM model to the CMAQ forecasts can yield reasonable forecast skill levels comparable to the operational AirKorea forecasts that elaborately combine the CMAQ model, AI models, and human forecasters. The present results suggest that applications of appropriate AI models can greatly enhance PM forecast skills for Korea in a more objective way.

In this paper, we have compared and contrasted competing and amplifying influences on the global and regional drivers, circulation and rainfall responses of the Asian monsoon under global greenhouse warming (GHG) and aerosol forcing, based on CMIP5 historical simulations. Under GHG-only forcing, the land warms much faster than the ocean, magnifying the pre-industrial climatological land-ocean thermal contrast and hemispheric asymmetry, warmer northern than southern hemisphere. A steady increasing warm-ocean-warmer-land (WOWL) trend has been in effect since the 1950's substantially increasing moisture transport from adjacent oceans, and enhancing rainfall over the Asian monsoon regions. However, under GHG warming, increased atmospheric stability due to strong reduction in mid-tropospheric and near surface relative humidity coupled to an expanding subsidence areas, associated with the Deep Tropical Squeeze (DTS, Lau and Kim, 2015b) strongly suppress monsoon convection and rainfall over subtropical and extratropical land, leading to a weakening of the Asian monsoon meridional circulation. The inclusion of aerosol emissions strongly masks WOWL, by over 60% over the northern hemisphere, negating to a large extent the rainfall increase due to GHG warming, and leading to a further weakening of the monsoon circulation, through increasing atmospheric stability, most likely associated with aerosol solar dimming and semi-direct effects. Overall, we find that GHG exerts stronger positive rainfall sensitivity, but less negative circulation sensitivity in SASM compared to EASM. In contrast, aerosols exert stronger negative impacts on rainfall, but less negative impacts on circulation in EASM compared to SASM.

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

The present paper designed to understand the variations in the atmospheric pollutants viz. PM, PM, SO, NO, and CO during the COVID-19 pandemic over eight most polluted Indian cities (Mumbai, Delhi, Bangalore, Hyderabad, Lucknow, Chandigarh, Kolkata, and Ahmedabad). A significant reduction in the PM (63%), PM (56%), NO (50%), SO (9%), and CO (59%) were observed over Major Dhyan Chand Stadium. At Chhatrapati Shivaji International Airport, a decline of 44% in PM and 50% in PM was seen just a week during the initial phase of the lockdown. Gaseous pollutants (NO, SO & CO) dropped up-to 36, 16, and 41%, respectively. The Air Quality Index (AQI) shows a dramatic change from 7% to 67% during observation at Chandigarh and Ballygunge during the inspection. Whereas, Ahmedabad, Worli, Income Tax Office, Talkatora, Lalbagh, and Ballygaunge have showed a significant change in AQI from 25.76% to 68.55%. However, Zoo Park, CST, Central School, and Victoria show relatively low variation in AQI in the range of 3.0% to 14.50% as compare to 2019 after lockdown. Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) analysis suggested that long range transportation of pollutants were also a part and parcel contributing to changes in AQI which were majorly coming from the regions of Iran, Afghanistan, Saudi Arabia, as well as a regional grant from Indian Gangatic plains and Delhi Non-capital region.

Concentrations of fine particulate matter smaller than 2.5 μm in diameter (PM) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM concentrations at 1-3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government's observations, constructing a long-term dataset covering the 2005-2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM concentrations. For the wintertime (December-January-February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March-April-May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM concentration and an extreme metric, i.e., seasonal number of high PM concentration days.

Climate change-induced weather changes have a sensitive impact on the clothing industry. Developing a predictive model for demand volatility caused by weather changes is necessary to allow a company to generate profit while reducing unnecessary resource use and greenhouse gas and wastewater emissions due to overproduction. This review compares and analyzes empirical clothing research papers published in the Republic of Korea since 2000 and examines research directions on the integration of clothing and weather and how weather information is utilized in the clothing industry. We summarize the impact of temperature, precipitation, wind, humidity, and other weather factors on sales. Specifically, the mixed results published in Korea were compared with previous international studies to find weather data and analysis methods. This study identifies the challenges in weather and sales-related studies and presents the scope of methodological improvements. Furthermore, the role of weather forecasting in the clothing industry's supply chain is proposed to respond to unpredictable weather patterns caused by climate change. The results of this review study should be considered that there is a limit to analyzing clothing sales in Korea only with weather factors because consumers' purchasing motives are very diverse.