Air pollution is a serious challenge in South Korea and worldwide, and negatively impacts human health and mortality rates. To assess air quality and the spatiotemporal characteristics of atmospheric particulate matter (PM), PM concentrations were compared with meteorological conditions and the concentrations of other airborne pollutants over South Korea from 2015 to 2020, using different linear and non-linear models such as linear regression, generalized additive, and multivariable linear regression models. The results showed that meteorological conditions played a significant role in the formation, transportation, and deposition of air pollutants. PM_2.5 levels peaked in January, while PM₁₀ levels peaked in April. Both were at their lowest levels in July. Further, PM_2.5 was the highest during winter, followed by spring, autumn, and summer, whereas PM₁₀ was the highest in spring followed by winter, autumn, and summer. PM concentrations were negatively correlated with temperature, relative humidity, and precipitation. Wind speed had an inverse relationship with air quality; zonal and vertical wind components were positively and negatively correlated with PM, respectively. Furthermore, CO, black carbon, SO₂, and SO₄ had a positive relationship with PM. The impact of transboundary air pollution on PM concentration in South Korea was also elucidated using air mass trajectories.

Previous studies have shown that the acceleration of global warming will increase the intensity of rainfall induced by tropical cyclones (TCs) (hereinafter referred to as “TC-induced rainfall”). TC-induced rainfall is affected by TC position and topography (slope shape and direction). Thus, TC-induced rainfall is expected to vary by sub-basin due to varying topographies. However, these relationships have not been explained, as historical TCs, which occurred several decades earlier, do not exhaustively encompass all TC positions that could potentially affect each basin. We used large ensemble regional climate model experiments with 5 km grid spacing, which enabled us to prepare a huge TC database for understanding the characteristics of TC-induced rainfall over sub-basins. We quantified the characteristics of TC-induced rainfall (rainfall volume, relationship between TC position and rainfall intensity, and contribution of TC intensity on rainfall) over four sub-basins in the Tokachi River basin, central Hokkaido, northern Japan. The results reveal differences in TC-induced rainfall characteristics between the sub-basins. In addition, the large ensemble data under a future climate scenario were used to evaluate future changes in the characteristics of TC-induced rainfall for each sub-basin.

Pakistan and northwestern India have frequently experienced severe heavy rainfall events during the boreal summer over the last 50 years including an event in late July and early August 2010 due to a sequence of monsoon surges. This study identified five dominant atmospheric patterns by applying principal component analysis and k-means clustering to a long-term sea level pressure dataset from 1979 to 2014. Two of these five dominant atmospheric patterns corresponded with a high frequency of the persistent atmospheric blocking index and positive sea level pressure over western Russia as well as an adjacent meridional trough ahead of northern Pakistan. In these two groups, a negative sea surface temperature anomaly was apparent over the equatorial mid- to eastern Pacific Ocean. The heavy precipitation periods with high persistent blocking frequency in western Russia as in the 2010 heat wave tended to have 1.2 times larger precipitation intensity compared to the whole of the heavy precipitation periods during the 36 years.

The shortage of extreme rainfall data gives substantial uncertainty to design rainfalls and causes predictions for torrential rainfall to deviate strongly from adopted probability distributions used in river planning. These torrential rainfalls are treated as outliers which existing studies do not evaluate. However, probability limit method test which its acceptance region expresses with high accuracy the range where observed ith order statistics could realize. Confidence interval which quantifies uncertainty of adopted distributions can be constructed by assuming that these critical values in both sides of the adopted region follow the same function form applied to actual observed data. Furthermore, its validity is proved through comparison of confidence interval derived from ensemble downscaling calculations. In addition, these critical values are almost in accordance with outliers in samples from the ensemble downscaling calculations. Therefore, prediction interval which expresses the range that an unknown observed datum can take is constructed by extrapolating the critical values for limit estimation of a future datum. In this paper, quantification method of uncertainty of design rainfall and occurrence risk of outliers in the traditional framework, using the proposed confidence interval and prediction interval, is shown. Moreover, their application to future climate by using Bayesian statistics is explained.