Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for highresolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of convective cell, mesoscale convective cell, diagonal squall line, and parallel squall line, was adopted to categorize MCS storm cells, following the fuzzy logic algorithm for storm tracking, over the Korean Peninsula. The relationships between rain rates and over 15 variables derived from data products of dual-polarimetric weather radar were investigated and quantified via 5 ML regression methods and a permutation importance algorithm. As an applicational example, ML classification models were also developed to predict locations of storm cells. Recalibrated ML regression models with identified pertinent predictors were coupled with the ML classification models to provide early warnings of heavy rainfall. Results imply that future work needs to consider MCS type information to improve ML modeling for nowcasting and early warning of heavy rainfall.

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Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Robotic Network (AERONET) produced nighttime AOD, called lunar AOD, providing an opportunity to estimate nighttime PM. Nighttime AOD measurements are particularly important as they help fill gaps in our understanding of aerosol variability and its impact on the atmosphere, as there are significant variations in AOD between day and night. In this study, the relationship between lunar AOD and PM was investigated using data from AERONET station, meteorological station, and air pollution station in Seoul Metropolitan area from May 2016 to December 2019, and then PM estimation model was developed covering both daytime and nighttime using random forest machine learning techniques. We have found the differences in the importance of variables affecting the AOD-PM relationship between day and night from the random forest model. The AOD-PM relationship in the daytime was more affected by time-related variables, such as the day of the year among the variables. The new model was developed using additional lunar AOD data to estimate continuous PM concentrations. The results have shown that the model based on lunar AOD data estimated well PM10 and PM2.5 with similar performance of model using solar AOD. The results imply the possibility of seamless near-surface PM concentration data on a large scale once satellites produce nighttime AOD data.