The Korea Meteorological Administration has employed the Asian Dust Aerosol Model 2 (ADAM2) to forecast Asian dust events since 2010, where the dust emission flux is proportional to the fourth power of the friction velocity. Currently, the dust emission reduction factor (RF) is determined by the normalized difference vegetation index (NDVI). This study aims to improve the forecasting capability of ADAM2 by developing a daily dust RF using both monthly (January 2007 to December 2016) and real-time moderate resolution imaging spectroradiometer (MODIS) NDVI data. We also developed a look-up table to transform the RF using NDVI and a system to update the RF by producing MODIS NDVI data for the last 30 days. Using these data, new RFs can be produced every day. To examine the impact of RF modification, the current (CTL) and new (EXP) RFs are compared during the period from March to May 2017. The simulations are verified by ground-based PM₁₀ observations from China and Korea. Accordingly, root mean square errors (RMSEs) are reduced by 11.58% when RF is updated using real-time NDVI data. The results suggest that recent daily NDVI data contribute positively to the forecasting ability of ADAM2, in the dust source and downwind regions.

Meteorological information at an arrival airport is one of the primary variables used to determine refueling of discretionary fuel. This study evaluated the economic value of terminal aerodrome forecasts (TAF), which has not been previously quantitatively analyzed in Korea. The analysis data included 374,716 international flights that arrived at Incheon airport during 2017–2019. A cost–loss model was used for the analysis, which is a methodology to evaluate forecast value by considering the cost and loss that users can expect, considering the decision-making result based on forecast utilization. The value was divided in terms of improving fuel efficiency and reducing CO₂ emissions. The results of the analysis indicate that the annual average TAF value for Incheon Airport was approximately 2.2 M–20.1 M USD under two hypothetical rules of refueling of discretionary fuel. This value is up to 26.2% higher than the total budget of 16.3 M USD set for the production of aviation meteorological forecasts by the Korea Meteorological Administration (KMA). Further, it is up to 10 times greater than the 2 M USD spent on aviation meteorological information fees collected by the KMA in 2018.

To downscale artificial intelligence (AI) medium-range forecasts over the Korean Peninsula, we developed a 1.5 km CorrDiff system trained with ERA5 inputs and UM-LDAPS analyses as targets. For 1.5 m temperature and 10 m wind, the regression stage reduced root-mean-square error (RMSE) and root-mean-square vector error (RMSVE) relative to ERA5 and increased spatial correlation. CorrDiff had smaller absolute biases and greater spatial variability than Regression but higher RMSE and RMSVE and lower spatial correlation. The most consistent improvement relative to Regression occurred for high-temperature events at or above the target 99th percentile, with higher probability of detection (POD) and critical success index (CSI) and lower conditional temperature error. Without additional training or correction, AI Forecasting System (AIFS) forecasts were downscaled through +288 h. In a July 2026 heatwave case at 47 stations, CorrDiff reduced temperature errors and alleviated the underprediction of high-temperature occurrence relative to Regression. CorrDiff had higher POD and CSI than the AIFS for temperatures ≥33 °C but higher RMSE and lower spatial correlation. Generative downscaling should therefore be evaluated for spatial variability, spatial agreement, and event-specific performance beyond deterministic errors; producing high-resolution data does not necessarily imply improved forecast performance against observations.