Advancements in numerical weather prediction (NWP) models have accelerated, fostering a more comprehensive understanding of physical phenomena pertaining to the dynamics of weather and related computing resources. Despite these advancements, these models contain inherent biases due to parameterization of the physical processes and discretization of the differential equations that reduce simulation accuracy. In this work, we investigate the use of a computationally efficient deep learning (DL) method, the convolutional neural network (CNN), as a postprocessing technique that improves mesoscale Weather Research and Forecasting (WRF) one-day simulation (with a 1-h temporal resolution) outputs. Using the CNN architecture, we bias-correct several meteorological parameters calculated by the WRF model for all of 2018. We train the CNN model with a four-year history (2014-2017) to investigate the patterns in WRF biases and then reduce these biases in simulations for surface wind speed and direction, precipitation, relative humidity, surface pressure, dewpoint temperature, and surface temperature. The WRF data, with a spatial resolution of 27 km, cover South Korea. We obtain ground observations from the Korean Meteorological Administration station network for 93 weather station locations. The results indicate a noticeable improvement in WRF simulations in all station locations. The average of annual index of agreement for surface wind, precipitation, surface pressure, temperature, dewpoint temperature, and relative humidity of all stations is 0.85 (WRF:0.67), 0.62 (WRF:0.56), 0.91 (WRF:0.69), 0.99 (WRF:0.98), 0.98 (WRF:0.98), and 0.92 (WRF:0.87), respectively. While this study focuses on South Korea, the proposed approach can be applied for any measured weather parameters at any location.

To quantify the impact of transported air pollutants on local air quality, this study applies an inverse modeling technique with tropospheric nitrogen dioxide column retrievals from TROPOspheric Monitoring Instrument, and the CMAQ‐ISAM model, which computes source attribution over six regions of interest in spring 2019 over East Asia. Satellite‐constrained nitrogen oxides (NO_x) emissions successfully improve model performance by significantly reducing the normalized mean bias of concentrations of NO_x, Ozone (O₃), and PM_2.5 from most regions in China and South Korea. Anticyclones over the Yellow Sea transport air pollutants and increase the contribution of emissions from adjacent regions to local air quality. Fluxes of air pollutants with posterior emissions showed notable enhancements in their magnitude by 3%–24% for NO_y, O₃, and PM_2.5. The contributions of emissions from neighboring regions on local NO_x and O₃ budgets in the planetary boundary layer were 22.96%–35.24% and 24.23%–42.26%. The contribution of emissions from North Korea was notable despite its comparably lower emission rates. Regarding the impact of external influences on local air quality, the enhanced chemical loss of NO_x compensated for increases by transport. Also, the increased vertical gradient of maximum daily 8‐hr average O₃ amplified vertical transport and dry deposition processes in the opposite direction along with slight changes in the chemical net O₃ production rate. This study shows the nonlinear response of local air quality to changes in the transport of air pollutants, which could be further investigated concerning changes in emissions and climatological factors that would modify the inflow of air pollutants.