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A 307-m tall meteorological tower was used to evaluate meteorological observation data obtained using a rotary-wing unmanned aerial vehicle (UAV). A comparative study between the tower and UAV observations was conducted during the daytime (06:00 to 19:00 local time (LT)) in the summer of 2017 (16–18th August). Hourly vertical profiles of air temperature, relative humidity, black carbon (BC), and ozone (O3) concentrations were obtained for up to 300 m height. Statistical metrics for evaluating the accuracy of UAV observations against the tower observation showed positive (potential temperature) and negative (relative humidity) biases, which were within acceptable ranges. The daytime evolution of the lower atmospheric boundary layer (ABL) was successfully captured by the hourly UAV observations. During the early morning, a large vertical slope of potential temperature was observed between 100 and 140 m, corresponding to the stable ABL height. The large vertical slope coincided with the large differences in BC and O3 concentrations between altitudes below and above the height. The transition from stable to convective ABL was observed at 10–11 LT, indicated by the ABL height higher than 300 m in the convective ABL. Finally, we provide several recommendations to reduce uncertainties of UAV observation.

Nitrous oxide (N2O) is a powerful greenhouse gas and is the largest remaining anthropogenic source of stratospheric ozone-depleting substances as halocarbons return towards preindustrial levels. To verify the N2O emission inventory using inverse analysis, precise and reliable measurements are necessary. In this study, we compared the conventional gas chromatography with the microelectron capture detector method (GC-μECD, Agilent 7890A) with advanced off-axis integrated cavity output spectroscopy (OA-ICOS, Los Gatos, EP-30) for atmospheric N2O measurements at the Jeju Gosan Suwolbong Station (JGS, 126.16° E, 33.30° N, 71.47 m a.s.l) in South Korea. The measurement uncertainties from linearity, repeatability, and reproducibility derived from the two instruments were compared. The values derived from GC-μECD were 2.4 to 8.7 times greater than that of OA-ICOS in all factors at the station. Since these factors affect the measurement quality, the calibration strategy should be well-established to reduce the measurement uncertainty. These uncertainties resulted in biases from the measurement of atmospheric N2O. The parallel inter-comparison experiment was implemented at JGS for 22 months, and the difference in atmospheric N2O was 0.17 ± 0.9 ppb between the two instruments. The significant differences were observed in the nonlinear range of the GC-μECD. Finally, these differences resulted in the over/underestimation of N2O characteristics locally and seasonally. Overall, OA-ICOS has a more robust performance with a lower measurement uncertainty than GC-μECD. Based on this study, we also suggest a calibration strategy for both instruments to achieve precise N2O measurements.

The springtime dust events in Northeast Asia pose many economic, social, and health-related risks. Statistical models in the forecasting of seasonal dust events do not fully account for environmental variations in dust sources due to climate change. The Korea Meteorological Administration (KMA) recently developed the GloSea5-ADAM, a numerically based seasonal dust forecasting model, by incorporating the Asian Dust and Aerosol Model (ADAM)’s emission algorithm into Global Seasonal Forecasting Model version 5 (GloSea5). The performance of GloSea5 and GloSea5-ADAM in forecasting seasonal Asian dust events in source (China) and leeward (South Korea) regions was compared. The GloSea5-ADAM solved the limitations of GloSea5, which were mainly attributable to GloSea5′s low bare-soil fraction, and successfully simulated 2017 springtime dust emissions over Northeast Asia. The results show that GloSea5-ADAM’s 2017 and 2018 forecasts were consistent with surface PM10 mass concentrations observed in China and South Korea, while there was a large gap in 2019. This study shows that the geographical distribution and physical properties of soil in dust source regions are important. The GloSea5-ADAM model is only a temporary solution and is limited in its applicability to Northeast Asia; therefore, a globally applicable dust emission algorithm that considers a wide variety of soil properties must be developed.