A commercial aircraft, departing from Seoul to Jeju Island in South Korea, encountered a convectively induced turbulence (CIT) at about z = 2.2 km near Seoul on 28 October 2018. At this time, the observed radar reflectivity showed that the convective band with cloud tops of z = 6–7 km passed the CIT region with high values of spectral width (SW; larger than 4 m s^–1). Using the 1 Hz wind data recorded by the aircraft, we estimated an objective intensity of the CIT as a cube root of eddy dissipation rate (EDR) based on the inertial range technique, which was about 0.33–0.37 m^2/3 s⁻¹. Radar-based EDR was also derived by lognormal mapping technique (LMT), showing that the EDR was about 0.3–0.35 m^2/3 s⁻¹ near the CIT location, which is consistent with in situ EDR. In addition, a feasibility of the CIT forecast was tested using the weather and research forecast (WRF) model with a 3 km horizontal grid spacing. The model accurately reproduced the convective band passing the CIT event with an hour delay, which allows the use of two methods to calculate EDR: The first is using both the sub-grid and resolved turbulent kinetic energy to infer the EDR; the second is using the LMT for converting absolute vertical velocity (and its combination with the Richardson number) to EDR-scale. As a result, we found that the model-based EDRs were about 0.3–0.4 m^2/3 s⁻¹ near the CIT event, which is consistent with the estimated EDRs from both aircraft and radar observations.

An observed sea fog event over the Eastern Yellow Sea on 15–16 April 2012 was reproduced in the Weather Research and Forecasting (WRF) simulation with high-resolution to investigate the roles of physical processes and synoptic-scale flows on advection fog with phase transition. First, it was verified by a satellite-based fog detection algorithm and in situ observation data. In the simulation, longwave (infrared) radiative cooling (LRC) with a downward turbulent sensible heat flux (SHF), due to the turbulence after sunset, triggered cloud formation over the surface when warm-moist air advection occurred. At night, warm air advection with continuous cooling due to longwave radiation and SHF near the surface modulated the change of the SHF from downward to upward, resulting in a drastic increase in the turbulent latent heat flux (LHF) that provided sufficient moisture at the lower atmosphere (self-moistening). This condition represents a transition from cold-sea fog to warm-sea fog. Enhanced turbulent mixing driven by a buoyancy force increased the depth of the sea fog and the marine atmospheric boundary layer (MABL) height, even at nighttime. In addition, cold air advection with a prevailing northerly wind at the top of the MABL led to a drastic increase in turbulent mixing and the MABL height and rapid growth of the height of sea fog. After sunrise, shortwave radiative warming in the fog layers offsetting the LRC near the surface weakened thermal instability, which contributed to the reduction in the MABL height, even during the daytime. In addition, dry advection of the northerly wind induced dissipation of the fog via evaporation. An additional sensitivity test of sea surface salinity showed weaker and shallower sea fog than the control due to the decrease in both the LHF and local self-moistening. Detailed findings from the simulated fog event can help to provide better guidance for fog detection using remote sensing.

The accurate treatment of surface turbulent fluxes is crucial for understanding the exchange of heat and moisture, which plays an important role in precipitation processes. In the bulk formula for estimating surface turbulent fluxes in atmospheric models, the ocean surface is assumed to be saturated with pure water. This study addresses the impact of salinity on the simulated precipitation of a global numerical weather prediction model, along with related physical reasoning, by incorporating sea surface salinity into the saturated vapor pressure over the ocean. The revised saturated vapor pressure algorithm, in which the salinity is considered, decreases the latent heat flux over the ocean, particularly over low‐latitude regions, resulting in a 6.3% reduction in the root‐mean‐square deviation against the Objectively Analyzed Air‐sea Fluxes Project (OAFlux) data. The reduced transport of water vapor to the atmosphere suppresses the generation of low‐level clouds. This decrease in cloudiness leads to warming near the surface due to increased downward solar radiation and cooling in the lower troposphere due to a lower condensation rate, which enhances vertical turbulent mixing. The resulting dryness within the planetary boundary layer affects the buoyancy of a lifting parcel and contributes to suppressing the occurrence of convective precipitation. The skill score of the simulated precipitation for medium‐range forecasts is improved by 3% due to the reduction in light precipitation over tropical oceans.

This study presents a comprehensive evaluation of the El Niño–Southern Oscillation (ENSO) simulation characteristics in the newly established coupled Korea Integrated Model (coupled KIM), which is designed for extended-range prediction. To robustly diagnose ENSO behavior, a 171-year coupled simulation was conducted and systematically evaluated against eight CMIP6 models that share the Nucleus for European Modelling of the Ocean (NEMO) as their oceanic component. Benchmarking the CLIVAR 2020 ENSO metrics framework, we evaluate diverse aspects of ENSO, including its spatial pattern, intensity, skewness, seasonality, and teleconnections, within the context of the tropical Pacific mean state. The coupled KIM exhibits moderate to above-average performance across most key metrics. Notably, the model successfully mitigates the common systematic bias of an excessive westward extension of the ENSO sea surface temperature (SST) pattern. This improvement is linked to a warmer eastern Pacific mean state compared to other CMIP6 models. However, this warm bias leads to a weak zonal SST gradient and a relatively deeper-than-normal thermocline, which consequently underestimates the overall ENSO intensity. Furthermore, this eastern Pacific warm bias undergoes pronounced seasonal expansion during January–February, intimately synchronized with an amplified double ITCZ bias. This seasonal coupling further flattens the simulated ENSO seasonality, although the ratio of ENSO amplitude between winter and spring remains one of the most realistic among the evaluated models. The model also demonstrates outstanding skill in reproducing ENSO skewness, which is plausibly attributed to recent advancements in the vertical mixing processes of the NEMO component. Additionally, the coupled KIM realistically captures tropical precipitation anomalies and their teleconnection patterns, including the extratropical atmospheric responses in the Northern Hemisphere. These findings validate the current developmental trajectory of the coupled KIM and highlight its robust potential for reliable extended-range forecasting.