This study investigates the sensitivity of seasonal climate simulation over China to horizontal resolution using the Weather Research and Forecasting (WRF) Model and the Regional Climate Model version 5 (RegCM5). Dynamical downscaling experiments are performed at horizontal resolutions of 10 km and 30 km for April–May (AM) and June–August (JJA) during 1992–2020, driven by the fifth generation ECMWF atmospheric reanalysis (ERA5) and evaluated against the China Meteorological Forcing Dataset version 2.0 (CMFD 2.0).The results show that increasing horizontal resolution consistently improves the simulation of near-surface air temperature, particularly in terms of spatial correlation and interannual variability, whereas precipitation exhibits a weaker and less robust sensitivity to grid refinement. The improvements are more evident in AM, while JJA precipitation remains highly uncertain and is often accompanied by enhanced wet biases at finer resolution, especially over South China and the Yangtze River basin. Analysis of climate extremes suggests that higher resolution generally improves the simulation of extreme events, although some regional biases persist. Overall, these results highlight both the benefits and limitations of increasing horizontal resolution in regional climate modeling and provide practical guidance for model configuration in seasonal climate applications over China.
Rice paddies, an important land use in East Asia, are known to influence land–atmosphere interactions through their standing water layer, but they are often simplified in many models. In this study, we implemented a rice paddy land surface scheme into the WRF model and evaluated its performance using high-resolution simulations over Boseong, South Korea, on 7 clear-sky days shortly after transplanting in June 2019. Validation was conducted with flux tower measurements, vertical profiles of temperature and humidity up to 300 m above ground level at Boseong, and AWS/ASOS observations. Three simulations—RICE (including heat storage in standing water), IRRI (saturated soil layers), and CNTL (default)—were performed and compared. Both RICE and IRRI generally improved estimates of latent and sensible heat fluxes and near-surface humidity compared to CNTL but showed less consistent improvement for near-surface air temperature and wind. Comparison between CNTL and IRRI shows that rice paddies induce surface cooling and increased humidity and reduce the growth of the planetary boundary layer (PBL). Inclusion of heat storage in the standing water layer in RICE delays an increase of sensible heat flux and growth of PBL during the daytime and increases near-surface air temperature at night compared to IRRI. Furthermore, moisture and cooling effects of the standing water layer over rice paddies are not confined to the paddies or the near-surface layer but extend into neighboring non-rice areas through horizontal advection and throughout the entire planetary boundary layer via turbulent mixing. This study suggests the importance of realistic representation of rice paddies in the WRF model for better weather forecasting.
Passive microwave observations have been actively assimilated into numerical weather prediction (NWP) systems due to their ability to provide atmospheric thermodynamic information even under cloudy conditions. However, the presence of ice crystals and large hydrometeors causes significant scattering of microwave radiances, leading to signals that differ substantially from clear-sky conditions. For this reason, these observations must be carefully distinguished from clear-sky signals to ensure the reliability of clear-sky data assimilation systems. Accurate cloud screening is critical for assimilation quality and forecast skills, yet existing scattering index algorithms often overestimate microwave scattering caused by large hydrometeors, particularly at large viewing angles or in the presence of low clouds. This study evaluates the performance of two widely used scattering indices and suggested a dual-index method that reduces false rejections by combining existing two scattering indices with optimized thresholds. The dual-index method effectively detected scattering-affected observations while retaining more non-scattering data. Furthermore, the method proposed in this study was implemented in the Weather Research and Forecasting (WRF) data assimilation system to assess its effectiveness on precipitation forecasting. The results demonstrated that assimilating observations at cloud edges and low-cloud regions increased atmospheric humidity, and secondarily modified the temperature, in the analysis fields. Precipitation was evaluated as a forecast response to these modified initial conditions: the forecast precipitation amount increased, reducing bias in precipitation prediction against observation. These findings emphasize the importance of accurately detecting scattering clouds for assimilation of microwave humidity-sounding observation over non-scattering atmosphere, ultimately improving short-range precipitation forecast through improved initial conditions.
This study examines long-term changes in quasi-stationary fronts (QSFs) over East Asia during boreal summer (1979–2023) using objectively identified fronts based on ERA5 reanalysis. Variations in QSF frequency, associated precipitation, and synoptic-scale atmospheric structure are analyzed, with emphasis on the contrast between early summer (ES; June-July) and late summer (LS; August-September). QSF frequency decreased during ES over the Korean Peninsula, eastern China, and southern Japan. During LS, QSF frequency increased significantly over the Korean Peninsula, increased non-significantly over southern Japan, and changed little over eastern China. QSFs account for approximately 25–60
This study investigates the sensitivity of simulated temperature to vertical grid spacing in the troposphere using medium-range forecasts with the Korea Integrated Model (KIM). To examine the impacts of finer vertical resolution, model performance was compared between configurations employing 91 and 137 vertical layers. Increasing the vertical resolution produces persistent cooling throughout the troposphere, with particularly strong signals in the lower and upper troposphere. This cooling cannot be explained solely by the ability to resolve an initial condition or the dynamical adjustments. Instead, physically generated cooling becomes apparent in the early stage of the simulation, indicating that physical parameterizations play the dominant role in generating the tropical cooling signal. A key physical structural change associated with enhanced vertical resolution is an increase in boundary layer height over oceanic regions, together with enhanced cloud-water and cloud-ice mixing ratios. Finer vertical grid spacing more effectively resolves the nonlinear potential temperature gradient near the boundary layer top, producing a colder and moister environment above the boundary layer and strengthening the low-level cloud-water and longwave radiative cooling. In the upper troposphere, increased ice mixing ratio is found to originate primarily from processes represented within the cumulus parameterization framework. However, the increase occurs regardless of whether convective activity strengthens or weakens, suggesting that the response is not controlled solely by enhanced convection intensity. Instead, the results indicate an important contribution from numerical discretization effects within the mass-flux-type cumulus parameterization under refined vertical resolution. These discretization effects promote the retention of more subgrid-scale condensates within convective plumes, leading to enhanced hydrometeors and radiative cooling throughout the troposphere. The findings indicate that higher vertical resolution does not necessarily guarantee improved forecast performance, emphasizing the need for careful evaluation and retuning of physical parameterizations when model resolution is modified.
