This study reevaluated the Chukwookee precipitation records of the Diary of the Royal Secretariat and The Record of Daily Reflections, which are highly regarded as unprecedentedly long continuous meteorological records in world history. I have reconstructed them called "Hanyang Rainfall Data Set" (1770~1907). This dataset focuses on increased rainfall during 'leap monthly years' compared to regular years. Based on the analysis of First Hanyang Rainfall Data Set, leap monthly years (1,273.4 mm) were +6.0% higher than the overall annual average (1,201.6 mm), and +9.8% higher than 'non-leap monthly years' (1,159.6 mm). Upon further review using Second Modern Rainfall Data Set (1908~2019, 112 years), leap monthly years (1,369.1 mm) showed a +3.2% increase compared to the overall annual average (1,326.9 mm), and a +5.1% increase compared to non-leap monthly years (1,302.6 mm), demonstrating a consistent trend. When consolidated over the longest span of 250 years in Third Seoul Rainfall Data Set (1770~2019), leap monthly years (1,316.1 mm) were found to be +4.6% higher than the overall annual average (1,257.8 mm), and +7.5% higher than non-leap monthly years (1,223.8 mm). Furthermore, examining annual rainy days, leap monthly years in "Hanyang Rainfall Table" (56.3 days) showed a +3.9% increase compared to non-leap monthly years (54.2 days), indicating an overall increase in rainy days during leap monthly years. The occurrence of such peculiar increases in rainfall during leap monthly years could be attributed to meteorological changes throughout the year, caused by the additional intercalary month. However, specific academic discussions require consultation and evaluation from the meteorological community.

The accurate prediction of Changma (Korean summer monsoon) onset and withdrawal dates is crucial for various sectors including agriculture, water resource management, and disaster prevention. This study applies 25 ensemble members from the operational Korean Integrated Model (KIM) to the Korea Meteorological Administration (KMA) Changma Index (CMI) to diagnose the forecast skill for the onset and withdrawal dates of the 2021 Changma, which marked as the third shortest period on record. The CMI, consisting of 200 hPa geopotential height and zonal wind variables around the Korean Peninsula, was used to compare reanalysis data and KIM's ensemble forecast data. While the CMI from individual ensemble members showed significant variability in predicting the Changma onset and withdrawal dates, the ensemble mean CMI accurately predicted the Changma onset date 12 days in advance with a one-day error margin, and also accurately predicted the Changma withdrawal date 9 days in advance. Detailed analysis of the variables constituting the CMI in KIM's ensemble forecast data indicated that variations in the 200 hPa geopotential height were particularly influential in determining the Changma onset and withdrawal dates. These results demonstrate that the ensemble mean forecast of KIM is more effective than individual ensemble member forecasts for predicting Changma onset and withdrawal dates, highlights the utility of KIM's ensemble forecast data and the effectiveness of using upper atmospheric variables (specifically 200 hPa geopotential height) for these predictions.

This study evaluates the performance of various soil moisture reanalysis datasets over the East Asian region to identify the most suitable product for climate and hydrological studies. The analysis includes land reanalysis products generated by the Noah, VIC, and Catchment land surface models (LSMs), driven by GLDAS2.0 near-surface atmospheric forcing, alongside MERRA2 and ERA5-land datasets. The 62 in-situ soil moisture measurements observed from 1980 to 2014 are used to validate the modeled data across the entire study period, while 58 of these measurements are used for the May to September (MJJAS) period. Results indicate that, when driven by the same atmospheric forcing, the Noah and Catchment models outperform VIC, and MERRA2 shows lower errors compared to ERA5-land. Seasonal soil moisture variability, primarily driven by the East Asian monsoon, peaks in September, with MERRA2 providing the most realistic simulation of seasonal phase and amplitude. Daily soil moisture variations are better captured by MERRA2 and ERA5-land than by GLDAS2.0-based products. Overall, MERRA2 emerges as the most reliable reanalysis dataset for evaluating both the climatological mean and variability of soil moisture in East Asia. Additionally, multi-model mean analysis reveals a long-term trend of drying soil moisture and enhanced land-atmosphere coupling in northern East Asia.

To simulate dispersion of atmospheric pollutants in urban areas, representative meteorological fields were calculated by classifying various meteorological data based on surface wind direction/speed and atmospheric stability obtained from the 5-year (2015~2019) record of ERA5 reanalysis data. Wind direction and speed were divided into 16 and 4 categories, respectively. Pasquill-Gifford (P-G) method is used to classify atmospheric stability into 3 categories for surface meteorological fields and Bulk Richardson number is used to classify atmospheric stability into 3 categories for vertical profiles. The atmospheric profiles of temperature, humidity, wind speed, and potential temperature for a given point (Seoul in this study) were grouped into the 192 (16 × 4 × 3) categories for each season. The classified atmospheric profiles represent the similarity of the group relatively well. These profiles can serve as input data for atmospheric dispersion modeling under various wind and stability conditions, providing more accurate and improved results. This approach ensures that vertical profiles accurately reflect the properties of surface data, enhancing correlation and reliability in simulation outcomes.

It has been widely documented how climate systems respond to net zero carbon emissions. While the reversibility of surface climate variables under CO₂ removal has been reported, tropopause height change has not been addressed. By using multi-model simulations where CO₂ concentrations are symmetrically ramped up and down, the present study investigates how zonal-mean temperature distribution and tropopause height respond to varying CO₂ pathway. During the ramp-up period, tropospheric warming and stratospheric cooling get strengthened, causing tropopause to rise in both the tropics and extratropics. Such changes are reversed during the ramp-down period as CO₂ concentrations are reduced. However, their recovery is slower, leaving the tropopause height at the end of CO₂ removal higher than its initial state. Such asymmetric response in tropopause height is mainly attributable to upper tropospheric rather than lower stratospheric temperature changes. This finding suggests that hysteresis behavior of climate systems to CO₂ removal may occur not only at the surface but also at the tropopause.

Reported tornado event occurred in Dangjin, Chungcheongnam-do at 0650~0730 UTC on 15 March 2019 was examined using Weather Research and Forecasting model with four nested domains (dx = 5, 1, 0.2, and 0.05 km). From synoptic analysis in ERA5 reanalysis data, eastward moving upper-level trough was developed rapidly in the Yellow Sea. Strong tropopause folding with Potential Vorticity Unit (PVU) higher than 1.5 PVU moved downward to 600-hPa level in mid troposphere following the trough over the location of tornado event. Under this large-scale background condition, surface front developed very intensively in the west coast of Korea along with strong low-level jet and moisture band at 850-hPa level. In domain 2, the maximum Bulk wind shear at Dorido point was 12.86 m s^-1. Cold front evolved to be a comma cloud with upper-level trough and strong bulk vertical shear near Dangjin, which include eastward moving gust front revealed as a strong horizontal temperature gradient and convergence near the surface. In domains 3 and 4, the local maximum value of the simulated vertical vorticity (24 × 10^-2 s^-1) with strong updraft (8.18 m s^-1) near the observed tornado event along with the surface gust front was found to be a possible area for tornadogenesis from the x-z and time-z cross-sections near the simulated tornado event. In the vorticity budget analysis, the local maximum of vertical vorticity during this event was generated significantly by tilting and stretching forcings.

We analyzed 3 radiosonde measurements in May at Yangpyeong (10 May 2019, 19 May 2023, and 10 May 2024), as conducted for the educational purpose. While the number of measurements is limited, we find interesting features using these measured data. First, the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis data at the same time at Yangpyeong show the similar vertical temperature profile to that of radiosonde measurements. Vertical profiles of wind speed and relative humidity from the MERRA-2 data also look similar to those of radiosonde measurements, but the consistency was not much guaranteed in the upper atmosphere (higher than 5 km altitude). Second, the temperature profile from the radiosonde measurement and MERRA-2 dataset at Yangpyeong (located in the east of Seoul) is very analogous to that from the radiosonde measurement at Osan (located in the south of Seoul). Considering that the straight distance between Yangpyeong and Osan is about 60 km, the consistency of temperature profile is remarkable. Vertical wind profile is also generally similar between two regions, but the gap becomes larger as the altitude goes up. While the vertical profile of relative humidity is somewhat different between two regions, the vertical profile of water vapor mixing ratio derived from the relative humidity is rather similar between two regions. This study shows that only small number of additional radiosonde measurements enable us much better evaluation of regional meteorology in a vertical scale.

The frequency and intensity of extreme weather events are increasing due to climate change, causing significant social and economic damage. Consequently, understanding the climatological atmospheric environment and trends in Korea is becoming important. This study analyzes diurnal and intra-seasonal variations of instability indices and environmental parameters (Inst_Inds) using rainy season (May to September) upper-air data from seven stations in Korea. We also analyzed trends in the mean of the Inst_Inds and the counts over the moderately unstable threshold (MUT). CAPE, LI, and TPW exhibit diurnal variation more unstable at 1200 UTC, while KI, SWEAT, and SSI show the most stable at 0600 UTC. Most Inst_Inds show stronger instability in July and August compared to May, June and September, indicating significant intra-seasonal variations. SWEAT and TPW generally show destabilizing trends regardless of station and month, whereas other indices do not exhibit consistent trends. CAPE and LI show destabilizing trends at Baengnyeongdo and Bukgangneung, but stabilizing trends at Osan, Gwangju, and National Typhoon Center. These trends are particularly strong in July and August, and at Baengnyeongdo and Bukgangneung stations, indicating increased instability in northern Korea. The tendency of MUT excess frequency varies from station to station in most instability indices, but TPW shows an increasing trend regardless of the station. The inconsistent trend of counts over MUT in Inst_Inds with the recent increase in frequency and intensity of heavy rain in Korea suggests the need to adjust thresholds that currently do not reflect changes in extreme precipitation characteristics.

This study analyzes and compares the results of airborne snowfall enhancement experiments conducted on November 25 and 28, 2019, as part of the International Joint Cloud Observation and Weather Control Experiment (IJCO-WCE) 2019 campaign. The objective was to assess the effects of experimental interventions on cloud precipitation patterns. To address the challenges in verifying artificial snowfall enhancement, this study proposes an innovative approach, utilizing a post-experiment zigzag flight path for in-situ observations. This approach allowed for detailed comparisons between affected and unaffected cloud regions. Precipitation was observed in the target area on the leeward side on November 25, whereas no precipitation was recorded during the November 28 experiment. We concluded that airborne snowfall enhancement is more effective when the lower atmosphere is unstable at the rear of a trough, as confirmed by changes in the distribution of precipitation particles in the clouds and on the ground. Two identical flight experiments were conducted using the KMA/NIMS atmospheric research aircraft, allowing detailed observations. Data collected from onboard cloud observation instruments and six ground stations facilitated detailed analyses of changes in the concentration and size distribution of cloud particles (e.g., supercooled droplets, ice crystals, and snow particles). The method of comparing particle sizes between clouds affected and unaffected by the experiments is used to verify the effectiveness of artificial snowfall enhancement techniques. This methodology could be widely adopted in future studies to improve our understanding of weather modification strategies.

This study aims to assess the performance of climate prediction systems around the world, and understand objective seasonal prediction skill of KMA's GloSea6. Using the 2023 hindcast verification values provided by the WMO Lead Centre for Long-Range Forecast (LC-LRF), we analyzed the skill in the global, East Asia, and European regions. The differences in prediction skill and RMSE between GPC (Global Producing Centers) were very small in this challenging area. Overall, GloSea6 showed the best ACC across variables and periods. Operating this outstanding climate prediction system not only ensures the provision of the best forecasting services but also offers excellent research and development tools. This result also suggests that seasonal forecasting requires different strategies against short- to medium-range forecast to account for climate prediction sources and reduce uncertainties. The skill differences between GloSea6-Seoul and GloSea6-Exeter, especially in high latitude, could be due to differences in snow and soil temperature initialization. Understanding these differences is important for future prediction system development. GPCs that use atmospheric only models instead of coupled, showed the limitations for seasonal predictions. Systems developed a relatively long time ago tended to perform low, suggesting that continuous improvements and upgrades are important. Among variables, SST showed the best prediction skill with the lowest RMSE. Temperature and pressure variables showed practical skill levels, around 0.5. We aimed to quantitatively assess the skills of climate prediction systems, and this assessment can guide the improvement and development of future systems and serve as a reference.

The New York State (NYS) Mesonet consists of 126 surface weather stations across the state with 17 of the sites also instrumented with active and passive profiler systems. The NYS Mesonet (NYSM) is the first and only state-run network in the USA, that includes a combination of surface stations, Doppler wind lidars (DWL) and thermodynamic profiles from Microwave Radiometers (MWR). NYSM's continuous and extensive observations from the surface to the lower atmosphere have a wide range of applications in air quality and human health, forecasting of severe storms, and predicting renewable energy production. This study provides results of assimilating the NYSM surface station data and the DWL wind profiles. The impact of NYSM observations on predictive skill is evaluated for one tornadic supercell case that has large uncertainties in analysis with respect to low-level temperature, moisture, and wind variability. Compared to forecasts assimilating solely conventional observations except NYSM, the additional assimilation of NYSM observations effectively corrects the cold and dry biases in central New York State, resulting in a more accurate representation of surface conditions. Notably, the assimilation of NYSM DWL wind profiles improves the prediction of the location and intensity of convective systems, thereby creating an environment that increases the likelihood of supercell and tornado formation.

Urban air mobility (UAM) is emerging as a solution to alleviate traffic congestion in urban areas. For the successful implementation and stable operation of UAM, acquiring high-resolution meteorological data, particularly wind, is essential. Despite the existence of various studies that have assessed meteorological downscaling systems, there is little research focusing specifically on the urban environment, where the dynamics of wind and weather patterns are more complex. In response to this need, our study advances and introduces a sophisticated downscaling system designed to facilitate high-resolution (100 m) wind speed simulations based on module in IMPROVER (Integrated Model post-PROcessing and VERification) from Met Office. This system takes into account intricate surface details, including orography, and the characteristics of urban landscapes are represented to enhance simulation accuracy and realism by incorporating the indirect effects of urban buildings. The system is quantitatively evaluated by Pearson correlation coefficient, root mean square error, and mean bias error, demonstrating better correlation and improved predictability relative to raw meteorological data. These results emphasize the significance of downscaling system specialized for urban areas, highlighting its contribution to reliable and realistic wind conditions.