Asia-Pacific Journal of Atmospheric Sciences最新文献

Severe convective storms provide a significant weather threat in Northern Thailand, especially during the pre-monsoon season (March–May), however a comprehensive storm database has been unavailable. The absence of comprehensive records complicates the research of storm features, the validation of numerical weather models, and the enhancement of radar-based detection methods. To address this deficiency, we established a spatial-temporal database that integrates official report records with crowdsourced social media data, documenting 259 distinct storm days at the regional level and 478 documented instances at the province level across a decade (2015–2024). Temporal study indicated a distinct seasonal pattern, characterized by an escalation in storm frequency from early March to late April, followed by a reduction in late May. Interannual variability was apparent, with 2018 exhibiting anomalously low activity (6 storm days) in contrast to peak years in 2020–2021 (33–35 days). Statistical analysis revealed substantial disparities in storm occurrences between early and late March (p = 0.0073), while Mann-Whitney U tests demonstrated that 2018 had significantly less storms than the following years (p < 0.05). The majority of storms impacted individual provinces (141 occurrences), with a diminishing frequency as the spatial expanse expanded. GIS-generated spatial anomaly maps indicated provincial variations in storm frequency compared to the decadal average. An investigation of atmospheric sounding from ten exceptional multi-province occurrences revealed elevated CAPE values (1215–1876 J/kg) and negative Lifted Index values, indicating conditions favorable to severe convective development. The database differentiates between hail-producing and non-hail-producing storms, facilitating the detection of unique meteorological variables linked to each category. This research establishes a systematic framework for storm recording in areas with limited observational networks, hence enhancing early warning systems, disaster preparedness, and the validation of radar-based storm detection. Future applications encompass comparative research of hail and non-hail storm situations, as well as integration with Thailand’s national radar mosaic to improve severe storm identification.

KMA has launched a project to produce global ocean reanalysis dataset from 1993 to 2020 by using KMA operational system– Global Ocean Data Assimilation and Prediction System2-ReAnalysis (GODAPS2-RA). We test-run GODAPS2-RA for recent five years (2016-2020) before the project formally initiated. The reason for prioritizing this period is that the year of Global Seasonal Forecasting System (GloSea6) hindcast is only available until 2016, highlighting the immediate necessity to extend to more recent years. In this study, we analyzed this preliminary experiment and examined whether GODAPS2-RA is properly implemented. It is found that the differences of GODAPS2-RA against the other validation dataset are within acceptable range. Compared with KMA operation system, GODAPS2-RA shows the reduction of observation innovation for most variables, signifying an improvement. However, the number of sub-surface temperature and salinity profile observations assimilated in the GODAPS2-RA is less than that in the operational run, consequently it turns out that further examination on the collection and processing procedure of profile data is necessary. In addition, the experimental GloSea6 hindcast using initial conditions produced by GODAPS2-RA is further tested. The results reveal that there is little difference between this experiment and existing operational GloSea6 hindcast, which indicating that GODAPS2-RA can be applied to the operational GloSea6 hindcast as oceanic initial conditions. By supplementing and preprocessing observations more appropriately and by further investigating the preliminary results, we will improve GODAPS2-RA to make it perform better, and finally official production will begin for releasing to publics.

This study investigates the statistical and dynamical relationship between the El Niño-Southern Oscillation (ENSO) and winter surface air temperature (SAT) in Korea, using station observations and reanalysis data from 1920 to 2023. Historical SAT records are compiled from 7, 14, and 60 stations for 1920–1959, 1960–1972, and 1973–2023, respectively. Despite the statistically significant correlation (r = 0.28) between the Niño 3.4 index and winter SAT in Korea, ENSO alone explains only a limited amount of interannual variability. Classifying the SAT anomalies according to the ENSO phase (i.e., warm for El Niño and cold for La Niña), the Niño 3.4 index yields binary-classification accuracy of 0.68; however, about half of the correctly classified anomalies fall within ±0.5 standard deviations from the climatological mean. Also, composite circulation patterns based on ENSO phases differ structurally from those associated with actual SAT anomalies. A multiple linear regression analysis reveals that mid- to high-latitude climate variables, such as the East Asian winter monsoon, western North Pacific (WNP) sea surface temperatures (SSTs), and the Arctic Oscillation, exhibit stronger and more stable associations with Korean winter SAT than ENSO. Especially WNP SSTs show the largest standardized regression coefficients (> 5.0) to indicate their dominant role. This study suggests the need for integrated forecasting approaches that consider both the tropical and extratropical influences, rather than relying solely on ENSO signals for improving the accuracy of seasonal climate predictions and supporting adaptive risk management strategies for wintertime extremes in the Korean Peninsula.

The atmospheric aerosol properties near Iranshahr City in Southwest Baluchestan, Iran, were investigated using handheld Calitoo sunphotometer measurements, space-borne CALIPSO-CALIOP lidar recordings, and synoptic station data. The findings indicate the prevalence of dust aerosols throughout the year, with a pronounced presence during the summer season. The aerosol optical depth (AOD), as determined by Calitoo measurements at 540 nm between April and October 2022, peaked in July with a monthly-mean value of 0.61 ± 0.14. During this month, the Angstrom exponent reached 0.43 ± 0.19, providing further evidence of severe dust storm activities in the region. An analysis of the level 2 aerosol profile data obtained from CALIOP measurements during 2006-2023 has been presented. The monthly-averaged AOD at 532 nm obtained from CALIOP data has a bell-shaped distribution with a peak in July (0.92 ± 0.39). These results are in agreement with those measured by Calitoo. The vertical profile of the particulate depolarization ratio (PDR) at 532 nm confirms the dominance of dust at all altitudes. Moreover, the extinction coefficient spike near the surface explains why horizontal visibility decreases during dust events. Consistent with previous studies, our findings identify the dry bed of Hamun Lakes as the primary dust source for the Baluchestan region. Additionally, the dry bed of the Jazmurian ephemeral lake and parts of the Lut Desert appear to be other major dust sources, particularly affecting the western parts of Baluchestan, Iran. These latter sources should be studied in more detail.

Tropical cyclones (TCs) in the tropical western North Pacific and the east Asia subtropical westerly jet (EASWJ) are important circulation systems in the east Asia–western north Pacific region, and interactions exist between them. Through statistical analysis and numerical experiments, this study delved into the influence of thermal remote disturbances induced by TC on the EASWJ, and the mechanism has also been analyzed. Statistical results show that TC activities in the tropical western North Pacific can cause anomalous westerlies near Japan, and the EASWJ axis in the corresponding area shifts significantly toward the high-value area of anomalous westerlies caused by TCs. Results of numerical experiments with TC Maria verified the results of the statistical analysis, and further revealed that TC Maria, locating in the tropical western North Pacific, induced anomalous cold advection in the vicinity of mid-latitude Japan. This, in turn, caused anomalous descent, ultimately leading to both anomalous adiabatic heating and anomalous latent heat absorption. Consequently, these changes altered the temperature distribution in the East Asian mid-latitudes and resulted in anomalous meridional temperature gradients. Under the constraint of thermal wind balance, the zonal wind over East Asian mid-latitudes changed accordingly, causing a shift in the location of the high-value westerly, which corresponds to the anomalous meridional movement of the EASWJ axis. The findings of this study indicate that TCs can affect EASWJ by exciting remote thermal disturbances.

Applying assumptions about the optical properties of dust, particularly the refractive index (RI), introduces significant uncertainty in thermal infrared dust-retrieval algorithms. To address this, we present a tailored RI dataset (ERML 2025) for Asian dust, derived from long-term chemical composition measurements in South Korea. An enhanced algorithm was developed using this Asian dust RI and thermal infrared channels from the GK-2 A Korean geostationary satellite. This LUT-based algorithm integrates three methods for dust layer height estimation: the Unified Model (UM), the Asian Dust Aerosol Model 3 (ADAM3), and a fixed-height approach. Operational dust detection processes and consistent assumptions were applied to minimize confounding variables in sensitivity tests. Qualitative validation using GK-2 A RGB and IASI-LMD products showed strong alignment in some regions and notable mismatches elsewhere, likely due to dust detection performance. Quantitative comparisons were conducted using MODIS data. Sensitivity analyses demonstrated that the combined use of the updated algorithm and UM model improved the operational method in most cases. Results also indicated that the updated algorithm retrieved higher AOD values, attributable to the increased absorption in the new RI dataset. Furthermore, comparisons with widely cited RI datasets revealed that while the real part of the Asian dust RI showed similar trends, its imaginary part differed markedly in magnitude and shape—reflecting the variability in dust origins. This region-specific RI dataset will help reduce inconsistencies in future studies caused by using RI values from remote sources that may not accurately represent Asian dust characteristics.

The variation in the occurrence time of extreme temperature events is a key factor in some natural disasters. Analyzing and predicting these changes can help reduce disaster losses. Reanalysis data have extensive spatial coverage and long temporal records, making them a crucial data source for climate change research. However, their suitability for analyzing extreme temperature timing variation remains unclear. In this study, 11 temperature-related time indices, such as frost start date (SD0), were used to quantify the variation in extreme temperature events occurrence time, and 4 evaluation metrics were applied to assess the applicability of reanalysis data (ERA5-Land) in simulating these variations. Results show: (1) ERA5-Land effectively captured the spatial distribution of extreme temperature events occurrence time, which exhibited the same patterns of early and late occurrence time across different regions of China as observed data. (2) ERA5-Land effectively captured the interannual variation of extreme temperature events occurrence time. The trend simulated by ERA5-Land was consistent with observations. (3) ERA5-Land has good applicability in simulating the evolution of extreme temperature events occurrence time in China. However, its applicability varies across different regions and indices. Applicability was poor for SD0, growing season startdate (SD10), and length (GSL) in the Qinghai-Tibet region, and for summer day start date (SD25) in southern China. Caution is needed when using reanalysis data in these regions. The findings help to address gaps in current climate data validations and provide a reference for analyzing changes in the occurrence time of extreme temperature events.

In this study, we analyzed the spatiotemporal characteristics of heavy rainfall in South Korea over the past 10 years (2013–2022) using clustering methods on Hourly data from 398 observation stations. We derived 18 variables related to heavy rainfall to assess frequency and intensity over different accumulation times (1, 3, and 12 h). After optimizing stations (395), variables (17), normalization (Robust scaling), clustering method (K-means), and the number of clusters (4), we analyzed characteristics in terms of location, sub-seasonal variability, and diurnal variation among the clusters. In general, the detailed characteristics of the frequency and intensity of heavy rainfall in each cluster vary depending on the accumulation time. Cluster 1 (C1), located at most of inland areas excluding Gyeonggi-do, and C2, in the northern and Jeolla-do regions, have a wide range of occurrences but low heavy rainfall frequencies. Both clusters show relatively high frequencies in July and August and exhibit diurnal patterns with peaks in the early morning and afternoon. And C2 has double the frequency of heavy rainfall in July and August compared to C1. C3 is along the east and south coasts, showing peak frequencies and intensities in August and September with early morning diurnal peaks. C4, located in Jeju, Geoje, and Misiryeong, has the highest frequencies and intensities of heavy rainfall, peaking from June to September. C4, which is mainly located along the coast, has one early morning peak. The three observatories excluded from clustering, located in high-altitude areas of Jeju Island, experienced about four times more frequent heavy rainfall events than other clusters, but with slightly stronger intensity.

Hailstorms are relatively rare in the Philippines, and as such, they remain understudied despite their potential to cause significant damage to agriculture and property. This study investigates the spatiotemporal characteristics of hail events across the Philippines from 2006 to 2024, identifying key seasonal, regional, and meteorological patterns. Most hail events occurred during the pre-Southwest Monsoon season (March-May), when conditions are favorable for localized convection. While Luzon accounted for the majority of reported hail events, larger hailstones (≥ 3 cm) were more frequently reported in the Visayas and Mindanao, where weaker monsoonal influence allows localized convective activity to persist into the later months of the year. Hail occurrences were also predominantly observed during the afternoon hours, consistent with peak diurnal heating and convective cycles. To better understand the local dynamics of hail formation in the Philippine setting, a detailed analysis was conducted on a significant hail event on 08 May 2020 in Cabiao, Nueva Ecija (15.2289° N, 120.8729° E). This event produced hailstones exceeding 4 cm in diameter and is the largest documented in this study. Satellite and radar observations revealed deep convection with cloud tops exceeding 12 km over a ~ 40 km region. High reflectivity values (> 60 dBZ) and lightning flash densities confirmed the storm’s intensity. Numerical simulations using the Weather and Research Forecasting (WRF) model captured the spatiotemporal evolution of the hail event, with increasing instability, strong updrafts, and substantial surface moisture flux convergence. Microphysical analysis indicated dominant hail and graupel mixing ratios at mid-levels. Hydrometeor vertical profiles emphasized the critical role of mixed-phase processes in hail development. This work presents the first hailstorm simulation in the Philippines using the WRF model and offers new insights into hailstorm dynamics in tropical environments, supporting future improvements in local hail detection and forecasting.

Asian dust storms (ADSs), originating from the Gobi and Taklamakan deserts, have widespread impacts on air quality, climate, ecosystems, and public health across East Asia due to the large-scale aerosol transport. Accurate prediction of ADS is essential for developing effective mitigation strategies and reducing their public health and ecological repercussions. We investigated the impact of assimilating aerosol optical depth (AOD) from the Geostationary Environment Monitoring Spectrometer (GEMS) on predicting ADS and made a comparative assessment with Moderate Resolution Imaging Spectroradiometer (MODIS) AOD assimilation, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) interfaced with the Maximum Likelihood Ensemble Filter (MLEF). The ADS event, occurred from 10 to 14 April 2023, was selected for the prediction and assimilation experiments. Our results indicate that the AOD assimilation generally improves the AOD forecast fields, with a high temporal resolution (three times a day) of GEMS AOD leading to better performance than once-a-day MODIS AOD. Although more frequent assimilation of GEMS AOD did not result in the lowest mean bias (MB) or root mean square error (RMSE) in PM₁₀ validation, it still outperformed assimilation of once-a-day GEMS AOD. This highlights the importance of frequent assimilation, using GEMS AOD, for PM₁₀ simulations. These findings underscore the significance of observation frequency in improving ADS prediction and emphasize the critical role of geostationary satellite observations in regional prediction.