Asia-Pacific Journal of Atmospheric Sciences最新文献

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.

Many operational forecasting centres of tropical cyclones (TCs) issue a static Cone of Uncertainty (COU) to convey the uncertainty associated with the forecast track. This COU is based on the climatological distribution of forecast position errors. The uncertainty information from an ensemble prediction system can help in producing a dynamic COU. The objective of the present work is to build a dynamic COU using multiple member forecasts from India’s National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Prediction System (NEPS-G), where the radius at each forecast is determined such that it includes 67% of the ensemble members. This dynamic COU is then compared against a static COU constructed using the fixed radii prescribed by the India Meteorological Department (IMD), which are derived from climatological forecast position errors of previous years. All TCs for the period 2019–2021 over the North Indian Ocean (NIO) have been considered in the present study. At shorter lead times (till 18 h), static probability circles are too small to capture most of the best tracks. The dynamic circles show higher detection rate than the static circles till at least 72 h forecast lead time. The static circles outperform the dynamic circles at longer lead times due to large errors in ensemble mean and inadequate ensemble spread. The dynamic circles, over both Bay of Bengal (BoB) and Arabian Sea (AS), perform better till 72-h lead time for the straight and recurving TCs. At longer lead times (84-h onwards), for BOB cyclones, static circles perform better but for AS cyclones, dynamic circles are slightly more effective. For storms with severe cyclonic and higher intensity, dynamic circles are more effective during 18 to 72-h forecasts. During the post-monsoon season for all lead times (except 120-h) best tracks lie within dynamic circles more often than static circles.

Monthly characteristics of temperature lapse rate (TLR) or gradient with latitude (TLR_Lat), longitude (TLR_Lon), and elevations (TLR_E) in Bangladesh were analyzed using 31 years (1980–2010) of monthly climate data from 28 stations, employing linear and multicollinearity models. TLR_Lat is shallower in the summer and steeper in winter, whereas TLR_Lon shows the opposite trend. Diurnal TLR_R monthly variations peak during the pre-monsoon season and are at their lowest during the monsoon months, aligning with synoptic weather patterns and variations in moisture, rainfall, cloud cover, pressure, and wind speed. Moisture-related variables (e_s LR, e LR, and Δe LR) positively correlate with TLRs, while R LR, P LR, W_s LR (excluding TLR_Lon), and C_c LR correlate inversely. Summer’s TLR_Lat and TLR_Lon changes are driven by the southwest monsoon, causing increased rainfall and cloud cover in the southern and eastern regions. The effects of orographic rainfall further steepen the TLR_Lon value in summer. In winter, steep TLR_Lat and shallow TLR_Lon are associated with cold, dry, westerly winds, reduced rainfall, and clear skies in the northern parts. Pre-monsoon months’ TLRs exhibit steep gradients, especially in longitude, attributed to disturbances, high humidity, and frequent thunderstorms in the northeast. Post-monsoon TLR_Lon and TLR_Lat are less pronounced than pre-monsoon due to decreased rainfall and reduced thermal forcing. Diurnal patterns exhibit shallower TLRs with latitude and elevation during the day, attributed to high humidity, cloud cover, and weak adiabatic mixing. The largest diurnal range occurs during the pre-monsoon months, influenced by high sensible heat flux, radiative cooling, and frequent thunderstorms, with the smallest range occurring in summer due to elevated moisture levels, monsoon rains, high latent heat flux, and dense cloud cover. These results offer valuable insights into thermal dynamics, in addition to hydroclimatic processes and their relationship to local and regional climate and topography for variation, contributing to future hydroclimatic modeling in this region.

Diagnosing the precipitation forecast biases in numerical weather prediction (NWP) is an important step and method to improve the accuracy of precipitation forecasts.This study utilizes deterministic precipitation forecast data from the ECMWF Integrated Forecasting System (IFS), along with precipitation observations from 2415 meteorological stations in China and ERA5 data, to analyze the characteristics and possible causes of precipitation forecast biases in the IFS over China. Methods include, such as Root Mean Square Error (RMSE), Mean Error (ME), Frequency Bias (FBias), forecast verification scores, and Integrated Water Vapor Transport (IVT) diagnostics. The main conclusions are as follows: (1) The precipitation forecast from the IFS exhibits a significantly high Fbias with a pronounced wet bias. The ME shows larger positive deviations under lower daily average precipitation, transitioning to negative errors as daily precipitation increases. The RMSE of precipitation demonstrates distinct spatiotemporal variations: higher values are observed during summer half-year compared to winter, and in southern regions versus northern areas. The influence of underlying surface topography on RMSE is clearly evident. (2)The RMSE and ME increase with increasing forecast lead time. The RMSE for 1200 UTC precipitation forecasts performs better than for 0000 UTC beyond 48 hours. (3) The Threat Score (TS) and Equitable Threat Score (ETS) scores for summer half-year precipitation forecasts are higher than in the winter half-year, with 1200 UTC initial forecast times being higher than 0000 UTC, indicating that the true forecast capability of 1200 UTC initial forecast times is better than for 0000 UTC. (4) The forecasts initialized at 0000 UTC exhibit overestimated southerly wind components and stronger vertical velocity, with larger standard deviations in both vertical motion and IVT. In contrast, the forecasts initialized at 1200 UTC show amplified easterly wind biases yet demonstrate smaller IVT discrepancies. These systematic differences in dynamical and moisture variables may be important reasons for the differences in precipitation forecasts between the two initialization times.

On May 22, 2020, Qiaotou Meteorological Station in Kaohsiung City of southern Taiwan recorded 415.5 mm of daily rainfall. This heavy rainfall event was driven by the Mei-yu front, a strong and moist southwesterly flow, a mesoscale convective system (MCS), and the complex terrain of southern Taiwan. The study indicates that on May 22, the low-level jet (LLJ) intensified to southwesterly flow strength, rapidly bringing abundant moisture to southwestern Taiwan, resulting in higher equivalent potential temperatures in the lower atmosphere. This study, through observational data analysis and case simulation, found that the first important cause of heavy rainfall in southwestern Taiwan is the presence of a strong southwesterly LLJ in an unstable atmospheric environment, which transports abundant moisture to the land in southwestern Taiwan. At the same time, the lifting mechanisms ahead of the Mei-yu front and low-level convergence over the ocean contribute to the intensification of the MCS. The second factor is Taiwan’s complex terrain effects, which functioned as a barrier to moisture transport and enhanced orographic lifting on the windward side, further intensifying the rainfall when combined with the strong southwesterly flow and MCS. Numerical simulations show that under the influence of complex terrain, mean precipitation over the southern section of Taiwan’s Central Mountain Range increases from north to south as elevation decreases, the average precipitation gradually decreases when the terrain is below 1100 m. For mountains between 2000 and 2500 m, maximum precipitation occurs on the windward slopes or in front of mountains, while rainfall and moisture sharply decrease on the leeward side. For elevations between 1000 and 1500 m, mean precipitation on the windward slopes increases, with the peak shifting closer to the mountain tops. Below 1000 m, mean precipitation decreases but aligns with the terrain, with the maximum near the peaks. At elevations below 600 m, leeward mean precipitation was above average.