Asia-Pacific Journal of Atmospheric Sciences最新文献

Atmospheric fine particulate matter (PM_2.5) is one of the most significant air pollutants posing a threat to human health and the environment. Investigating its water-soluble ions is both essential and urgent. From January to December 2022, continuous monitoring of PM_2.5and its components was conducted using the Urumqi Atmospheric Environment Super Station. Nine water-soluble ions in PM_2.5 were analyzed using ion chromatography (IC) and other instruments, and their sources were identified through principal component analysis and the PMF model.The results revealed that the annual average PM_2.5 concentration in 2022 was 60.40 μg m⁻³. During both the heating period and the Spring Festival, PM_2.5 levels exceeded 75 μg/m³, while the non-heating period exhibited relatively lower concentrations, averaging 16.88 μg m⁻³. The primary water-soluble ions in PM_2.5 were SO₄²⁻, NH₄⁺, and NO₃⁻, accounting for 24%–55%, 11%–38%, and 10%–25%, respectively. These three ions constituted 90.5% of the total mass concentration of water-soluble anions and cations. The strongest correlations were observed between NH₄⁺ and SO₄²⁻ (r = 0.948) and NH₄⁺ and NO₃⁻ (r = 0.937).The concentrations of secondary inorganic aerosols (SNAs) during the non-heating and heating periods were (31.31 ± 4.95) μg m⁻³ and (79.31 ± 46.31) μg/m³, representing 68.17% and 80.53% of the total water-soluble ions, respectively. Additionally, the metal elements Sb, As, Cd, Pb, and Ti were found to be highly enriched. In summary, the primary sources of water-soluble ions in PM_2.5 originate from secondary aerosol formation, combustion processes, and fugitive dust emissions. Meanwhile, the concentration of PM2.5 components continues to exceed the national secondary air quality standards, necessitating intensified regional environmental governance measures.

In this study, the effects of cloud seeding experiments were analyzed using ensemble numerical modeling. This study focuses on an aircraft seeding experiment conducted over the East Sea near the Yeongdong region of Gangwon Province on October 4, 2022. The weather research and forecasting (WRF) model was applied with parameterization to reflect the effects of hygroscopic seeding materials. The particle size distribution of domestically produced sodium chloride (NaCl) powder was measured and incorporated into the model. Fifty ensemble members (seeding start time legs) were constructed to calculate the probability of seeding-induced precipitation, which was then used to analyze the precipitation efficiency. The results showed that seeding materials were primarily dispersed to the Yeongdong and Yeongseo regions of Gangwon Province due to northeasterly winds. The 6-h (14:00–20:00 KST) cumulative simulated precipitation enhancement was 2.7, 4.4, and 0.9 mm at Bukgangneung (BGN), Gangneungseongsan (GNSS), and Daegwallyeong (DGY), respectively. Analysis of the precipitation ion components confirmed a distinct increase in seeding material-related ions at the BGN site, corresponding to 98% probability of seeding-induced precipitation, as per ensemble-based analysis. Areas with a high probability of seeding-induced precipitation exhibited increased precipitation, with an efficiency of 19.63% (median) and 23.50% (mean) in the 100% probability zones. The highest precipitation efficiency occurred at altitudes of 1000–1200 m above sea level, aligning with the seeding altitude (approximately 1.5 km above sea level) and cloud formation height.

Accurate seasonal climate forecasts are vital for regions like Cambodia's Northern Tonle Sap Basin (NTSB), where agriculture is closely tied to rainfall patterns. While most studies have focused on the TSB, the northern areas, crucial contributors to Cambodia's national food basket, have remained largely unstudied. Here, this gap is addressed by evaluating the performance of 8 state-of-the-art seasonal forecast models from the Copernicus Climate Change Service (C3S) over a 24-year hindcast period (1993–2016). The evaluation is bolstered by ground-based data from 38 agrometeorological stations. Among the models, the Ensemble, the Japan Meteorological Agency (JMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model emerged as top performers, with the Ensemble particularly excelling in replicating both temporal and spatial precipitation patterns, making it invaluable for agrometeorological applications. The Ensemble demonstrates particularly strong performance in regions such as western Oddar Meanchey and eastern Preah Vihear, where biases are less than 5%. To tailor the Ensemble to the specific climatic and geographic context of the NTSB, we refined it using the Delta Change technique, and this reduced biases even further to < 1%. Our study not only contributes to improving the precision of agrometeorological advisories in a key, but under-researched region, but also sets a precedent for how regional climate forecasting can be enhanced through context-specific model evaluations and corrections. These findings provide a practical framework for supporting resilient agricultural strategies in areas vulnerable to climate change, bridging a critical gap between climate science and agricultural practice.

Using long-term rawinsonde observation data collected from nine stations, we obtained the climatology of low-level jets (LLJs) over Korea, including occurrence frequency, altitude, wind direction, and wind speed. The characteristics (frequency, altitude, speed, direction) of LLJ occurrence on the Korean Peninsula show unique spatiotemporal variations. At stations located on the west coast (Baengnyeongdo and Heuksando), LLJ frequency was high from April to May (approximately 40%) and low in winter (approximately 15%). The station on the northeastern coast (Sokcho) displayed a double-peak pattern in LLJ frequency (approximately 30%), with peaks occurring from April to May and July to August. The inland areas (Gwangju and Osan) showed significantly lower LLJ occurrence frequencies than the coastal stations. In contrast, the southeastern coast (Pohang) and Jeju Island exhibited high occurrence frequencies (30–50%) throughout the year, unlike other stations where LLJs rarely occur even in winter. The altitude at which LLJs primarily occur is low (concentrated below 500 m) at the west coast stations and higher (evenly distributed up to 3 km) at the east coast stations. The wind directions of LLJs at the west coast and inland stations exhibited seasonal changes, being southerly in summer and northerly in winter, which were attributed to monsoon. In contrast, the east coast (Sokcho and Gangneung) consistently showed westerly wind LLJs throughout the year. LLJ wind speeds ranged from 13 to 20 m/s, with the strongest winds occurring in the northern part of the east coast (Sokcho and Gangneung).

This study aims to investigate the temporal and spatial variations in lightning activity and its association with key meteorological parameters across the northwestern Indian Himalayan region and the Indo-Gangetic Plains from January to June 2022. The analysis utilizes high-resolution datasets from the International Space Station-Lightning Imaging Sensor (ISS-LIS) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA-5). The results reveal a pronounced peak of 1,858 lightning flashes in May, following a gradual increase from 36 flashes in March. Lightning activity was predominantly concentrated between elevations of 829 m and 3,200 m, with the normalized lightning flash count peaking at 2,410 m. Spatially, the foothills of the Himalayas and the Indo-Gangetic Plain exhibited the highest lightning concentrations. Meteorological analysis demonstrated that Convective Available Potential Energy (CAPE) and Sensible Heat Flux (SHF) showed strong positive (r = 0.62) and moderate positive (r = 0.56) correlations, respectively, with lightning activity. Seasonal patterns indicated a peak in lightning activity during the pre-monsoon season, attributed to elevated CAPE (135.79 J/kg) and SHF (72.38 W/m²). In contrast, the monsoon season experienced reduced lightning activity despite higher CAPE (269.10 J/kg) and SHF (109.42 W/m²), likely due to the cooling effects of increased rainfall. Principal Component Analysis (PCA) further confirmed the critical influence of CAPE, SHF, and surface temperature on lightning dynamics. These findings aim to provide valuable insights into the complex interplay between meteorological variables and lightning activity, enhancing our understanding of thunderstorm dynamics and contributing to the development of improved lightning mitigation strategies.

Platooning represents a crucial strategy for mitigating emissions from heavy-duty vehicles (HDVs). This study evaluates the effects of platoon composition on the surrounding airflow utilizing a computational fluid dynamics (CFD) model, and quantifies the resultant fuel efficiency and CO₂ emissions. This study examines fuel consumption data reconstructed from field experiments to validate the CFD model’s ability to accurately simulate drag forces within a homogeneous three-truck platoon. The potential for fuel savings was assessed based on CFD-simulated fuel consumption, taking into account various inter-vehicle distances and driving speeds. The model successfully reproduced the fuel consumption observed in a platooning formation comprising lead, middle, and trailing trucks, with an error margin below 6.2%. Fuel consumption analysis shows that while lead and middle trucks consume more fuel with increased inter-vehicle distances, the trailing truck's consumption decreases at specific distance-to-length ratios (D/L), increasing again beyond a D/L of 1.1. Additionally, a significant decrease in total fuel efficiency was noted for D/L ratios exceeding 1.5. Considering the diverse platooning scenarios analyzed, the study anticipates an annual reduction of up to 7 tons of CO₂ equivalent per vehicle. By optimizing platooning configurations, this research contributes to enhancing fuel efficiency and reducing emissions from HDVs.

Recent studies have shown that the Pacific-Japan (PJ) pattern is the dominant climate mode and has a relationship with rainfall anomalies in East Asia. However, the influence of the PJ pattern on the rainfall of Sri Lanka remains largely unclear. Therefore, the present study examines the impact of the PJ pattern on the rainfall of Sri Lanka during the boreal summer utilizing observational and reanalysis datasets from 1981 to 2020. It is noted that the PJ pattern has a significant positive relationship with rainfall in Sri Lanka during the boreal summer. Furthermore, based on the composite analysis, we found that Sri Lanka experiences wet conditions during the positive phase of the PJ pattern in the summer, while the negative phase of the PJ pattern contributes to dry conditions. During the positive phase of the PJ pattern, moisture convergence over Sri Lanka is associated with the easterlies extending from the southern flank of Western North Pacific anomalous anticyclonic circulation, which results in enhanced convection and wet conditions over Sri Lanka. On the other hand, moisture divergence over Sri Lanka is linked with the westerlies extending from the southern flank of the Western North Pacific anomalous cyclonic circulation, decreasing the convection and dry conditions over Sri Lanka. This study suggests that the PJ pattern is a significant climate mode for understanding the rainfall pattern in Sri Lanka.

In early January 2016, Storm Frank, an extreme winter storm with a peak intensity of 928 hPa, intruded into the Atlantic sector of the Arctic. This led to unprecedented warming and significant sea ice loss in the Barents-Kara (B-K) Sea. Following this extreme warming event, a series of extreme weather events occurred in mid- and late-January across Eurasia, including a persistent blocking pattern near the Ural mountains and extreme cold wave events over Mongolia, China, and Korea. This study utilizes the Korean Integrated Model (KIM), coupled with an ocean-sea ice model, to reproduce this event and to examine its extended medium-range forecasting performance. While the control model effectively captures the initial Arctic warming, it struggles to reproduce the observed sustained warming that lasted over two weeks. Here, we identified that the model significantly overestimates the sea ice concentration in the B-K Sea, where the initial warming is more pronounced in observations. Through sensitivity experiments, we found that reducing the sea ice strength parameter, which governs the ice resistance to pressure and deformation, effectively alleviated this overestimation. This adjustment facilitates easier sea ice melting, strengthens the ocean-atmosphere interactions, and extends the duration of simulated Arctic warming. Our findings emphasize the crucial role of accurate Arctic sea ice representation in extended medium-range forecasting for East Asia, particularly for extreme weather events.

In this study, GloSea6 hindcast (HCST) from the UK Met Office is used to investigate the model prediction skill for the impacts on the East Asian summer associated with two extreme El Niño cases (1997/1998 and 2015/2016). For the 1998 case, we found that GloSea6 model is able to predict the ocean–atmosphere circulations one to two seasons ahead, including the anomalously positive sea surface temperature (SST) in the Tropical Indian Ocean (TIO) and the anomalous anticyclone (AAC) in the Western North Pacific during the spring and summer seasons. However, for the 2016 case it fails to capture the observed fast cooling of the spring TIO SST and the rapid decaying of the summer AAC, due to an overestimated linkage between the summer TIO and the precedent winter El Niño. Physically, the exaggerated model SST warming over both the eastern and western Indian Ocean suppresses the development of the surface westerly wind that enhances the summer monsoon flow in the TIO and cools the warmed SST as in the real world. According to further analysis, the sensitivity of the TIO is linked to the formation of the spring AAC, which is influenced by the longitudinal position of warm Pacific SST, causing the HCST to display a more idealized El Niño-TIO-AAC teleconnection than the observations. Thus, simulating the decaying El Niño and its teleconnection to the TIO is crucial for reliable seasonal forecasts of East Asian climate during post-El Niño summers.

In this study, the microphysical characteristics of snowfall in Seoul, South Korea and their changes with meteorological conditions are examined using about 6-year observation data from a Parsivel disdrometer. The snow particle size distribution (PSD) exhibits convex-down shapes, being better represented by gamma distributions than exponential distributions. As snowfall rate increases, the snow PSD broadens and its peak rises. The changes in gamma PSD parameters with snowfall rate differ between the mean PSD and 1-min PSDs. The volume-weighted mean diameter D_m much more rapidly increases with snowfall rate in comparison with D_m in Beijing, China and Pyeongchang, South Korea, suggesting the relative importance of aggregation in Seoul. 77% of snowfall in Seoul occurs when northwesterly blows at the 850-hPa level. This snowfall is associated with west-high/east-low pressure patterns, large air–sea temperature differences (~ 19 °C), and shallow (≤ 2.5 km) precipitation systems, suggesting a large contribution of sea-effect snowfall from the Yellow Sea. The northwesterly-type snowfall with lower temperatures (≤ 25th percentile, COLD) and with higher temperatures (≥ 75th percentile, WARM) at the 850-hPa level is compared in the same intensity range of 0.5–1 mm h⁻¹. Compared with the WARM snowfall, the COLD snowfall has relatively broad PSDs and less-rimed snow particles. The COLD snowfall is associated with relatively large wind shear, small static stability, low temperatures of − 21 to − 9 °C, and low humidity in the lower atmosphere, which is attributed to relatively strong northwesterly resulting in relatively strong cold and dry advection. This implies that enhanced aggregation by stronger turbulence and dendritic growths can contribute to the broader PSDs and that weakened riming for the lower temperatures might be associated with the less-rimed snow particles.