Asia-Pacific Journal of Atmospheric Sciences最新文献

Western disturbances (WDs) are upper-tropospheric mid-latitude synoptic systems propagating eastward along the subtropical westerly jet stream. They are capable of causing extreme precipitation events and have strengthened their impact over the Karakoram part of the Himalayas in recent decades. They play a crucial role in the sustenance of the “Karakoram Anomaly,” which refers to the anomalous stability/surge of a few Karakoram glaciers in contrast to the other glaciers of the Himalayas. Using the existing WD-catalog derived from ERA5 and MERRA2 reanalysis datasets, we observed that the core genesis zone for Karakoram WDs had undergone a statistically significant shift of ~ 9.7^oE, migrating towards more favourable conditions for cyclogenesis. The study proposes a new parameter to identify regions of potential extratropical cyclogenesis. The shift can be attributed to an enhanced genesis potential, convergence, and higher moisture availability along the WD path. Composite analysis suggests that moisture availability has risen significantly over the shifted zone. Moreover, the propagation speeds of these systems have significantly declined, which explains the recent intensification of precipitation events related to WDs over the Karakoram and hints toward a crucial synoptic influence on the anomalous regional mass-balance phenomenon.

In this study, we propose a new approach to improve the accuracy of the horizontal atmospheric motion vector (AMV) in cloud-free skies and its forecasting. We adapted the optical flow of the convolutional neural network (CNN) framework model using two 10-min interval infrared images at water vapor channels (centered at 6.3, 7.0, and 7.3 (mu m)) from the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A). Since all pixels had seamless AMVs calculated by CNN (CNN AMVs), we could also predict AMVs using the linear regression method. The tracking performance of the CNN-based algorithm was validated using AMVs retrieved from GK2A (GK2A AMVs) by estimating the difference between those values and the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) wind data over Korea in 2022. CNN AMVs showed similar or better root-mean-square vector differences (RMSVDs) than GK2A AMVs (12.33–12.86 vs. 15.89–19.96 m/s). The RMSVDs of the forecasted AMVs were 2.74, 2.95, 3.41, and 4.79 m/s at lead times of 10, 20, 30, and 60 min, respectively. Consequently, our method showed higher accuracy for tracking motion in the production of AMVs and succeeded in forecasting AMVs. We expect that such potential improvements in computational accuracy for operational GK2A AMVs will contribute to increased accuracy when forecasting meteorological phenomena related to wind.

Site adaptation has become a necessary step in resource assessment for ensuring the bankability of a renewable energy project. The process involves collecting short-term observation data to correct the long-term dataset available from the satellite-derived models, which could thus provide a more accurate estimate of the solar resource data. This study aims to enhance the site-adaptation of direct normal irradiance, as its correction remains notably challenging in comparison to global horizontal irradiance due to its larger error, which is often attributed to the complexity of cloud modeling. A new methodology for site-adaptation is proposed that exploits the use of a new indicator variable that describes the correctness of sky-condition classification by the clear-sky index. This variable has dual applications within the context of site adaptation: firstly, it is employed in the two-step binning procedure subsequent to the conventional clear-sky binning during preprocessing, and secondly, it serves as an additional input feature in machine-learning-based site adaptation. The results show that the former method can reduce the mean bias error to a mere 0.4%, while the latter is better for reducing large discrepancies as shown by the lower root mean squared error.

On August 8 and 9, 2022, a record-breaking rain rate of 142 mm h⁻¹, with an accumulated rainfall of more than 500 mm, was observed in the Seoul metropolitan area, Republic of Korea. This study focuses on analyzing the concentration of lightning in southern Seoul, which occurred solely on August 8. It is worth noting that the daily rainfall of August 8 was approximately twice that of August 9 (381 mm on August 8 vs. 198 mm on August 9). The RKSG (located in Yongin, 40 km south of Seoul) Weather Surveillance Radar-1988 Doppler was used to explore the characteristics of cloud microphysics associated with lightning activity. Four major heavy rain periods on August 8 were grouped into three categories of lightning rate (e.g., intense, moderate, and none), and their polarimetric signatures were compared. Significant differences in the vertical distribution of graupel were found within the temperature range of 0 °C and − 20 °C, as indicated by radar reflectivity (Z_H) > 40 dBZ and differential reflectivity (Z_DR) < 0.5 dB. Although graupel was detected in all three categories at the relatively warm temperatures of 0 °C to − 10 °C, its presence extended into colder regions exclusively in the intense category. This observation preceded the appearance of lightning by approximately 6 min. At heights with temperature ≤  − 20 °C, a high concentration of vertically aligned ice crystals was observed in lightning-prone regions, leading to a decrease in differential phase (Φ_DP). In summary, this study provides valuable insights into the microphysical characteristics of thunderstorms and their relationship to lightning activity in the Seoul metropolitan area.

The severe acute respiratory syndrome (SARS-CoV-2), also referred to as COVID-19 originated in the Wuhan city of Hubei Province of China in late December 2019 and spread to more than 200 countries, including many in Southeast Asia. This review has established a close relationship between the spread of coronavirus and air pollution and suggests that the prevailing environmental factors played a role in the spread of infection in the region. The rate of coronavirus transmission significantly declined as effective strategies and measures such as lockdowns, quarantine curfews, and country-wide lockdowns were adopted, eventually resulting in a dramatic improvement in air quality in different South Asian countries. The imposition of the lockdown improved air quality, contributing to lower incidences of COVID-19 infection and fatality rates across the region. Studies conducted by various scientists indicated a significant reduction in the level of air pollutants, especially the particulate matter (PM₁₀, PM_2.5) CO, SO₂, and NO₂ due to stringent restrictions on movement, shutting down of most industries, and halting of commercial and construction activities. However, ozone levels did not show any significant decrease. The results provided by the various agencies clearly suggest that the respiratory spread of infections is directly proportional to the air-quality parameters, and steps taken to decrease the particulate matter and other pollutants can help in containing the infection. The studies can help understand the epidemiology of the disease and thus serve as a useful tool for governments to manage the spread of respiratory infections and help mitigate air pollution and disease spread by adopting staggered lockdowns.

Snow plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. While snow plays a crucial role as a boundary condition in meteorological applications and serves as a vital water resource in certain regions, the acquisition of its observational data poses significant challenges. An effective alternative lies in utilizing simulation data generated by Land Surface Models (LSMs), which accurately calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. In this regard, the synthetic intercomparisons of the snow physics in the LSMs can give insight for further improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the earth system models or operational forecasting systems. We examined single-layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), the University of Torino land surface Process Interaction model in Atmosphere (UTOPIA), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). Through the comparison analysis, we emphasized that inclusion of geomorphic and vegetation-related variables such as elevation, slope, time-varying roughness length, and vegetation indexes as well as optimized parameters for specific regions, in the snow-related physical processes, are crucial for further improvement of the LSMs.

Fifteen GCMs in the Coupled Model Intercomparison Project Phase 6 are evaluated for the skill in simulating the atmospheric river (AR) frequency (F_AR) and integrated vapor transport (IVT) during 1995–2014. All GCMs simulate well the annual and seasonal climatology of F_AR and IVT for both the global and East Asia domains. Large biases in F_AR and IVT occur in the same regions characterized by high AR activities including the midlatitude Pacific and Atlantic oceans, the Southern Ocean, and the tropical region from the eastern Indian Ocean to the western Pacific. The sign and magnitude of large model errors vary across the GCMs to result in small model-mean biases. The seasonal variation of the skill of individual GCMs is smaller than the variation of the skill across the GCMs, implying that the model skill varies more widely by the difference in model formulations than the response of individual GCMs to seasonal forcing variations. A novel method to relate the skill for simulating F_AR and IVT to that for winds and water vapor is introduced. The method shows that the vertical integration of the covariance of wind and water vapor in the definition of IVT can be well approximated by the multiplication of two separate functions obtained by vertically integrating either winds or water vapor, especially in the regions of strong AR activities. Spearman’s rank correlation in conjunction with this method suggests that the model skill for F_AR and IVT is significantly related only to that for winds.

Limited observations hinder understanding of turbulent characteristics in mountainous terrain resulting from heating or cooling of slopes, wind, vertical motions, and heat or moisture advection, which disperse aerosols and other pollutants over the region. In this study, the 1290 MHz radar wind profiler data are utilized to compute the boundary layer height (BLH), the refractive index structure constant (C_n²), and the energy dissipation rate (ɛ) over the central Himalayan site for the period of November 2011 to March 2012, from the intense Ganges Valley Aerosol Experiment (GVAX) field measurements. The radar wind profiler (RWP) based estimation of BLH and ɛ is validated against the radiosonde, representing the effectiveness of the datasets for further investigation. The strong seasonal variation of log C_n² and log ɛ, with average values of ≈ -12 m^−2/3 and -2 m² s⁻³, respectively, is associated with the mountain-induced local circulations and stability in the atmospheric boundary layer. The weak stratification during weak flow is found to be responsible for deep mixing, particularly in the nocturnal boundary layer in spring. Furthermore, the level of cloud cover significantly impacts the strength of turbulence, with the highest cloud cover resulting in a substantial increase in log C_n² (approximately -11 m^−2/3) due to intense updraft and downdraft motions compared to clear skies. Additionally, the distribution of aerosol loading across the site, coupled with the behavior of BLH, atmospheric stability, and orographic-induced circulations, implies distinctive seasonal mechanisms for transporting aerosols toward the mountains. This study offers valuable insights into the diurnal and seasonal patterns of turbulent mixing and the mechanisms behind the transport of pollutants through boundary layer processes over the region.

To achieve net-zero carbon emissions by 2050, it is vital to prioritize climate action and monitor the progress of policies with accurate emission estimates. As CO₂ emission estimates can be independently verified using atmospheric CO₂ measurements, the need for optimal CO₂ monitoring networks has increased. This study proposed an experimental method for designing national-scale atmospheric CO₂ monitoring networks. We used gridded data for fossil fuel CO₂ emissions, facilitating the selection of emission grids as potential monitoring sites. First, we determined the appropriate number of CO₂ monitoring sites, which increased in proportion to the magnitude and variability of CO₂ emissions within the region. Subsequently, the emission grids corresponding to the region were arranged in descending order of emissions. Grids were then selected at regular intervals as potential monitoring sites, aligning with the predetermined number of sites. This selection process ensured that monitoring sites were evenly distributed, ranging from areas with high emissions to those with lower emissions. Lastly, as a verification step to assess the suitability of this potential network, a transport model simulating meteorological conditions was employed to evaluate its coverage to detect the influence of CO₂ emissions. This method was applied to South Korea, and 96 candidate monitoring sites were created. The optimal CO₂ monitoring network distributed evenly across South Korea could evaluate variations in CO₂ emissions. The simple monitoring network design method proposed in this study can accelerate the installation of a national CO₂ monitoring network, ultimately enabling the verification of CO₂ emissions and supporting climate policies.