Journal of Atmospheric Science Research

Seasonal Remote-Sensing Observations of Aerosol-Cloud Relationships under Varied Water Vapor Conditions

2025-09-04T17:26:10+08:00

Understanding the aerosol-cloud relationship is critical for reducing uncertainties in climate projections, especially over regions that experience complex aerosol dynamics from both natural and anthropogenic sources. This study aims to investigate how aerosols influence cloud properties under varying water vapor conditions over nine urban and rural subregions of the Eastern Mediterranean, disentangling the seasonal and annual relationships between aerosols and cloud parameters while distinguishing the effects of different aerosol types. For this purpose, Aerosol Optical Depth (AOD) at 550 nm, Water Vapor (WV) under clear-sky conditions, Cloud Cover (CC), Cloud Optical Depth (COD), and Cloud Top Pressure (CTP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua satellite, for the time period July 2002-December 2012. Additionally, anthropogenic (AOD_anthr) and dust AOD (AOD_dust) datasets, constructed through a synergy of satellite observations, chemical transport modeling, and reanalysis products were utilised. Our results reveal consistent increases in CC with increasing total, anthropogenic, and dust aerosol loading across all regions, seasons, and water vapor levels, supporting the hypothesis of aerosol-induced cloud invigoration. COD was found to increase with AOD when AOD < 0.5 but remain steady or decline for higher AOD levels, independent of water vapor concentration. Furthermore, anthropogenic aerosols tend to enhance COD more strongly than dust aerosols. Seasonal differences in cloud height were also observed: in spring and summer, CTP decreases (indicating higher cloud tops) with increasing AOD and CC, while in autumn and winter, under high water vapor conditions, aerosol loading leads to higher clouds.

Statistical Modeling of PM2.5 Concentrations: Prediction of Extreme Events and Evaluation of Advanced Methods for Air Quality Management

2025-07-23T16:34:15+08:00

This study analyzes the statistical behavior of PM2.5 concentrations in Brasília using advanced probabilistic and time series modeling to support air quality management and extreme event forecasting. The methods applied include Generalized Extreme Value (GEV) distributions, Bayesian inference with Log-Normal distribution, ARIMA models, and quasi-Gaussian approaches. Model performance was evaluated through statistical metrics such as RMSE, R², and the Approximation Index, with parameter estimation improved using the Metropolis-Hastings algorithm. Results show that the GEV 1 model provides a better fit for lower PM2.5 concentrations, while GEV 2 performs better at predicting extreme events. The log-logistic and log-normal distributions also demonstrated good fit, capturing asymmetry and long-tail behavior typical of environmental data. The ARIMA model identified seasonal patterns and supported short-term forecasts, though its predictive capacity for extreme values was limited. Bayesian inference allowed robust estimation of parameter uncertainties and revealed the non-negligible likelihood of severe pollution events. The study concludes that model selection should depend on the forecasting objective: GEV for extremes, Log-Normal for general variability, and ARIMA for trends and seasonality. The use of MCMC sampling techniques significantly improved model robustness. These findings provide a comprehensive framework for understanding air pollution dynamics and guiding public policy on air quality in urban environments.

Hydrological Assessment of the “Madar 22” Weather Event: Implications for Water Resources Management in Saudi Arabia

2025-07-08T15:44:31+08:00

A rare and intense tropical weather event, named “Madar 22” affected large areas of the Kingdom of Saudi Arabia and neighboring Gulf countries during July and August 2022. This study aims to assess the hydrological impacts of the event on both surface and groundwater resources, and its implications for water resources management in the Kingdom. Also, one of the specific objectives of the present study is to estimate both runoff and potential groundwater recharge coefficients during “Madar 22” weather event across all regions of Saudi Arabia. Utilizing rainfall data from the MEWA hydrological network and ArcGIS-based spatial analysis, the event was found to deliver extreme precipitation equivalent to a 10-year return period for a 3-hour storm. The event generated a total rainfall volume of 20.6 billion cubic meters, of which 1.058 billion cubic meters was estimated as surface runoff. This led to the harvesting of 294 million cubic meters in 189 dam reservoirs and recharged an estimated 239 million cubic meters of groundwater, as indicated by water table rises in 65 shallow wells. The present study concluded that the mean precipitation depth recorded in August 2022 is substantially higher than the long-term monthly average, illustrating a clear trend toward intensified late-summer rainfall. The findings of the present study show critical implications for renewable water resource management in arid and semi-arid regions. They emphasize the need to integrate extreme weather events into flood risk assessments, dam operation protocols, and groundwater recharge strategies to enhance national climate resilience.

“Friagem” Events in the Brazilian Amazon, Changes and Variability in Its Climatological Patterns

2025-08-08T09:35:03+08:00

Friagem events in the southwestern Brazilian Amazon including their changes, variability and climatological features during the 1979–2020 period were examined. The incursion of polar origin cold air mass into the region during austral autumn and winter, which leads to the abrupt drop of the air temperature, characterizes the friagem event. Sixty-five friagem events were identified during the analysis period. These events are more frequent in July and August, with a decreasing trend in both frequency and duration over the years. The average intensity was 14.8 °C, representing a 5°C drop in relation to the average minimum air temperature in the study domain. Additionally, the most intense events occurred in La Niña years and during the positive phase of the Antarctic Oscillation. On the global scale, friagem event is associated with the zonal wavenumber 3-4 pattern in the 500 hPa geopotential anomaly field, with a northwest-southeast oriented anomalous anticyclone extending from the southeast Pacific, across southern South America to southwestern Atlantic, which is flanked to the northeast by an anomalous cyclone over southern Brazil and the adjacent Atlantic. Friagem is also associated with negative sea surface temperature (SST) anomalies in the central and eastern tropical Pacific resembling the La Niña pattern, and negative SST anomalies in the South Atlantic off southeast and south Brazil. This study contributes to improving our knowledge of the friagem events in the Amazon, highlighting the importance of monitoring in the context of climate change and interannual climate variability associated with El Niño and La Niña.

Study of Atmospheric Variables using Low-Cost Stratospheric Balloon-Borne Missions

2025-06-27T09:09:06+08:00

A better understanding of atmospheric dynamics and improvement of regional weather and climate models require accurate measurement and analysis of atmospheric variables such as temperature, pressure, and wind velocity across altitudes. In this study, we present such results from a series of high-altitude balloon missions conducted by the Indian Centre for Space Physics (ICSP). These missions, in which balloons reach up to altitudes of ~42 km, provide high-resolution vertical profiles of atmospheric parameters over the Indian subcontinent, a region where such data are sparse. We analyze the payload's vertical ascent rates, horizontal displacements, and variations in some atmospheric parameters, such as temperature, pressure, and wind velocity with altitude. Wind velocity components—zonal (east-west) and meridional (north-south)—are also examined, with particular emphasis on their seasonal variability due to subtropical jet streams during pre- and post-monsoon periods. Our analysis reveals significant seasonal variation in wind patterns at stratospheric heights. We obtain clear indications that the atypical wind behaviors observed in 2019 may be linked to anomalies in monsoonal rainfall patterns. These results contribute valuable insights into upper atmospheric dynamics over the Indian region and also highlight the importance of balloon-borne observations in refining regional atmospheric models.

Atmospheric Ozone Variability in Central Brazil: A Spatiotemporal Analysis Across Three Distinct Biomes

2025-08-20T17:19:20+08:00

This study investigates the spatial and temporal variability of the Total Column Ozone (TCO) across three Brazilian biomes—Cerrado, Pantanal, and Atlantic Forest—between 2005 and 2020. Satellite-derived TCO data from the Ozone Monitoring Instrument (OMI) were used to assess monthly, seasonal, and interannual variations. Linear and polynomial regressions were applied to identify trends and variability, while descriptive and comparative statistics supported biome-specific characterization. The results reveal distinct seasonal cycles, with primary TCO peaks during the dry season (August–October), driven by intense solar radiation and biomass burning, and secondary peaks during the rainy season (March–May), likely influenced by stratospheric ozone transport. The Cerrado exhibited the highest TCO values and variability, followed by the Atlantic Forest and Pantanal. The observed upward TCO trend (~ 0.03 DU/year) is modest and biome-dependent, reflecting both global atmospheric circulation patterns (e.g., Brewer–Dobson, QBO) and localized human impacts such as land use change and fire regimes. Notably, the Pantanal showed the lowest TCO concentrations and interannual fluctuations, likely due to its flat topography and high humidity. This work emphasizes the importance of regionalized ozone monitoring in tropical ecosystems and supports public policy aimed at reducing emissions, protecting biodiversity, and mitigating climate impacts. The integration of satellite-based data and spatiotemporal analysis provides valuable insights for future climate-chemistry modeling in tropical regions.

Role of Mid-Level Vortex in the Genesis of Tropical Cyclone over the North Indian Ocean Using WRF Model

2025-07-21T10:46:51+08:00

The formation of a tropical cyclone (TC) involves complex dynamic and thermodynamic processes. Understanding the role of mid-level vortices in the genesis of TCs over the NIO is crucial for improving TC forecasting. This study aims to enhance the field of meteorology by providing insights into these genesis processes. We utilized the National Centre for Environmental Prediction (NCEP) FNL reanalysis 1x1 degree data as input for the Weather Research and Forecasting (WRF) model version 4.0.3. The model was configured with one-way nesting between the parent and child domains. For the simulations, we employed the Kain-Fritsch (new Eta) scheme and the WRF Single-Moment 6-class graupel scheme (WSM6) as options for cumulus and microphysics. Our study identified the development of top-down vortices over the Arabian Sea (AS) and bottom-up vortices over the Bay of Bengal (BOB). We found that area-averaged mid-level relative vorticity (MRV) below 8 × 10⁻⁵ s⁻¹ over a 6 × 6-degree area does not support the formation of TCs. Also, changes in mid-level temperature, whether increases or decreases, corresponded with the behaviour of MRV. Low-pressure systems (LPS) where vertical wind shear (VWS) did not exceed 10 ms⁻¹ were prone to evolve into TCs. We also found that an increase in MRV, accompanied by VWS exceeding 10 ms⁻¹, hindered the development of the mid-level vortex. Furthermore, there was an inverse relationship between geopotential height (GPH) and MRV, which favoured TC formation. The trends in MRV calculated by our model were comparable to those found by the European Centre for Medium-Range Weather Forecasts (ECMWF). Therefore, the results of this study are both valid and significant.

A Mathematical Model for Fluxes Associated with Airflow over Northeast Region of India

2025-06-27T09:24:48+08:00

An attempt has been made to study the horizontal momentum flux and vertical energy flux associated with baroclinic airflow over northeast region of India. The northeast region of India features two prominent orographic barriers: the Assam-Burma Hills (ABH) and the Khasi-Jaintia Hills (KJH). This paper presents a three-dimensional (3-D) model for mountain waves, applied to calculate momentum flux (MF) and energy flux (EF) associated with airflow over the Assam-Burma Hills (ABH) and Khasi-Jaintia Hills (KJH) in northeast India. We investigate the impact of the Assam-Burma Hills (ABH) and Khasi-Jaintia Hills (KJH) on momentum flux and energy flux associated with vertically propagating internal gravity waves, considering a realistic airflow with height-dependent wind and stability profiles. The model employs a comprehensive set of assumptions, including a three-dimensional (3D) laminar flow regime, inviscid fluid behavior, adiabatic conditions, and the Boussinesq approximation, all within the context of a non-rotating moist airflow environment. The simulation yields detailed results for the energy flux along the vertical z-axis, as well as the two horizontal components of momentum flux along the x-axis and y-axis. These results have been thoroughly evaluated and subsequently compared with the findings of earlier researchers in the field, facilitating a robust validation of the model's performance.

Urban Air Pollution Trends and the Rise of Electric Vehicles in South Asia: A Case Study of Ludhiana

2025-08-04T11:46:37+08:00

The increasing adoption of electric vehicles (EVs) is regarded as a key strategy for mitigating urban air pollution in rapidly developing regions like South Asia. However, the effects of EV penetration on various pollutants—especially secondary pollutants like ozone—remain complex and context-dependent. This study investigates pollutant trends in Ludhiana, India, from 2013 to 2025, focusing on NO₂, PM₂.₅, CO, and O₃ concentrations. Data were sourced from national monitoring agencies and NASA’s Aura/OMI satellite platform, while EV statistics were obtained from the Punjab Transport Department. Statistical methods, including regression and time-series decomposition, were used to explore pollutant dynamics in relation to EV trends. A decline in NO₂, PM₂.₅, and CO was observed over the study period. However, these trends likely reflect a combination of factors, including stricter emission norms, fuel quality upgrades, and broader regulatory interventions—alongside EV growth. Ozone displayed a nonlinear response, peaking mid-decade and declining thereafter, suggesting complex photochemical interactions. While EV integration may have contributed to reduced direct emissions, further studies incorporating source apportionment and real-time emissions data are necessary to isolate its specific impact. This study offers preliminary insights into the environmental dynamics of transport electrification in South Asian cities.