This study presents continuous, real-time monitoring of the atmospheric boundary layer (ABL) dynamics over Dibrugarh, an easternmost location of the state of Assam in Northeast India, using a Ceilometer Lidar CL31. A distinct diurnal cycle and strong seasonal variability in ABL height (ABLH) are observed. The ABL reaches its maximum height ( 1750 m and 1450 m) during the pre-monsoon (March–May) and monsoon (June–September) seasons, while it remains shallower ( 925 m and 1025 m) during the post-monsoon (October–November) and winter (December–February) periods. Surface sensible heat flux and latent heat flux significantly influence ABL growth during the warmer months. The diurnal evolution of the lifting condensation level (LCL) is also analysed to investigate the ABL interaction with cloud formation and development. It is observed that the LCL generally lies above the ABL. However, the ABL often exceeds the LCL during afternoon hours in the pre-monsoon and monsoon seasons, suggesting favourable conditions for cumulus cloud formation. Ceilometer-derived ABLH are further compared with European Centre for Medium-Range Weather Forecasts version 5 (ERA5) reanalysis data. The ERA5 is observed to capture the diurnal and seasonal evolution of ABL but is underestimating the Ceilometer retrieved ABL with a mean bias error (MBE) (root mean square error, RMSE) values ranging − 5.5 m – − 183.6 m ( 97.2 m – 316.7 m). Seasonally, the highest correlation coefficient of 0.98 is observed in the monsoon with MBE (RMSE) − 53 m (87.6 m). However, the highest MBE and RMSE, despite a good correlation (0.97), in pre monsoon suggests a discrepancy in reanalysis data, mainly arising from the differences in retrieval methods, where Ceilometer uses gradient method while ERA5 uses the Bulk Richardson method. The present observation of real-time diurnal ABL cycle will be helpful in explaining the diurnal evolution of atmospheric composition specially the aerosols and trace gases measured over the study location using ground-based observations as well as those simulated using climate models.

The Leh-Ladakh region is a high-altitude cold desert (3255 m above mean sea level), located under the rain shadow of the Himalayas, display various cloud features crucial to understand the extreme weather conditions. Global Satellite Mapping of Precipitation (GSMaP) parameter helped to show that almost 37 % of rain falls over Leh-Ladakh region (7-38 degrees N, 62-100 degrees E) during last 23 years of monsoon season. Low rainfall though facing extreme rainfall events, need a continuous monitoring of cloud measurements. In the present study, cloud base height (CBH) variability is investigated using Ceilometer Lidar measurements, complemented by Moderate Resolution Imaging Spectroradiometer (MODIS), European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) during September 2022-August 2023. Our comparative findings suggested that Ceilometer's CBH measurements aligned with calculated MODIS CBH, whereas ERA5 CBH gets underestimate. Further, Ceilometer measurements of multi-layer clouds, consist of three distinct layers. These day-to-day seasonal variations in clouds show highest occurrence frequencies during the pre-monsoon (67.94 %) and monsoon (98 %), clearly reflects the onset and active phases of the Indian summer monsoon. Further, July recorded with the highest cloud occurrence frequency (84.03 %), consisting of single-layer (15.92 %), double-layer (25.98 %) and triple-layer (42.13 %) clouds. Our study inferred a high fraction of mid-level (similar to 3-6 km; 77.53 %) clouds during winter, pre-monsoon, monsoon, and post-monsoon seasons. Thus, altostratus, altocumulus, nimbostratus, or altogether were particularly prominent across all the seasons, with their variability linked to orographic and climatic factors.

The lifting condensation level (LCL) is a key parameter in estimating the convective cloud base height (CBH) and plays a crucial role in various meteorological processes. Under typical atmospheric conditions, the CBH coincides with or is positioned above the LCL. This study investigates the occurrence of clouds forming below the LCL over Ahmedabad, a semi-arid region in Western India, during 2022-2024. An analytical LCL formulation, derived from surface temperature and relative humidity, showed minimal bias compared to established empirical formulas when validated against radiosonde data. Convective clouds near the LCL are most frequent during the monsoon season. However, during the post-monsoon and winter months, anomalous cloud formations below the LCL were observed, with a mean CBH of 1044 +/- 135 m, predominantly between 0600 and 1200 UTC. These lowlevel clouds were associated with a strong thermal inversion below the LCL, occurring under surface temperatures ranging from 24 degrees C to 37 degrees C and relative humidity levels between 17 % and 49 %. The surface sensible heat flux during these clouds' occurrence was lower than that observed for clouds forming near the LCL but comparable to clear-sky conditions. Conversely, the surface latent heat flux was higher than in clear-sky conditions but lower than in cases where clouds formed near the LCL, averaging 147 +/- 74 W/m2. These findings highlight the role of thermodynamic stability and surface heat fluxes in modulating cloud formation processes in semi-arid environments. Improved understanding of clouds forming below the LCL is essential for enhancing weather prediction models and refining convective parameterization schemes in numerical weather and climate models, particularly in arid and semi-arid regions where cloud development significantly influences regional climate variability.

The Atmospheric Boundary Layer (ABL) plays a crucial role in regulating surface-atmosphere interactions, directly influencing weather, air quality, and climate. This study presents a comprehensive characterization of the ABL over Dehradun, a humid subtropical region in the Doon Valley at the foothills of the Indian Himalayas. An analysis of five years (2020-2024) of ground-based Lidar observations reveals pronounced diurnal, monthly, seasonal, and interannual variations in boundary layer height (BLH), which are strongly modulated by local topography and cloud cover. The BLH exhibits afternoon peaks (similar to 2 km) during pre-monsoon and summer months due to enhanced solar heating and convection, while shallow winter layers (<1 km) arise from reduced insolation and stable stratification. Monsoon conditions suppress BLH development (<1 km), with recovery occurring during the post-monsoon period (similar to 1-1.2 km). Clouds exert a significant influence, reducing the mean BLH from similar to 1.3 km under clear-sky conditions to similar to 0.9 km under cloudy conditions, particularly during the premonsoon and monsoon seasons between 12:00 and 16:00 IST. The reanalysis datasets reproduce broad seasonal and diurnal patterns but systematically underestimate BLH by 120-250 m. Furthermore, reanalysis-derived BLH exhibits reduced skill under cloudy conditions, with correlation coefficients decreasing to similar to 0.47 compared to values exceeding 0.65 under non-cloudy conditions. Among the datasets, IMDAA reanalysis demonstrates closer agreement with Lidar-derived BLH than ERA5, owing to its finer spatial resolution and regional assimilation. This study establishes a baseline understanding of ABL dynamics over northern India's complex terrain, highlighting the role of valley circulations, cloud occurrence, and the need for high-resolution observations to better constrain models and improve air quality and climate assessments.

Cloud base height (CBH) is a fundamental atmospheric parameter for weather forecasting, aviation safety, and climate research, as it provides key information on boundary layer structure, atmospheric stability, and cloud-radiation interactions. In this study, a two-step hybrid framework combining statistical cloud detection with machine learning (ML) regression for CBH estimation is developed and evaluated. In the first step, cloud presence is identified using a Variability Index (VI) defined as the ratio of the standard deviation of the backscatter profile to its peak value. This physically interpretable index shows strong class separability, with a large effect size (Cohen's d approximate to 1.96), a maximum F1 score of about 0.83 at a VI of approximately 0.24, and an area under the ROC curve of 0.88, indicating effective cloud detection performance. In the second step, Multiple Linear Regression, Fine Tree Regression, Random Forest, and Gaussian Process Regression (GPR) models are applied to cloud-present profiles to estimate CBH. Among these, Random Forest, a tree based nonlinear ensemble model perform best, achieving a high correlation coefficients of about 0.94 +/- 0.04. GPR, a kernel-based model, demonstrated slightly lower performance compared to Random Forest, achieving correlation coefficients of R = 0.91 +/- 0.05, while the Fine Tree model showed the weakest performance among the nonlinear models tested in this study, achieving R = 0.89 +/- 0.07. In contrast, Multiple Linear Regression model showed lowest accuracy with R = 0.58 +/- 0.13. The results demonstrate that combining a simple, explainable statistical classification approach with advanced machine learning regression significantly improves the reliability and accuracy of CBH retrieval from Lidar backscatter data. The proposed framework is computationally efficient and shows strong potential for operational implementation in real-time atmospheric monitoring networks.

Delhi experiences severe air quality deterioration during the post-monsoon and winter seasons, driven by anthropogenic emissions and natural meteorological factors. This study investigates the atmospheric boundary layer (ABL) characteristics during heavy air pollution and fog conditions over Delhi from October 2023 to February 2024 using ground-based Lidar, satellite, and reanalysis data. Lidar measurements reveal a persistently shallow ABL (<1 km) from November to January, with nighttime boundary layer height (BLH) suppressed by strong radiative inversions. Elevated PM2.5 concentrations during this period show an inverse, power-law relationship with BLH. The ventilation coefficient (VC) remained below 800 m(2)s(-1) from November to January, indicating poor dispersion. INSAT-3D/3DR satellite data showed a peak fog occurrence of 75 % over Delhi, with the highest frequency in January. Analysis showed that the combined frequency of haze, fog, and low-level clouds reached 22.46 % during the study period, with the highest occurrences in November (45.10 %) and January (39.55 %). Ground-based Lidar observations captured fine-scale features such as shallow inversion layers, nighttime ABL collapse, and diurnal boundary layer development more accurately than reanalysis. These insights are crucial for enhancing urban weather models, air quality forecasts, and early warning systems in pollution-affected regions.

The April 17, 2024, eruption of Mount Ruang, an active stratovolcano located in Indonesia’s Sangihe Islands, stands as one of the most significant volcanic events in recent decades. This study investigates the geological drivers of the eruption, particularly the subduction of the Indo-Australian Plate beneath the Eurasian Plate, and examines its atmospheric, environmental, and socio-economic impacts. The eruption released a substantial amount of sulfur dioxide (SO₂), reaching 129.2 DU, which contributed to elevated aerosol concentrations and significant atmospheric disturbances. Observations from Landsat-9 OLI-II, Himawari-9, and SABER satellites reveal large-scale vegetation loss, modifications in cloud cover driven by convection, and unusual temperature patterns. The eruption resulted in a remarkable increase in stratospheric temperature from about 230 K to 270 K. SO₂ emissions captured by the TROPOMI were carried westward by prevailing winds, resulting in atmospheric cooling and cloud formation. TROPOMI observations further indicated that the eruption’s effects extended to South America. The study employed the HYSPLIT model to trace the dispersion of volcanic gases, revealing that SO₂ released during the eruption ascended to approximately 18 km before being transported westward. OLR data from the NOAA CDR demonstrated a reduction in upward longwave flux due to cloud formation following the eruption. These findings highlight the significant role of volcanic aerosols in climate modulation and highlight the urgent need for enhanced monitoring of remote volcanic regions. The research also addresses the ecological and socio-economic impacts on local communities, particularly in agriculture and fishing, and emphasizes the need for improved hazard assessments, evacuation protocols, and community education. This study provides critical insights for global volcanic risk management and the development of effective mitigation strategies in high-risk areas.

This study analyzes the cloud base height (CBH) and precipitation patterns over Ahmedabad, a semi-arid urban city in the Western-Indian region, over the period 2000 to 2021. The study compares ERA5 precipitation product with GSMaP_ISRO precipitation data during the Indian Summer Monsoon (ISM) period and examines trends in cloud frequency across different altitude levels over the Ahmedabad region. Results indicate that the ERA5 is able to represent the monthly rainfall patterns over the study region. The findings reveal that clouds accounting for significant rainfall during ISM typically have a cloud base height (CBH) below 1 km, with the highest frequency observed between 200 m and 250 m. A notable increasing trend in the frequency of rainy clouds (rain > 0.5 mm/hour) is observed during September (withdrawal monsoon) with an increase of (0.49 ± 0.23)

Clouds are critical in shaping local weather patterns, particularly in mountainous regions where complex environmental factors influence their behavior. This study provides a comprehensive analysis of cloud properties over Mt. Abu (24.59° N, 72.71° E, 1219 m a.m.s.l), a high-altitude region in the Aravalli Range of Western India, utilizing ground-based Lidar and satellite datasets. The study found an annual cloud occurrence of approximately 23

This study investigates the dynamics of atmospheric clouds and boundary layer due to a sudden dust storm over Ahmedabad (23.02 degrees N, 72.57 degrees E), a Western-Indian region, during the pre-monsoon season on May 13, 2024. The storm was triggered by the outflow from convective systems originating in southwest Gujarat and southeast Rajasthan, combined with the significant deepening of the thermal low core over Ahmedabad, which generated strong near-surface winds and initiated the dust storm. These systems and the dust storm were captured by the INSAT-3D satellite and MODIS instrument on NASA's Aqua and Terra satellites. The ground-based Ceilometer Lidar backscatter profile showed an abrupt change in the mixed layer height (MLH) from similar to 2.5 km to about 250 m during the storm due to attenuation of the signal by heavy dust load. The MLH, similar to 2 km on 12 May (previous day), shallowed to similar to 800 m on 14 May (post dust storm day), with increased backscatter indicating high dust concentration. Vertical visibility dropped to 340-660 m during the dust storm. During the storm, relative humidity near the surface increased from 29% to 48% due to moisture transport by frontal system along the density current pathway, while near-surface wind speeds peaked at around 6-10 m/s. After the storm, deep convective clouds formed with a vertical extent of similar to 11 km, resulting in approximately 19 mm of rainfall with nearly 15 mm falling within just 1 h indicating the dust-cloud interaction. This study highlights the impact of moist convection and subsequent dust storm on clouds and boundary layer dynamics, emphasizing the importance of ground-based instruments, satellites, and reanalysis datasets in atmospheric monitoring. Understanding the causes, mechanisms, and consequences of dust storms is critical for mitigating their effects and adapting to the changing climate patterns that may influence their frequency and intensity.

The Atmospheric Boundary Layer (ABL) represents the critical interface between the Earth's surface and the free atmosphere, playing a pivotal role in shaping weather patterns, air quality, and the dispersion of pollutants. This study comprehensively investigates the ABL dynamics over the Western-Indian region during 2019-2023. Continuous observation of ABL is made over the Western-Indian region's three locations: Ahmedabad, Mount Abu, and Udaipur. Ahmedabad (23.02° N, 72.57° E) is a highly polluted urban location in the Indian state of Gujarat with a hot, semi-arid climate, while Mount Abu (24.59° N, 72.71° E) is a high-altitude location in the Aravalli range of mountains in Rajasthan. On the other hand, Udaipur (24.58° N, 73.71° E) is close to the desert region in Rajasthan, surrounded by lakes and having a hot semi-arid climate. The ABL is continuously monitored over these stations using a ground-based Ceilometer lidar. By analyzing observational data collected from diverse geographical locations, we seek to identify regional variations in ABL characteristics and their consequences on local weather systems. Results indicated a large winter-summer difference in ABL over Ahmedabad, with summer Boundary Layer Height (BLH) exceeding winter BLH by 1–1.5 km. These differences were less over the Mount Abu and Udaipur region. The ABL usually collapses over all three study regions during monsoon and is thicker during the pre and post-monsoon. Ground-based observation of ABL using lidar has been compared with the radiosonde, satellite, and reanalysis datasets. The ERA5 reanalysis underestimated the BLH, especially the nocturnal boundary layer height. Due to the proximity to the Thar desert, the study sites witness dust storms. The study also investigated the impact of dust storms on the ABL. Through a combination of advanced measurement techniques, such as lidar and satellite observation, we aim to provide a nuanced understanding of the spatiotemporal variability of key ABL parameters. In conclusion, this study aims to contribute to understanding how the ABL responds to changing climate conditions and its role in modulating the Earth's energy balance. By enhancing our understanding of ABL dynamics, we can improve the accuracy of weather predictions, refine climate models, and develop strategies for mitigating the impact of air quality issues on human health and the environment.

Stratiform clouds, which form under stable atmospheric conditions, play a crucial role in negative cloud radiative forcing. Utilizing a combination of ground-based observations, satellite data, and reanalysis datasets, the study investigates the formation of stratiform clouds over Udaipur (24.58° N, 73.71° E, 598 m a.m.s.l) compared to Ahmedabad (23.02° N, 72.57° E, 56 m a.m.s.l) in the semi-arid region of Western India during the post-monsoon period. Ground-based Lidar observations indicated consistent cloud occurrences between 2 and 4 km over Udaipur during post-monsoon. Conversely, cloud occurrence over Ahmedabad is found to be lower despite the city having higher levels of columnar water vapor during this period. During the post-monsoon period of 2022–2023, the total cloud occurrence between 2 and 4 km altitude over Udaipur ( 4.24

Ground-based instruments play a very crucial role in accurately measuring the actual precipitation falling over the surface, especially during severe precipitation events. The present analysis offers a thorough description of the recordbreaking extremely heavy rainfall event occurring over central India during September 15-17, 2023 since 1958. This study utilizes data, collected from various state-of-the-art instruments (Automatic Weather Station (AWS), Laser Precipitation Monitor (LPM), Ceilometer installed at Indian Institute of Technology (IIT) Indore and C-Band DWR installed at Indian Institute of Tropical Meteorology, Bhopal, Madhya Pradesh, India). The event continued for nearly 36 hours, resulting in approximately 260 mm of rainfall with a maximum rain rate reaching up to 180 mm per hour. During the heavy rain event, the instruments recorded the rain microphysical properties, cloud cover, and meteorological conditions concurrently. The spatiotemporal radar reflectivity revealed a convective system evolving towards Southwest Madhya Pradesh. The study enhances preparedness for extreme weather events and provides valuable insights into the meteorological conditions during these occurrences. Additionally, it underscores the importance of an integrated multiinstrument approach for accurate forecasting and nowcasting of such exceptional rainfall events.

In recent years, air quality in Indian megacities has emerged as the most pressing global concern, with Asian megacities being the most polluted. Unlike India’s pollution capital, Delhi, Chennai is a megacity significantly impacted by industrial and transportation activities and has been designated as a non-attainment city by the Government of India under the National Clean Air Programme. The first step towards sustained clean air is identifying the sources causing the megacity’s air quality to deteriorate. The current study is the first-ever attempt to develop bottom-up inventory at ultra-fine resolution (i.e., 0.4 km × 0.4 km), where the annual emission includes 39.6 Gg yr-1 of PM2.5, 65.0 Gg yr-1 of PM10, 387.3 Gg yr-1 of CO, 175.2 Gg yr-1 of NOx, 70.9 Gg yr-1 of SO2, 271.4 Gg yr-1 of VOC, 10.5 Gg yr-1 of BC, and 17.7 Gg yr-1 of OC for the base year 2020. This cutting-edge data on surface emissions with identified hotspots, would be vital tool for air quality studies and the first step towards framing mitigation strategies for sustainable air in Chennai.

Intensified by climate change, extreme rainfall events are frequently increasing and have become severe particularly in tropical and subtropical regions. On 16 April 2024, the United Arab Emirates (UAE) experienced a record-breaking flood event, characterized by similar to 250 mm of rain within a 24-hour period and surpassed the previous record of 1949. Later, on 2 May 2024, another rainfall event occurred but with low rainfall (similar to 20 mm in 12 hours). These consecutive incidents raised a need to understand convective mechanism, so present study focus to analyse it using various cloud properties over Dubai. Our findings inferred that the April event was mostly driven by extensive cloud such as cumulonimbus which stretched up to similar to 16 km, with prominent features of high optical thickness (similar to 150), small-sized cloud droplets (similar to 23.89 mu m), and considerable liquid water content (similar to 2185 g/m(-2)). Further, we found high (similar to 400-1800 J kg(-1)) and comparatively low (similar to 0-700 J kg(-1)) potential energies, which trigerred convection on April 16 and May 02 respectively. Thus our study gained insights related to occurrence of convective activities at regional level using atmospheric and cloud parameters. These outcomes can be used as an input in climate models to improve weather patterns even in arid environments like Dubai, where cloud seeding is a regular practice.

Clouds are integral components of the hydrological cycle and exert significant influence on regional and global weather patterns. Understanding cloud height, layers, and fraction in the atmosphere is crucial for precipitation and regulating Earth’s energy balance. This study investigates the cloud characteristics such as the cloud base height (CBH), cloud top height (CTH), and the vertical visibility over Udaipur, an urban city situated in the Aravalli ranges of Western India, employing ground-based Lidar (Ceilometer), satellite (MODIS), and reanalysis datasets (ERA5). The analysis focuses on CBH observations from Ceilometer Lidar during 2021-22, evaluating reanalysis and satellite-derived CBH. Results reveal peak detection (cloud presence or fully obscured sky) during the southwest monsoon, with frequencies reaching approximately 44

Atmospheric boundary layer (ABL) has been characterized over a high altitude station, Umiam in the North Eastern Region (NER) of India using observations and reanalysis products. In this study, we have used in situ data from Dr. Pisharoty GPS radiosonde launched using meteorological balloons during the years 2009 to 2013 and 2019 to 2020 during the afternoon time. For continuous measurement of ABL we have used the data obtained using ceilometer lidar from December 2019 to November 2023. For long term study of ABL height (ABLH) we have used three reanalysis datasets, viz. ERA5, IMDAA and MERRA2. Seasonal variation of ABLH from the radiosonde, ceilometer and reanalysis datasets showed highest during pre-monsoon followed by winter, post monsoon and minimum during monsoon. On both cloudy and clear-sky days, diurnal variability of ABLH was studied using ceilometer data where more than 55