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.

ABSTRACT Employing observed gridded daily rainfall and temperature datasets for 1971–2022, we developed a revised India Climate Extremes Index ( IndCEI ) to assess long‐term variability in climate extremes across the country. The IndCEI modifies the original CEI framework by integrating seven indicators particularly relevant to the Indian climate system: maximum temperature ( T max ), minimum temperature ( T min ), consecutive dry days (CDD), heavy precipitation events, the Standardised Precipitation–Evaporation Index (SPEI), Heat Index (HI), and Excess Heat Factor (EHF). Results reveal robust and widespread warming signals over India, with significant increases in T max extremes across all four homogeneous regions. Northwestern India (NWI) emerges as the principal hotspot, exhibiting the strongest positive trends in T max , HI, and EHF—indicative of intensifying heat stress and more frequent heatwaves. South Peninsular India (SPI) also shows marked increases in drought‐related extremes (SPEI, CDD). In contrast, Northeast India (NEI) displays heightened short‐duration heavy rainfall events alongside increasing drought episodes, signalling greater hydroclimatic variability. East‐Central India (ECI), the core monsoon zone, presents weaker or inconsistent trends, likely reflecting the influence of monsoon variability and embedded low‐pressure systems. Spatial analyses indicate that both T max and T min extremes have expanded beyond localised pockets, while drought and HI extremes have intensified and spread inland from coastal areas‐creating new hotspots in central and eastern India. At the all‐India scale, IndCEI exhibits a statistically significant increasing trend (~0.36% of area per decade), driven primarily by rising heat stress and drought indicators. Overall, the findings highlight a growing risk of climate extremes, particularly heat, drought, and rainfall across India. The results underscore pronounced regional contrasts: western and southern India are increasingly dominated by heat‐related stress, while Northeastern India faces rainfall‐driven extremes. These expanding hotspots of climate stressors call for region‐specific adaptation strategies, particularly addressing heat in NWI, drought in SPI, and rainfall variability in NEI.

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.

Short-lived dust storms (DS) pose significant challenges due to their sudden onset, rapid intensification, and severe impacts on air quality, visibility, transportation, and public health. Unlike long-duration dust storms, these short-duration events remain poorly understood, largely because of limitations in observations and forecasting capabilities. The dust storm that occurred over Delhi on 11 April 2025 provides a valuable case to examine the meteorological processes driving such rapid-onset events. ERA5 fields reveal the presence of a low-pressure system over northwest India, accompanied by strong westerly surface winds exceeding 12 m s⁻1, which facilitated dust transport from nearby arid regions. Radiosonde derived convective available potential energy shows a sharp increase ( 2300 J kg⁻1) on the dust storm day, compared to 700 J kg⁻1 on the preceding day, indicating a highly unstable atmosphere conducive to deep convection and dust uplift. HYSPLIT back-trajectory analysis further confirms the dust transport from source regions. AERONET and satellite observations show enhanced aerosol loading, with aerosol optical depth peaking at 0.73 and 0.6 during the event, respectively. An abrupt rise in PM₁₀ and PM₂.₅ concentrations is noticed 950 µg m⁻3 and 200 µg m⁻3, respectively, highlighting the dominance of coarse-mode dust aerosols. This abundance of dust aerosols reduced visibility from 3500 m to 500 m and led to a surface cooling of 3 °C due to reduced incoming solar radiation. The results highlight the urgent need to strengthen short-range forecasting of rapid-onset DS by integrating high-frequency observations and improving dust parameterizations, thereby enhancing public health preparedness, aviation safety, and urban resilience.

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.

In 2023, the coastal town of Kayalpattinam in Tamil Nadu recorded an extraordinary rainfall exceeding 950 mm on December 17 and 18, resulting in severe flash floods and devastation to livelihoods in the community. This study critically examines the physical mechanisms driving this event across scales. Employing regional reanalysis datasets, we elucidate the localized characteristics responsible for extreme precipitation and systematically address the associated uncertainties. The investigation revealed that the spatiotemporal dynamics of moisture transport played a vital role in the increased moisture influx over the region. In particular, the local convection combined with heightened atmospheric instability and intensified advection in the surrounding areas played a pivotal role in the formation of significant mid-tropospheric cyclones. These developed atmospheric phenomena are rarely observed in this region, which typically experiences tropical cyclones and depressions more frequently. This study emphasizes the necessity of conducting meticulous investigations to improve risk assessments and preparedness for future climatological phenomena of similar magnitude.

The study aims to investigate the moist dynamics that govern tropical precipitation variability at sub-seasonal timescales during the boreal summer monsoon season over the Indian region. For this purpose, we have used indigenously generated long-term (1981-2020) regional reanalysis data from the National Center for Medium-Range Weather Forecasting (NCMRWF) known as Indian Monsoon Data Assimilation and Analysis (IMDAA). Several process-oriented diagnostics (PODs) (e.g. column water vapor (CWV)-rainfall association; moist static energy budget (MSE), and Gross Moist stability (GMS)) are employed to examine the role of the vertical structure of specific humidity (q) and large-scale vertical motion (w) in representing some of the crucial moist processes necessary for tropical convection. Results are validated using the ERA5 reanalysis and the relative roles are quantified. Our examination suggests that despite having systematic biases in key variables responsible for monsoon convection, several aspects (mean state, vertical and space-time structures) of the sub-seasonal variability are captured well in the regional reanalysis system, which is encouraging. Nevertheless, diagnostics also reveal that the moisture-convection feedback mechanism is relatively weaker in IMDAA reanalysis, which is evident from the weak CWV-rainfall association. For instance, for lower CWV thresholds (<55 mm), IMDAA overestimates and produces excess rainfall over CI. Applying the vertical MSE budget to the IMDAA data demonstrates that the moisture and MSE advection terms act as leading components, suggesting strong predictability (∼7-10 days) signals in horizontal advection of moisture and MSE. The study underscores the limitation of IMDAA reanalysis vertical distribution of moisture in the time evolution of active and break monsoon phases. Further, study emphasizes the need for PODs in examining the fidelity of IMDAA reanalysis. It indicates the merits and demerits of IMDAA for a better understanding of the monsoon processes and model development.

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)

This study examines the long-term variability (1980-2022) of low-level clouds and their base heights using cloud observations from India Meteorological Department (IMD) over the Indo-Gangetic Plain (IGP) crucial for aviation. For this purpose, synoptic cloud observation data, coded as per world meteorological organisation (WMO) standards, were collected every three hours from four weather stations of IMD namely Amritsar, Delhi, Lucknow, and Patna. Highest prevalence of cloud types namely Stratus (St), Stratocumulus (Sc), Cumulus (Cu), and Cumulonimbus (Cb) was observed during monsoon than pre-monsoon. We have reported the occurrence of Cb clouds during monsoon in the range of 20-50%. Sc clouds show diurnal variation, peaking at 00, 03 UTC, and 15, 18, and 21 UTC. Cu and Cb clouds exhibit maxima in the afternoon during pre-monsoon and monsoon seasons, possibly due to the diurnal cycle of the atmospheric boundary layer height variations. Monsoon cases surpass pre-monsoon at all IGP sites. Notably, Cb with CBH 600-1000m causes maximum rainfall during monsoon, and predominant Cb base heights are 600-1000m, 1000-1500m, and 1500-2000m across decades.

This study aimed to investigate whether the current state of high-resolution operational numerical weather prediction (NWP) model forecasts has advanced to a level where they could supplant satellite-based precipitation estimates for real-time applications. Recent advancements in data assimilation, model physics, and computing power have enabled short-term NWP model forecasts to compete with satellite-based rainfall estimates, offering the added benefit of a high spatial and temporal resolution. This study evaluates the 24 hr rainfall forecast of three global NWP models: (i) the NCUM (National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model), (ii) the UKMO (United Kingdom (UK) Met Office (UKMO) model), and (iii) the IMD-GFS (Indian Meteorological Department (IMD)-Global Forecast System (GFS)) model along with the MEPS (multi-model ensemble predicted system) and two satellite precipitation products (IMERG: Integrated multi-satellite retrievals for global precipitation measurement and GSMaP: Global satellite mapping of precipitation) against the IMD gauge gridded observations for the 2016–2023 summer monsoon seasons at a daily scale. The findings indicate that 24 hr model forecasts are comparable to those of satellite-based products. In certain scenarios, these forecasts exhibited a better performance in capturing spatiotemporal variations and detecting precipitation, albeit with a slightly higher rate of false alarms at lower thresholds. The NCUM and UKMO models exhibit approximately similar forecast skills, yet are marginally better than the GFS model. However, MEPS shows a good prediction of spatiotemporal variation but struggles with weak precipitation overestimation and high-intensity precipitation underestimation. This indicates that a simple average of the multi-model product does not enhance rainfall frequency or magnitude prediction. GSMaP shows a limited ability to detect monsoon rainfall, whereas IMERG performs moderately better but consistently overestimates at all rainfall thresholds. These findings provide valuable insights for incorporating forecasted precipitation estimates into day-to-day weather monitoring over the Indian monsoon areas, serving as a useful reference for precipitation retrieval algorithms for future generations.

Rossby wave breaking (RWB) is a significant pathway for intrusion of stratospheric ozone into the troposphere. These events increase tropospheric ozone, which influences the greenhouse effect, atmospheric chemistry, and local ecosystems. As RWBs frequently affect the Indian subcontinent, a comprehensive study is required to understand the impact of RWB-induced ozone variations in the troposphere over the study region. To identify the RWB events, we used a contour searching algorithm and analyzed them for the period from 2004 to 2021 for Indian domain. Furthermore, we analyzed the anomalous ozone variability during the detected RWB event days using the CAMS global reanalysis (EAC4) and two independent satellite data sets, the Microwave Limb Sounder (MLS) and the Atmospheric Infrared Sounder (AIRS). Additionally, we utilized ground-based observations from the CPCB to examine the influence of RWB on the changes in surface ozone. The results of our study suggest that the CAMS reanalysis agrees well with the two independent satellite products, which provide a comprehensive understanding of ozone variability from various datasets. The upper-level potential vorticity anomaly allows ozone evolution to begin a few days before the strongest breaking time and intensify on the strongest day. Moreover, RWB enables the vertical intrusion of ozone down to 750 hPa, with variations observed from one case to another. Intrusion strength yields diverse tropospheric column increments (e.g., 190.5 ppbv at 100-150 hPa). Surface ozone response (850 hPa) to RWB correlates with intrusion intensity, resulting in 10-19 ppbv ozone anomalies. This could arise from the augmented tropospheric column ozone due to turbulent mixing. These findings deepen our understanding of RWB–related ozone variability and its impact on surface levels.

This study examines the efficacy of the National Center for Medium-Range Weather Forecasting (NCMRWF) global coupled unified model (CNCUM) hindcasts in depicting seasonal biases, focusing specifically on the Indian summer monsoon (ISM). Using 23 years (1993–2015) of coupled hindcasts driven by the UK Met Office GloSea5 seasonal prediction system, we analyze model biases in the mean state and sub-seasonal variability. High-resolution observations from, a key instrument aboard the Tropical Rainfall Measuring Mission (TRMM) satellite (hereafter referred to as TMI) SST and rainfall, along with GPCP rainfall data, are used for validation. The results indicate that while the CNCUM hindcasts satisfactorily portray key aspects of boreal summer monsoon over the Indian subcontinent and surrounding oceanic regions, systematic biases still persist. Notably, there is a significant wet bias ( 6–8 mm/day) during July and August over the western Indian Ocean, and dry biases dominate large regions of the Indian subcontinent. The annual cycles of SST and rainfall over the equatorial Indian Ocean are relatively weak compared to other monsoon regions. Additionally, the model exhibits a leading nature of SST anomalies ( 5–7 days) during active-break monsoon periods, which is encouraging, but also reveals inconsistencies in the SST-rainfall relationship over the Bay of Bengal. A novel contribution of this study is the identification of a strong association between rainfall biases and free tropospheric (700 − 400 hPa) moisture distribution. This finding highlights the need for a deeper understanding of moist processes within the CNCUM modelling system. By highlighting both the strengths and caveats in the model’s performance, this research provides valuable insights and suggests pathways for future model development and improvement.

Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 offers high temporal and spatial resolution global satellite precipitation estimates, with data available from the mid-2000s onwards. The National Aeronautics and Space Administration (NASA) has recommended that researchers transition from the widely used Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA-3B42) to the new IMERG product. Recently, IMERG has been upgraded from Version 6 (V6) to Version 7 (V7), encompassing significant changes in data accuracy and retrieval algorithms. This study evaluated the performance of IMERG V7 (IMERGF-V7 and IMERGL-V7) and its predecessors, IMERGF V6 (IMERGL-V6 and IMERGF-V6) and TMPA (3B42RT V7 and 3B42 V7), to capture the spatial and temporal variations in rainfall, with a specific focus on their applicability to southwest monsoon precipitation over India before the GPM period (JJAS 2000–2013). The study also examined the changes in the error characteristics from V6 to V7 in the IMERG data and compared them with the TMPA data. The results indicate that across India, Gauge based products generally perform much better than real-time products. Hence, the IMERGL-V6 real-time product is comparable to the TMPA-3B42 and IMERGF-V6 research products based on a few statistical and categorical analyses. In addition, IMERG V6 did not show a significant improvement than TMPA-3B42 during the study period. Overall, IMERG V7 demonstrated comparable improvement in identifying monsoon rainfall compared with the TMPA-3B42 and IMERGF-V6 products. However, it overestimated lower rainfall quantities and underestimated instances of heavy rain, which needed to be enhanced in the next release. The results indicate that, IMERG V7 products can serve as a suitable replacement for TMPA products when studying the Indian monsoon. Insights from this evaluation will be valuable for algorithm developers, satellite rainfall product users, and researchers seeking reliable precipitation data for the region.

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.

This study presents a comprehensive performance evaluation of the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis total aerosol optical depth (AOD) over India. We first use AOD observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003 to 2020 to evaluate the spatial and temporal patterns of AOD simulated by CAMS. Owing to the lack of aerosol speciation in MODIS, we complement it with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), which provides individual aerosol species such as dust, black carbon, organic carbon, sulfate, and sea salt. The results demonstrate that CAMS exhibit high AOD, similar to MODIS, particularly over the Indo-Gangetic Plain, despite some underestimations in total AOD. Temporal trend analysis indicates that CAMS exhibited rising AOD trends similar to MODIS across Indian regions, though discrepancies arise in western India during pre-monsoon and monsoon seasons. Spatial differences in different aerosol species between CAMS and MERRA-2 suggest potential differences in model parameterizations. Principal component analysis (PCA) further reveals that the first mode of AOD (PCA-1) in both CAMS and MERRA-2 shows a strong correlation (> 0.7) with MODIS during all seasons except pre-monsoon. This enhances our understanding of aerosol distribution and its implications for regional climate and air quality. Consequently, this study provides valuable insights into the performance of CAMS, supporting advancements in climate modeling, air quality management, and environmental policy-making in densely populated and rapidly developing regions.

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.

This study explores the application of National Centre for Medium Range Weather Forecasting Unified Model-Regional (NCUM-R) modelling framework to simulate convective rainfall events during active summer monsoon conditions in the Indian region. The primary focus is on assessing the impact of assimilating reflectivity and radial wind data (RAD) from the Indian Doppler Weather Radar (DWR) networks with respect to control (CTL) experiment. By comparing model-simulated rainfall with merged satellite-rain gauge data, the analysis demonstrates that assimilation significantly enhances the accuracy of the analysis compared to relying solely on the background model analysis. The assimilation system with DWR data shows a substantial reduction in biases, indicating improved alignment with observations. Notably, assimilation of DWR observations positively influences wind patterns and rainfall predictions, particularly in regions with deep convection, leading to enhanced wind speeds and rainfall accuracy. The evaluation of RAD and CTL experiments is based on different rainfall categories, such as light-to-moderate and moderate-heavy rain events, highlighting the significant impact of assimilation of DWR observations on forecasts. The RAD experiment surpasses the CTL in simulating rainfall, reducing biases, and enhancing predictive capabilities, especially showing better performance in initial forecast hours and reduced gradually. Various statistical skill scores like Probability of Detection (POD), False Alarm Rate (FAR), Equitable Threat Score (ETS), and Frequency Bias (FBIAS) highlight the improved performance of the RAD experiment in predicting rainfall amount and location, particularly for moderate to heavy rainfall events. Therefore, the assimilation of reflectivity and radial velocity data improves dynamic and thermodynamic fields, enhancing wind convergence and cloud prediction accuracy of convective system. Further research is recommended to optimize background error settings and observational error specifications for convective systems over the Indian region.