This study investigates a multi-day extreme rainfall event (MERE) that occurred on 8–9 July 2023 in the Indian Himalayan mountainous region (IHMR). This catastrophic MERE, accompanied by exceptionally heavy rainfall reaching up to 424 mm over two days in Ropar, with 286 mm recorded in a single day in Chandigarh triggered flash floods, landslides, and debris flows, resulting in severe devastation, significant loss of life (91 fatalities), and extensive economic damage. Given that the skill of the Weather Research and Forecasting (WRF) model in simulating heavy rainfall is highly dependent on the choice of cloud microphysics (CMP) in a convective-permitting modelling framework, sensitivity analysis has been conducted using five CMP schemes to assess their performance to identify the best suitable CMP for this event in the study region. The WRF model was configured with three nested two-way-interacting domains (18 km, 6 km, and 2 km) to assess the performance of these CMP schemes in a convective-permitting simulation framework. Our study demonstrates the skillfulness of WRF to forecast the MERE of July 2023 over IHMR at 2 km resolution domain and also resolve the local and synoptic-scale interactions. These results show that all the CMP configurations (with 2-km resolution) were able to reproduce heavy rainfall; however, among the five CMP schemes, the Aerosol-Aware Thompson (AA-Thompson) scheme demonstrated the best agreement with IMERG observations, exhibiting the most reliable spatial and temporal distribution of rainfall, minimal bias, and a lower root mean square error compared to the other schemes. Furthermore, with the best CMP configuration, we evaluate the predictive capability of the WRF-based convective modelling framework (2-km horizontal grid spacing) for forecasting extreme rainfall at different lead times (72 h, 48 h and 24 h ahead). The analysis reveals that the 24-hour lead time prediction of extreme rainfall aligns most closely with IMERG observations, while discrepancies in precipitation amount and location become more pronounced at 48- and 72-hour lead times. Consequently, the index of agreement decreases from 0.59 to 0.45 against ERA5 and from 0.45 to 0.41 against IMERG. Overall, the study highlights the predictive capability of mountain MEREs and the complex interaction of synoptic features, moisture dynamics, and large-scale circulations in generating extreme rainfall over the study region.

This study investigates future projections of the extreme Humidex, a compound index integrating air temperature and relative humidity, as an indicator of heat stress under two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5). Using multi-model ensemble simulations from 15 high-resolution CMIP6 models, we examine seasonal variations in the extreme Humidex at both global and regional scales across 21 land regions. The historical simulations from CMIP6 models were validated using ERA5 observational data, ensuring reliability and accuracy in model projections. Additionally, the climatology of Humidex values derived from MME simulations was compared with the climatology of the Heat Index obtained from MME simulations, revealing consistency between the two heat stress indicators. To quantify heat-related hazards, we calculate the number of seasonal Dangerous Humidex Days (DHDs). Furthermore, we assess the relative contributions of air temperature and relative humidity to projected changes in the extreme Humidex. The results reveal a marked intensification of heat stress under SSP5-8.5, with late-century (2080-2100) increases in the extreme Humidex reaching approximately 5 to 9 degrees C across most land regions, compared to around 1.5 to 3.5 degrees C under SSP1-2.6. The JJA season exhibits the greatest intensification, particularly over Eastern North America and Northern Asia. On average, DJF shows a 2.78-fold increase in Humidex anomalies, while JJA demonstrates an even larger amplification of approximately 2.90-fold when transitioning from a low to high-emission scenario. The number of seasonal Dangerous Humidex Days also increases significantly, with an additional 60 to 80 days during DJF across many tropical and subtropical regions of the Southern Hemisphere, and more than 80 additional days per JJA season in most tropical regions under the high-emission scenario. Sensitivity analyses indicate that air temperature is the dominant driver of future changes in the extreme Humidex, while relative humidity exerts a secondary but regionally important influence.

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.

An in-house-developed millimeter-wave humidity sounder onboard EOS-07 (EOS-07 MHS), launched in February 2023, operates at six frequencies around the 183.3 GHz water vapor absorption band. This study presents a preliminary performance assessment of EOS-07 MHS, including brightness temperature validation, humidity profile retrieval methodology and its validation. Under clear-sky conditions, the biases in brightness temperature measured by EOS-07 MHS, relative to RTTOV simulations were within +1 K, except for channels 1 and 6. Similarly, intercomparisons with ATMS observations showed biases within +1 K and a standard deviation of 2-3 K. A random forest-based method was employed to retrieve specific humidity profiles from EOS-07 MHS observations demonstrated agreement with ERA5 reanalysis and radiosonde observations. Compared with radiosonde data, the mean bias and standard deviation of retrieved specific humidity were approximately 0.78 g/kg and 2.3 g/kg, respectively. The mean percentage bias was within +20 % below the 800 hPa pressure level, and ranged between +20 % and + 40 % above the 800 hPa pressure level. Relative to ERA5, the mean bias and rootmean-square deviation (RMSD) were under 30 % and 50 %, respectively. The estimated total precipitable water vapor showed a mean bias of 1.7-3.1 mm and a standard deviation of 5.2-5.7 mm compared to ERA5. Additionally, the EOS-07 MHS data were assimilated into the WRF model, resulting in improved atmospheric analyses and forecasts. A month-long cyclic assimilation experiment demonstrated consistent enhancements in moisture representation across the lower and middle atmosphere.

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.

This study presents the assimilation impact of all-sky water vapour (WV) radiance observations from the recently launched Indian geostationary satellite (INSAT-3DS) into the Weather Research and Forecasting (WRF) model. To evaluate the impact of INSAT-3DS data, three identical assimilation experiments were conducted in July 2024 over South Asia: a control run (WCNT) without INSAT-3DS Imager WV radiance assimilation, a clear-sky WV radiance assimilation run (WCLR), and an all-sky WV radiance assimilation run (WCLD). The assimilation impact was assessed by comparing the WRF analyses simulated brightness temperature (T-B) against independent satellite observations from Advanced Technology Microwave Sounder (ATMS), High-Resolution Infrared Sounder/4 (HIRS/4), and Microwave Humidity Sounder (MHS) sensors. Results demonstrate that all-sky assimilation significantly increases the number of assimilated observations (similar to 300 %) compared to clear-sky assimilation, leading to analyses that are more consistent with independent satellite measurements. Short-range forecast evaluations confirm the advantages of all-sky radiance (ASR) assimilation, with improved predictions of simulated WV T-B, moisture, and temperature fields compared to clear-sky radiance (CSR) assimilation. When verified against INSAT-3DS WV channel observations, WCLD forecasts consistently exhibit reduced bias and root mean square deviation (RMSD) compared to WCLR and WCNT forecasts. These results highlight the potential of ASR assimilation to enhance the accuracy of the WRF model predictions, particularly in summer monsoon-affected regions where cloud-affected radiances contain crucial atmospheric information. Overall, this study underscores the importance of ASR assimilation in improving the representation of atmospheric moisture and advancing short-range weather forecasts.

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.

The aim of this study is to compare the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) reanalysis and the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analysis simulated all-sky brightness temperature (TB) against the water-vapour (WV) absorption channels of Cross-track Infrared Sounder (CrIS) on-board NOAA-20 satellite, during severe cyclonic storm “Remal” (24 - 28 May 2024). Such comparisons offer valuable insight for future analysis/reanalysis development, even though clear-sky CrIS observations are currently part of their assimilation system. Both the ERA5 and NCEP GDAS capture the large-scale TB patterns associated with the tropical cyclone (TC), but ERA5 exhibits small root-mean-square difference (RMSD) in the upper atmospheric WV channels. The value of bias is consistently lower in the ERA5 reanalysis as compared to the NCEP GDAS analysis in the three selected WV channels representing upper, middle, and lower levels. The ERA5 reproduces cold-core and rain-band features with smaller bias and RMSD patterns, whereas the NCEP GDAS shows larger differences and pronounced warm biases in the cyclone core. To quantify the influence of hydrometeors, cloud effect average (CA) parameter and mutual information (MI) analysis were performed, allowing hydrometeors contributions to be assessed alongside other control variables. MI values in ERA5 decrease significantly within convective cores of the TC, where cloud tops dominate radiative signals. Overall, this study highlights the importance of hyperspectral infrared measurements for comparing the numerical weather prediction (NWP) model analysis/reanalysis under all-sky conditions.

Desert Locust (DL) infestations pose a significant threat to food security in arid and semi-arid regions, particularly in East Africa, Central Asia, and the Indian subcontinent. In 2020, during the COVID-19 pandemic, India witnessed an unprecedented upsurge of DL activity during the summer (zaid) season (April-June), severely impacting Rajasthan, Gujarat, and neighbouring states. This study investigates the environmental drivers of the DL outbreak and assesses crop damage using geospatial datasets, reanalysis products, and numerical weather models. Fifteen grid cells (100 km x 100 km) along the DL-prone corridor from East Africa to India were analyzed for environmental suitability, with seasonal Spearman correlation analysis applied to identify significant factors influencing locust activity. In winter, locust activity was significantly positively correlated with rainfall (rho = 0.47, p = 0.021), dew point temperature (rho = 0.76, p = 0.01), and soil moisture (rho = 0.50, p = 0.05), highlighting the importance of moisture and temperature conditions in facilitating locust presence. In spring, significant positive correlations were observed with air temperature (rho = 0.56, p = 0.027), soil temperature 1 (rho = 0.65, p = 0.01), and a very strong correlation with soil temperature 2 (rho = 0.73, p = 0.002). These findings showed the crucial role of temperature and moisture during the winter and spring seasons as key drivers of locust behaviour. The Linear Discriminant Analysis (LDA) model shows potential in locust presence prediction, though challenges remain due to data limitations. Crop damage was quantified using Normalized Difference Vegetative Index (NDVI), showing severe vegetation loss in affected areas (NDVI <0.3) and degradation due to locust feeding. The study further integrates weather forecast wind patterns, MODIS Leaf Area Index (LAI), and soil moisture from SMAP to track locust migration. Wind patterns, particularly westerly and south-westerly winds, guided the locusts' entry into western India. Despite moderate LAI values, the vegetation cover in central and western India provided sufficient sustenance for the locusts. Soil moisture from SMAP consistently supported locust dispersal across northern Rajasthan, central India, and parts of Uttar Pradesh. The integration of these environmental factors offers a comprehensive understanding of DL behaviour, enhancing early warning and control efforts.

This study aims to evaluate the Pangu-Weather machine learning (ML) model against the operational numerical weather prediction (NWP) model of the National Centres for Environmental Prediction Global Forecast System (NCEP GFS). The initialisation and integration of the Pangu-Weather ML model, utilising NCEP GFS and Global Data Assimilation System (GDAS) analysis, and evaluating its performance alongside NCEP GFS operational forecasts is one of the major scientific problems addressed in this study. The European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset is used to validate the 240-hour predictions made by the NCEP GFS and Pangu-Weather ML models for the South Asia region between May 1 and 15, 2024. Based on the results, surface temperature, pressure, and wind speed may all be predicted more accurately. Improvements in temperature and moisture forecasts are the primary areas where these beneficial effects are observed at various levels of the atmosphere, becoming more pronounced with longer forecast periods. The results of this study open new avenues for investigating how machine learning can be combined with more conventional dynamical models, in addition to demonstrating how effectively the Pangu-Weather ML model improves forecasting accuracy. This synergy holds the promise of advancing meteorological forecasting capabilities, particularly in regions characterised by high temperature and humidity variability.

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)

Earth science has embraced the application of deep learning (DL) across various fields. The research aimed to enhance the Analog Data Assimilation (AnDA) approach by integrating a DL technique. This involved using a representative catalog of the dynamical model to rebuild the system dynamics. The outcome of this was the development of the Deep Data Assimilation (DeepDA) technique, which uses ensemble-based assimilation methods like the Ensemble Kalman Filter (EnKF) and Particle Filter (PF) along with DL to model system dynamics. To achieve this, an artificial recurrent neural network with a long short-term memory (LSTM) architecture was utilized for data-driven forecasting. To assess the effectiveness of DeepDA as compared to the AnDA model-driven assimilation methods, a series of numerical experiments were conducted using the chaotic dynamical model Lorenz-63. The results demonstrated that DeepDA exhibits highly efficient computational capabilities and satisfactory prediction accuracy and skills compared to AnDA.

Central Indian Region (CIR), which lies in the core monsoon zone, is susceptible to Widespread EREs (WEREs) during the Indian Summer Monsoon (ISM) season, significantly affecting the rainfed agriculture and ultimately the economy of the region. Though the dynamics of ISM extremes have been studied before, a little attention has been paid to addressing the vertical moist thermodynamic structures of atmosphere which may also play a decisive role in the evolution of such WEREs. This study uses ERA5 and INSAT-3D atmospheric sounding datasets, to investigate the evolution of dynamic and moist thermodynamic characteristics of the vertical atmosphere, preceding the WEREs over CIR. Additionally, it also attempts to attribute the dynamic and thermodynamic contributions of moisture advection to atmospheric water budget during these extremes. Results indicate that the WEREs are primarily contributed by large scale precipitation over the CIR. Results also suggest that it is the large-scale moisture influx (aggravated by the local dynamic and thermodynamic factors) that mainly leads to the advent of these extremes. A strong warming in the middle troposphere, cooling in the lower troposphere and higher moisture accumulation throughout the middle to lower troposphere are also observed to be associated with the extreme days. This study also brings out that vertical advection of moisture is the major contributor to the moisture budget. Furthermore, the dynamic term (enforced by the large scale circulation driven vertical velocity anomalies) contributes > 90

This study aims to evaluate the performance and accuracy of Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) and India Meteorological Department (IMD) gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP_ISRO) rainfall products over Indian landmass during the Indian Summer Monsoon (ISM) from 2000 to 2022. An extensive validation against rain gauge observations reveals that GSMaP_ISRO exhibits stronger agreement with gauge measurements than CHIRPS. GSMaP_ISRO accurately captures day-to-day variations in mean rainfall across the Indian mainland, whereas CHIRPS tends to slightly overestimate mean rainfall. Correlation and root-mean-square difference metrics highlight the superior performance of GSMaP_ISRO during ISM. Spatial analysis reveals notable discrepancies between the datasets, especially over complex terrains like the Western Ghats and northeastern India, where CHIRPS and GSMaP_ISRO show significant divergence. Additionally, the study explores the applicability of selected rainfall products for meteorological drought monitoring using the monthly Standardized Precipitation Index (SPI-1). Results suggest that GSMaP_ISRO exhibits greater spatial variability in rainfall distribution while maintaining coherence with CHIRPS in drought-prone regions. Overall, GSMaP_ISRO emerges as a more reliable dataset for monsoon studies in India, offering improved accuracy and spatial variabilities for drought monitoring applications.

An extreme forest fire event occurred in the Uttarakhand region of northern India, impacting air quality from April 23–27, 2024. This study examines the impact of assimilating aerosol optical depth (AOD) data, retrieved from the Earth Observation Satellite (EOS-06) Ocean Color Monitor (OCM-3) sensor, into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The assimilation of OCM-3 related AOD data significantly enhances the accuracy and reliability of fire detection and prediction. Statistical analyses show a notable reduction in bias ( 61

ABSTRACTHigh resolution remotely sensed rainfall data is indispensable to accurately monitor rainfall variability at a river basin scale under climate change. The aim of the study is to assess the change in rainfall climatology and determine the performance of three GPM rainfall products (IMERG Final_Run, GSMaP_Gauge and recently developed GSMaP_ISRO) against in situ observation over major Indian River basins for the monsoon season rainfall during 2000–2020. The analysis provides valuable insights into the issues in the rainfall products and their performance under varying rainfall intensity (low, moderate and heavy) and orography. Results indicate that mean monsoon rainfall is better represented in GSMaP_ISRO than IMERG Final_Run and GSMaP_Gauge estimates. GSMaP_ISRO outperformed IMERG Final_Run and GSMaP_Gauge with smaller root‐mean‐square error and higher correlation coefficient. It was observed that the performance of GPM rainfall is influenced by rainfall intensity and terrain height of basins. In particular, for high rainfall occurrence, the Brahmaputra and Barak basins in northeast India exhibited large uncertainties in IMERG Final_Run and GSMaP_Gauge products. The statistical evaluation of different rainfall scores (POD, FAR and CSI) suggested that the GSMaP_ISRO rainfall has significant skill over Indian river basins. A significant improvement is observed over Brahmaputra (RMSD = 34.46, CC = 0.92, FAR = 0.26), Barak and others (RMSD = 65.75, CC = 0.95, FAR = 0.29) and Cauvery basin (RMSD = 23.1, CC = 0.8, FAR = 0.15) for GSMaP_ISRO estimates. These findings provide valuable information on the accuracy of GPM rainfall necessary for hydro‐meteorological applications.

EOS-06 Scatterometer is a continuity mission of SCATSAT-1 and carries a Ku-band Scatterometer with a scanning pencil beam configuration. It deploys two beams, a vertically polarized outer beam and a horizontally polarized inner beam, to cover a swath of 1800 km. The mission mainly caters to ocean wind measurements for oceanographic applications and weather forecasting, with the data being extensively used for cyclogenesis predictions across the globe, specifically, the tropical regions. In order to ensure data quality and suitability for operational applications, the data products need to be extensively calibrated and validated. This paper discusses the generation, calibration, validation and retrieval aspects of EOS-06 Scatterometer data products carried out in the Indian Space Research Organization (ISRO).