The increase in the frequency of extreme precipitation in different parts of the world is well documented and a cause for concern in terms of global climate change. Although clouds are the only source of precipitation, there is a lack of knowledge about the type of clouds involved in extreme precipitation events. Satellite- and ground-based observations show that over the central Indian region (19 degrees-26 degrees N, 75 degrees-85 degrees E), about 60% of the extreme precipitation comes from deep convective clouds (DCCs), which have a cloud-top pressure (CTP) of less than 440 hPa and a cloud optical thickness (COT) of over 23. It was also observed that cloud liquid water (CLW) and COT show the highest correlation with extreme precipitation. Furthermore, CLW and COT show the highest contrast between extreme and nonextreme precipitation events. Simulations by a 12-km Global Forecast System (GFS, spectral) model show that the model underestimates the extreme precipitation threshold with increased forecast lead time. The simulation of associated cloud optical parameters is also poor at all lead times in different parts of India. The model also fails to capture the observed relationship between the frequency of extreme precipitation and deep convective clouds without showing any correlation between them at all lead times. This is possibly because the model simulates the observed vertical structure of the apparent heat source poorly at all lead times which leads to poor simulation of the observed relationship between cloud optical properties and extreme rainfall. SIGNIFICANCE STATEMENT: The occurrence of extreme rainfall events is on the rise across the globe, and India is no exception. Clouds are the only source of rainfall; however, there is a lack of understanding about what type of clouds is causing them. Therefore, it is necessary to understand the relationship between cloud, convection, and extreme rainfall events during the Indian summer monsoon months. Furthermore, it is also important to investigate the skill of the current-generation global models to simulate them. This study highlights how cloud optical properties, convection, and extreme rainfall events are related to each other and also fi nds that a 12-km GFS model simulates these observed relationships poorly at all lead times leading to the poor simulation of extreme rainfall events.

Abstract. Volatile organic compounds significantly impact the atmospheric chemistry of polluted megacities. Delhi is a dynamically changing megacity and yet our knowledge of its ambient VOC composition and chemistry is limited to few studies conducted mainly in winter before 2020 (all pre-covid). Here, using a new extended volatility range high mass resolution (10000–15000) Proton Transfer Reaction Time of Flight Mass Spectrometer10K, we measured and analyzed ambient VOC-mass spectra acquired continuously over a four-month period covering “clean” monsoon (July–September) and “polluted” post-monsoon seasons, for the year 2022. Out of 1126 peaks, 111 VOC species were identified unambiguously. Averaged total mass concentrations reached ~260 µgm-3 and were >4 times in polluted season relative to cleaner season, driven by enhanced emissions from biomass burning and reduced atmospheric ventilation (~2). Among 111, 56 were oxygenated, 10 contained nitrogen, 2 chlorine, 1 sulphur and 42 were pure hydrocarbons. VOC levels during polluted periods were significantly higher than most developed world megacities. Surprisingly, methanethiol, dichlorobenzenes, C6-amides and C9-organic acids/esters, which have previously never been reported in India, were detected in both the clean and polluted periods. The sources were industrial for methanethiol and dichlorobenzenes, purely photochemical for the C6-amides and multiphase oxidation and partitioning for C9-organic acids. Aromatic VOC/CO emission ratio analyses indicated additional biomass combustion/industrial sources in post-monsoon season, alongwith year-round traffic sources in both seasons. Overall, the unprecedented new information concerning ambient VOC speciation, abundance, variability and emission characteristics during contrasting seasons significantly advances current atmospheric composition understanding of highly polluted urban atmospheric environments like Delhi.

With the changing climate, the study of fog formation is essential due to the impact of the complexity of natural and anthropogenic aerosols. The evolution of the droplet size distribution in the presence of different aerosol species remains poorly understood. To make progress towards reducing the uncertainty of fog forecasts, the Eulerian–Lagrangian particle‐based small‐scale model for the diffusional growth of droplets is used to better understand the droplet activation and growth. The small‐scale model simulations are performed using observed data from the Winter Fog Experiment study over Indira Gandhi International Airport, New Delhi. The microphysical properties, such as droplet number concentrations (Nd) and liquid water content (LWC), important for fog simulation, are evaluated to gain more insights. The small‐scale simulations have shown the droplet microphysical properties at different evolutionary stages. The Nd and effective radius change with variations in LWC for different aerosol chemistries (i.e., organics, mix, and inorganic). The calculated visibility at small scale is also shown with the variation of Nd and LWC. This study compared visibility from an existing parametrization with parcel–direct numerical simulation calculation. The hygroscopicity , which is highly related to the activation of aerosols to condensation nuclei, is taken into account to demonstrate the contribution of aerosol chemistry to fog droplet formation. The results highlight that hygroscopicity is essential in the numerical model for fog and visibility prediction as the microphysical properties of fog are regulated by aerosol species.

Air pollution poses a significant environmental risk to large cities worldwide, including New Delhi, India's capital. The occurrence of frequent episodes of elevated levels of air pollution during October-March in Delhi and National Capital Territory (Delhi-NCT) chokes its similar to 32 million residents every year. Current air quality models lack the ability to accurately predict severe air pollution events in Delhi-NCT, rendering decision-makers helpless in their efforts to safeguard public health. To address this, a new initiative introduced a high-resolution Air Quality Early Warning System (AQEWS) in 2018, followed by the integration of a decision support system (DSS) in 2021. This enhancement enables dynamic source attribution data and diverse emission reduction scenarios within a single model forecast. The newly developed system, Air Quality Warning and Integrated Decision Support System for Emissions (AIRWISE), assimilates near-real-time satellite aerosol optical depth (AOD) retrievals, satellite-based fire information, surface data from 320 air quality monitoring stations, and high-resolution emissions, resulting in an extensive modeling framework. This framework demonstrates exceptional prediction capabilities, accurately forecasting very poor air quality episodes up to 3 days in advance with a remarkable 83% accuracy, even at a street-level resolution of 400 m. The AQEWS is the world's first operational air quality forecasting system operating at a high resolution and incorporating chemical data assimilation. The Commission for Air Quality Management (CAQM) relies on forecast data to enforce the Graded Response Action Plan (GRAP) in Delhi-NCT, which imposes restrictions on pollution sources. This paper outlines the AQEWS and DSS, summarizes modeling experiments, verifies forecasts, and discusses challenges in accurately predicting extreme pollution episodes.

This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–2022 shows high accuracy (up to 80 %) and a very low false alarm ratio (∼ 20 %) from day 1 to day 5 of the forecasts, especially when the ambient air quality index (AQI) is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the National Capital Region (NCR)'s districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to the PM2.5 load in Delhi. During peak pollution events (stubble-burning periods or wintertime), however, the contribution from the main sources (farm fires in Punjab–Haryana or local sources within Delhi) could reach 65 %–69 %. According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios.

Self-recording rain gauges hourly rainfall data from 1969 to 2010 have been utilized to identify rain events at a sub-daily scale. At the sub-daily scale, a significant decrease in the frequency of heavy rainfall events (HREs) is observed over central India and northeast India, while an increase is observed over the northern west coast of India. Frequency of short-duration HREs over central India and long duration HREs over northern west coast of India is increased in the recent decades than in earlier decades. Incongruity with the observations, CMIP6 historical and AMIP high temporal resolution models are not able to simulate the short-duration HREs and, in turn, the observed trends at a sub-daily scale over the India landmass. The inability of CMIP6 models to predict short-duration HREs suggests caution in predicting future projections of extreme precipitation at a sub-daily scale and highlights the need for further improvements in climate models.

Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993-2015, we assess NERP's strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model's dynamics, emphasizing tropical and extratropical influences. fl uences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden-Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies fi es MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper- level dynamic interactions, demonstrating NERP's ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon fl ow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.

The frequency and intensity of fog episodes during the winter season has been increasing during the past decade over the megacity of Delhi due to the high pollution load. The role of atmospheric aerosols is very important in the life cycle of fog in the urban areas. This paper presents the results on the variation in aerosol optical properties (scattering and absorption coefficients) and the black carbon (BC) mass concentration during the foggy period in winter (December 2015 to February 2016) at the Indira Gandhi International (IGI) Airport, New Delhi. The interaction between scattering and absorbing aerosols, and fog before, during and after the foggy period has been studied as a typical case. The BC mass concentration, along with the aerosol scattering and absorption coefficients, increased before and during the initial phase of the dense foggy period. However, there was a steep decrease in them after the sustained period of dense fog, which suggests possible scavenging by fog droplets. Also, it was observed that the decrease in ambient temperature and depression temperature (DT) and the increase in relative humidity (RH) played a major role in sustaining the dense fog despite the reduction in aerosol load. The single-scattering albedo (SSA) decreased during the dense fog due to a higher reduction of the scattering aerosols than the absorbing ones. Both the scattering and the absorption coefficients showed a significant correlation with cloud condensation nuclei (CCN).

This study examines the asymmetry in the Indian summer monsoon rainfall (ISMR) response over India and its four homogeneous regions to two distinct types of temporal evolution in La Ni & ntilde;a. We have shown this uneven response by analysing the large-scale dynamics over tropical Indo-Pacific region for the period 1951-2022. We have identified two types of La Ni & ntilde;a events during monsoon season (June-September) based on whether they evolved from El Ni & ntilde;o or La Ni & ntilde;a from preceding boreal winter season (December-February). India receives significantly more (less) rainfall during La Ni & ntilde;a years, when it was preceded by El Ni & ntilde;o (La Ni & ntilde;a) in the preceding winter. We further observed the spatial diversity of rainfall over India with a northeast-southwest dipole pattern. When La Ni & ntilde;a years were preceded by El Ni & ntilde;o, positive surface pressure anomaly over west-north Pacific, low-level westerlies and moisture transport favoured the rainfall over south peninsula and west-central India. Whereas moisture divergence associated with anomalous lower-tropospheric anticyclone over west-north Pacific suppressed the rainfall over Indo-Gangetic plains. However, when La Ni & ntilde;a years were preceded by La Ni & ntilde;a in winter, the absence of westerlies and weak moisture transport subdued the rainfall over south peninsula and west-central India. At the same time, moisture convergence and a greater number of monsoon depressions favoured rainfall over north-west India. This study also looked at how well eight Copernicus Climate Change Service (C3S) models predicted ISMR and SST for two types of La Ni & ntilde;a with April initial conditions during the period 1993-2016. Models were able to capture the spatial pattern of SST anomalies over Indo-Pacific Ocean, but all models could not capture the spatial pattern of ISMR. However, in terms of intensity, six out of eight models could predict more (less) ISMR when it was preceded by El Ni & ntilde;o (La Ni & ntilde;a), coinciding with the observed anomaly. The present study shows the asymmetry in the Indian summer monsoon rainfall response over all-India and its four homogeneous regions to two distinct types of temporal evolution of La Ni & ntilde;a, by analysing the sea surface temperature, moisture transport and large-scale dynamics over the tropical Indo-Pacific region for the period 1951-2022. image

Volatile organic compounds (VOCs) significantly impact the atmospheric chemistry of polluted megacities. Delhi is a dynamically changing megacity, and yet our knowledge of its ambient VOC composition and chemistry is limited to few studies conducted mainly in winter before 2020 (all pre-COVID-19). Here, using a new extended volatility range high-mass-resolution (10 000-15 000) proton transfer reaction time-of-flight mass spectrometer, we measured and analysed ambient VOC mass spectra acquired continuously over a 4-month period, covering "clean" monsoon (July-September) and "polluted" post-monsoon seasons, for the year 2022. Out of 1126 peaks, 111 VOC species were identified unambiguously. Averaged total mass concentrations reached similar to 260 mu g m(-3) and were > 4 times in the polluted season relative to the cleaner season, as driven by enhanced emissions from biomass burning and reduced atmospheric ventilation (similar to 2). Among 111, 56 were oxygenated, 10 contained nitrogen, 2 chlorine, 1 sulfur, and 42 were pure hydrocarbons. VOC levels during polluted periods were significantly higher than most developed world megacities. Methanethiol, dichlorobenzenes, C6 amides, and C9 organic acids/esters, which have previously never been reported in India, were detected in both the clean and polluted periods. The sources were industrial for methanethiol and dichlorobenzenes, purely photochemical for the C6 amides, and multiphase oxidation and partitioning for C9 organic acids. Aromatic VOC / CO emission ratio analyses indicated additional biomass combustion/industrial sources in the post-monsoon season, along with year-round traffic sources in both seasons. Overall, the unprecedented new information concerning ambient VOC speciation, abundance, variability, and emission characteristics during contrasting seasons significantly advances current atmospheric composition understanding of highly polluted urban atmospheric environments like Delhi.

To investigate the mechanisms responsible for severe convection linked with a variety of mesoscale, synoptic systems over the Indian region, a preliminary analysis is conducted. Using IMD RSRW/Reanalysis (ERA5)/Lightning (TRMM LIS) products both local-scale thermodynamics and large-scale background conditions responsible for the major lightning hot spots in different seasons are investigated. In the pre-monsoon season, high lightning activity over NE Indian region is due to moisture intrusion by south-westerly at lower levels (warm air advection), along with mid-level north westerlies (cold air advection) which favour the low-level convergence and upper-level divergence. SCS over south India are mainly attributed to wind discontinuity over the region. During monsoon season major lightning activity in NW India is due to the presence of heat low and the rest of the country less intense SCS activity is evident due to high wind shear conditions in connection with low-level (westerly) and upper-level (easterly jet stream). In the post-monsoon season, major lightning in south India are due to the prevailing north-easterlies and in north India, the activity is attributed to the passage of western disturbance (WD). Station plot analysis suggests the varied distribution of thermodynamic indices season/region-wise due to the diverse background forcing. Correlation analysis between the lightning and thermodynamic indices suggests that more skillful indices are KI (similar to 0.7 to 0.8), precipitable water (PPW) (similar to 0.8) (pre-monsoon, monsoon) and (PPW) (similar to 0.8), convective available potential energy (CAPE) (similar to 0.6) in post monsoon over respective lightning hot-spot regions. Independent verification using Wyoming data/reanalysis for different case studies suggests that monitoring the monthly thresholds of highly correlated indices provides a helpful proxy to operationally forecast the severe convective systems (season/region). Mean Equivalent potential temperature, and mixing ratio in mixed layer has shown reasonable association with the lightning occurrence over all lightning hot spot regions which are implemented in the IMD GFS and IMD WRF models.

Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF-Urban Asymmetric Convective Model (WRF-UACM) for Delhi, India, integrating explicit urban physics with Sentinel-updated USGS land-use and urban morphological parameters derived from the UT-GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF-BACM) using Winter Fog Experiment (WiFEX) data, WRF-UACM significantly improved urban meteorological variables like diurnal variation of 10-meter wind speed, 2-meter air temperature (T2), and 2-meter relative humidity (RH2) on a fog day. UACM also demonstrates improved accuracy in simulating temperature and a significant reduction in biases for RH2 and wind speed under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF-BACM, WRF-UACM reduces the cold bias soon after the sunset, thus improving the fog onset error by ~4 hours. This study underscores the UACM’s potential in enhancing fog prediction, urging further exploration of various fog types and its application in operational settings, thus offering invaluable insights for preventive measures and mitigating disruptions in urban regions.

Volatile organic compounds (VOCs) and particulate matter (PM) are major constituents of smog. Delhi experiences severe smog during the post-monsoon season, but a quantitative understanding of VOCs and PM sources is still lacking. Here, we conduct a source apportionment study for VOCs and PM using a recent (2022), high-quality dataset of 111 VOCs, PM2.5, and PM10 in a positive matrix factorization (PMF) model. Contrasts between clean monsoon air and polluted post-monsoon air, VOC source fingerprints, and molecular tracers enabled us to differentiate paddy residue burning from other biomass-burning sources, which had previously been impossible. Burning of fresh paddy residue, as well as residential heating and waste burning, contributed the most to observed PM10 levels (25 % and 23 %, respectively) and PM2.5 levels (23 % and 24 %, respectively), followed by heavy-duty vehicles fuelled by compressed natural gas (CNG), with a PM10 contribution of 15 % and a PM2.5 contribution of 11 %. For ambient VOCs, ozone formation potential, and secondary-organic-aerosol (SOA) formation potential, the top sources were petrol four-wheelers (20 %, 25 %, and 30 %, respectively), petrol two-wheelers (14 %, 12 %, and 20 %, respectively), industrial emissions (12 %, 14 %, and 15 %, respectively), solid-fuel-based cooking (10 %, 10 %, and 8 %, respectively), and road construction (8 %, 6 %, and 9 %, respectively). Emission inventories tended to overestimate residential biofuel emissions at least by a factor of 2 relative to the PMF output. The major source of PM pollution was regional biomass burning, while traffic and industries governed VOC emissions and secondary-pollutant formation. Our novel source apportionment method even quantitatively resolved similar biomass and fossil fuel sources, offering insights into both VOC and PM sources affecting extreme pollution events. This approach represents a notable advancement compared to current source apportionment approaches, and it could be of great relevance for future studies in other polluted cities and regions of the world with complex source mixtures.

Abstract Heat wave has become a great concern for India in the recent years due to its disastrous impact on various sectors including health. Thus, accurate forecasts of heat wave events well in advance are required for preparing adequate mitigation strategies. The present study assesses the prediction skill of numerical weather prediction models in The Observing system Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) experiments for predicting heat waves over India up to 7 days in advance. The models considered for this analysis are; the European Centre for Medium‐Range Weather Forecasts (ECMWF), the UK Met Office (UKMO) and National Centre for Environmental Prediction (NCEP). The model forecast verifications have been carried out for the hot weather season (April–June) over India for the period of 2008–2013. Fourteen heat wave events were identified during the study period using gridded daily maximum temperature (Tmax). The study reveals that the spatial distribution of maximum Temperature is well predicted by the TIGGE models for a forecast lead time of 1–7 days. The analysis suggested that the magnitude of heat wave events, even with a 7 days lead time, can be correctly predicted by more than 80% of ensemble members in all the TIGGE models. The prediction skill of the maximum temperatures over heat wave prone area during heat wave events is higher for ECMWF, then UKMO and NCEP models. The forecast verification analysis thus indicates that the TIGGE models are able to provide early warnings of heat waves with at least 5 days lead time.