The present study analyses the performance of the Weather Research and Forecasting (WRF) model in simulating rainfall over Pune city during four recent heavy rainfall events. An attempt has been made to enhance the forecasting skill of the model at an urban scale by employing a comprehensive urban land use/land cover (LULC) map, by utilizing a high-resolution local climate zones (LCZ) classification over Pune, rather than the default LULC. Results revealed that incorporating LCZ information and multilayer urban canopy model (BEP) into the WRF model improved the spatial variability of WRF-simulated meteorological conditions and rainfall. The heterogeneity in the morphological characteristics of the city, as provided through the LCZ, played a notable role in altering the near-surface heat and moisture flux. The model simulated a decrease in 2 m temperature, 10 m wind speed, and sensible heat flux; while the latent heat flux and the planetary boundary layer height increased when LCZ maps were incorporated. It also altered the vertical temperature, moisture, and wind profiles. The observed changes in surface energy fluxes and vertical instability were favourable to heavy rainfall and, therefore, aligned closer to the observed pattern. Statistical analysis also demonstrated an improved forecast skill of the model when LCZ were incorporated. This study emphasizes the importance of realistic land-use representation in urban rainfall forecasts. The representation of heterogeneous urban surface improved the rainfall prediction skill of WRF model. This work highlights the importance of urban-scale LULC representations in the WRF to enhance the model's predictive capabilities of heavy rainfall over urban cities.

Mumbai experiences heavy rainfall events that significantly disrupt daily life and infrastructure every year, yet operational forecasts still struggle with accuracy and forecasts. This study explores the skill efficacy in temporal and spatiotemporal supervised machine learning (ML) applications for the prognoses of daily rainfall in the Mumbai Metropolitan region. Based on ERA5-reanalyzed and observed datasets, the learning and predictions associate the daily-averaged atmospheric predictors with observed rainfall accumulations for the 1969-2019 period. The temporal learning showed that deep-learning (DL) long-and-short-memory (LSTM) algorithm exhibits consistent skill merits of training and predictions irrespective of the choice of observed and ERA5-reanalyzed predictors. Following this premise, spatiotemporal learning is conducted using a suite of multi-layered unidirectional and bidirectional convolutional LSTM frameworks with additional physics-aware predictors from ERA5, such as column-integrated moisture and layer stability parameters, targeted to observed rainfall during the monsoon season. The 2-layer bidirectional configuration shows optimal performance and reproduces observed moderate-to-heavy rain amounts with 65% skill. Although extreme rain situations (> 100 mm) are temporally captured, a higher skill is generally noted in reproducing these situations embedded within observed incessant rainy spells exhibiting gradual daily variations compared to the ones associated with rapid variations. The study shows the promising potential of DL implementations to support the operational forecast guidance in heavy rainfall alerts for the Mumbai region.

Abstract Northwest Indian cities (Delhi, Jaipur, and Agra) are highly prone to heatwaves (HWs) during April to June. We address the analysis of a prolonged HW event between May 24 and May 28, 2020, using in‐situ, reanalysis, and 1.5‐km resolution Weather Research and Forecasting (WRF) model products. Both observations and reanalysis revealed that warm (˜40–44°C) and dry northwesterly winds (˜10 m·s −1 ) prevailed over northwest and central India during the HW period. The WRF model simulated the evolution of surface air temperature, relative humidity, winds, and HW duration and its spatial extent fairly well. The model could depict the nighttime intensification of HWs (˜3–7°C) in urban areas and a pronounced heat dome with vertical extension of up to 2 km. The findings reveal that weaker winds, the overlong presence of the heat dome, and a nighttime urban heat island (UHI) over urban centers exacerbate the extreme heat conditions in the cities. The altered physical processes over urban areas enhances the UHI during the night and early morning hours, thereby amplifying and prolonging the persistence of heat domes, eventually contributing to the duration and intensity of HWs. Computed statistical metrics confirm that the WRF model performed satisfactorily in predicting HW over northwestern India. Furthermore, a sensitivity test revealed a reduction in UHI intensity (about 3–5°C) when urban built‐up classes were replaced by rural classes. This research outcome is valuable to urban planners and policymakers for addressing challenges related to urbanization and increasing heat stress.

The East coast of India, including Bhubaneswar and Cuttack in Odisha, often faces heavy rainfall events (HREs), leading to floods and significant loss of life and property. The present study evaluates the performance of a previously customized WRF model, forced by NCEP-GFS, for its capabilities in HRE forecasting and compares it with the India Meteorological Department's Global Forecast System (IMD-GFS) model in predicting HREs in quasi-operational mode. Their performance is assessed against observed daily rainfall station data, considering 23 HREs that occurred during the 2022 monsoon season. Our findings indicate that the optimum WRF configuration successfully captures both the occurrence of HREs and their magnitudes. Results show that the optimized WRF model effectively captures both the occurrence and intensity of HREs, achieving an overall success rate of 64

Air quality in India faces significant risk from agricultural residue burning, especially in Punjab and Haryana, which are pivotal to the world's second-largest agrarian economy. This study quantifies emissions from post-monsoon biomass burning (10 October-30 November 2022) in these states using VIIRS fire detection data and Sentinel-2-derived burnt areas. Ground validation via district-level surveys aligns with the findings of our study. Results show 51% of the total crop area was burned (14,700 km2 in Punjab; 8,300 km2 in Haryana), leading to substantial emissions of PM2.5 (54.28 Gg; 7.94 Gg), CH4 (25.63 Gg; 3.75 Gg), CO2 (1,100.3 Gg; 195.7 Gg), NH3 (0.83 Gg; 0.15 Gg), SO2 (0.68 Gg; 0.12 Gg), and CO (62.1 Gg; 11.04 Gg). Emissions in Punjab are about 6.5 times higher than in Haryana attributable to greater burnt area (similar to 14,700 km2), higher crop yield, and elevated residue-to-crop ratios. Compared to VIIRS, Sentinel-2 data provides approximately 3.6 times higher emission estimates, reflecting improved burnt area detection. District-level emission variations underscore the influence of diverse farming practices, weather, and residue management. An uncertainty analysis, derived from multiple emissions estimates and methodologies, highlights regional disparities: SO2 exhibits the highest uncertainty in both states with PM2.5 and CO, respectively, showing the least. Understanding these emissions and uncertainties is vital for forecasting air pollution in downwind cities such as New Delhi and for formulating targeted mitigation strategies.

Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities.

This study attempts to investigate whether the mangroves of Maharashtra, India, are acting as a source or sink of carbon. Additionally, efforts were made to develop the empirical model using ground-based tree biomass and satellite-based indices to estimate above-ground biomass (AGB) for the state of Maharashtra for the year 2018–19. Total of 44 geotagged, well-distributed sample plots (0.1 ha each) were laid down to measure the tree girth and height (of all the trees) of different mangrove species. The available biomass equations were used to estimate the AGB at the plot level. Normalized Difference Vegetation Index (NDVI) and plot-wise AGB correlation were tested for minimum, maximum, sum and amplitude of stacked monthly NDVI. The strongest correlation of AGB was observed with maximum NDVI and was therefore used in regression analysis to estimate the AGB and carbon present in mangroves. Carbon values ranged from 2.78 to 249.64 t C ha −1 . Carbon sequestration was estimated using statistical method by estimating the difference in total carbon content within the mangroves between the years 2005–06 and 2018–19. Total carbon content was determined by multiplying per hectare AGB with the mangrove area for the respective years. In 2005–06, the carbon pool in the state amounted to 1.08 × 10 6 tonnes, which increased to 1.32 × 10 6 tonnes by 2018–19. The mangroves in Maharashtra sequestered a total of 0.24 × 10 6 tonnes of carbon from 2005–06 to 2018–19, confirming their role as a carbon sink.

In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km2) was validated against the satellite-based burnt area (1.07 km2) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting.

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.

Forests are vital for life on Earth but are threatened by forest fires, which have significant impacts on climate change both locally and globally. This study examines a forest fire that lasted from 15 to 26 February 2019 in Karnataka, India, using the Weather Research and Forecasting model with Chemistry (WRF-Chem) model to analyze the effects and atmospheric spread of fire-emitted aerosols. Model simulations are analyzed to understand the horizontal and vertical transport and radiative effects of the fire. The results show high aerosol levels and smoke particles reaching up to 3.5 km altitude and above. The fire raised near-surface air temperatures by ~1–1.5 °C. The net atmospheric forcing due to the fire over the affected area ranged from approximately 10 to 14 W/m2, resulting in heating rates between about 0.002 and 0.005 K/day in the impacted region.

The Urban Heat Island (UHI) phenomenon significantly affects us by exacerbating heat discomfort, increased energy consumption, and urban air pollution. The severity of UHI associated with heat waves and heat stress-related mortality is now one of the major concerns, particularly in densely populated cities. Therefore, the significance of the UHI has emerged as a crucial issue due to cities’ fast growth and urban development, necessitating a comprehensive review of the present status of UHI research, particularly concerning regional comparisons. This paper delineates the characteristics of UHI, focusing on its intensity, impact, determining factors, and potential mitigation strategy. It synthesizes an insight into important aspects of UHI from a comprehensive analysis of over 400 national/international research articles. The findings indicate a lack of UHI research studies in the central and eastern regions of the country, bringing out the need for further investigation in these areas. The observed UHI intensity varies across the country between 2 and 10 °C, with the northwest seeing a more pronounced temperature gradient. Following a detailed review, a few suggestions for future research to minimize the impact of UHI on public health, energy consumption, and the economy are proposed, as are strategies for mitigation. While studies on UHIs in India have primarily relied on observational data, there is still a substantial need for more research on employing numerical model-based and machine-learning approaches. Furthermore, the availability of mitigation research on Indian cities is limited. Additional research is needed to ascertain the intricate mechanisms behind the UHI effect on cities vulnerable to various climatic risks and hazards.

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM2.5 predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM2.5 which shows the lowest errors and best agreement with the observed data. The best results were achieved with initial/boundary conditions from ERA and GDAS datasets and local PBL parameterization (MYJ and MYNN). It was also found that PM2.5 concentrations are relatively less sensitive to changes in initial/boundary conditions but in contrast show a stronger sensitivity to changes in the PBL scheme. Moreover, the sensitivity of the simulated PM2.5 to the choice of PBL scheme is more during the polluted hours of the day (evening to early morning), while that to the choice of the meteorological input data is more uniform and subdued over the day. This work indicates the optimal model setup in terms of choice of initial/boundary conditions datasets and PBL parameterization schemes for future air quality simulations. It also highlights the importance of the choice of PBL scheme over the choice of meteorological data set to the simulated PM2.5 by a chemical transport model.

Meteorology and Hydrological extreme events, such as heavy rainfall and associated Flooding is one of the increasing disasters in India for last two decades. Due to heavy reservoir discharge, Impact of rapid Urbanization, unauthorized encroachments across riverbanks extreme flood events are likely to be more common and severe in the future, potentially impacting millions of people.Pune one the fastest growing megacities in India facing frequent riverine flooding and associated disaster causing huge property losses in millions and causalities. The city is located at the leeward side of Sahyadri mountain range, with 7 reservoirs on the upstream side of the catchment, which control the flows in the rivers impacting the downstream Urban catchment. The reservoirs spillway discharges causes riverine flooding along with contribution from free catchment runoff, which usually occur concurrently. Estimation of reservoir inflows and subsequent spillway discharges is needed for integrated reservoir operations to execute effective flood control measures. To understand these severe flood disasters associated with reservoir operation ensemble multi model simulations were carried for Pune catchment for flood mitigation.In current study, coupled meteorology model WRF with integrated high resolution (10m) hydrology model HEC-HMS and Hydraulic Model HEC-RAS was developed. High resolution CartoSAT, Digital Elevation Model (DEM) and generated 1m DTM was used to develop both hydraulic and hydrology models. The geometric data for dam structures and gates/spillways have been incorporated in developed models. Gates were operated based on reservoir rule curves for spillway discharge and riverine flood simulations. Spatially distributed high-resolution WRF (1.5 Km) forecasted (72 Hrs.) gridded rainfall data with temporal resolution of 15 mins has been used for forecasting the flood condition in the city. 3D buildings have been incorporated in the terrain to recognize water depth and flooding in the city, which can be visualized through 2-dimensional Rasmapper and 3-dimensional viewer. The performance of the models has been validated on the basis of statistical error functions (NSE, RSR, PBIAS and R2). Pune flood disaster events for the year 2019 and 2022 were simulated by developed flood forecasting system with reservoir operations. The model output (water level, spread and discharge) were validated using observed flood data from Pune Municipal corporation and dam discharges from water Resource department.The developed multi-model flood forecasting framework will help the reservoir authorities to perform reservoir operations effectively in future to minimize the downstream flood conditions. Also the disaster management authorities will plan flood mitigation plans with sufficient lead time.

This study evaluates the impact of the Planetary Boundary layer (PBL) on rainfall simulated by the Weather Research and Forecasting (WRF) model during extreme rainfall event cases over an urban city. This is accomplished by producing a heavy rainfall event forecast using the WRF model with five varying PBL scenarios (ACM2, YSU, MYJ, QNSE, MYNN) but otherwise identical model configuration. The study examined the role of PBL in modulating rainfall characteristics by investigating the impacts of PBL on forecast skills with particular reference to rainfall. The PBL scheme plays a crucial role and can significantly alter the spatial pattern and magnitudes of rainfall simulated by the model. Results showed that the QNSE boundary layer scheme substantially improved the magnitudes and spatial pattern of rainfall simulated by the model. The model’s ability was cross-checked with this setup to capture the other two heavy rainfall events over Pune city and was found satisfactory. The current model setup could capture the spatial distribution and the magnitude of rainfall compared with satellite data and observation over point locations. These findings are highly relevant to highlight the dependency of the model on parameterization schemes to forecast extreme rainfall events accurately.

Stubble-burning in northern India is an important source of atmospheric particulate matter (PM) and trace gases, which significantly impact local and regional climate, in addition to causing severe health risks. Scientific research on assessing the impact of these burnings on the air quality over Delhi is still relatively sparse. The present study analyzes the satellite-retrieved stubble-burning activities in the year 2021, using the MODIS active fire count data for Punjab and Haryana, and assesses the contribution of CO and PM2.5 from such biomass-burning activities to the pollution load in Delhi. The analysis suggests that the satellite-retrieved fire counts in Punjab and Haryana were the highest among the last five years (2016-2021). Further, we note that the stubble-burning fires in the year 2021 are delayed by ∼1 week compared to that in the year 2016. To quantify the contribution of the fires to the air pollution in Delhi, we use tagged tracers for CO and PM2.5 emissions from fire emissions in the regional air quality forecasting system. The modeling framework suggests a maximum daily mean contribution of the stubble-burning fires to the air pollution in Delhi in the months of October-November 2021 to be around 30-35%. We find that the contribution from stubble burning activities to the air quality in Delhi is maximum (minimum) during the turbulent hours of late morning to afternoon (calmer hours of evening to early morning). The quantification of this contribution is critical from the crop-residue and air-quality management perspective for policymakers in the source and the receptors regions, respectively.

This paper discusses the comparative results of surface and satellite measurements made during the Phase1 (25 March to 14 April), Phase2 (15 April to 3 May) and Phase3 (3 May to 17May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase1, Phase2 and Phase3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by -8.29% during 2nd Phase of lockdown period.

Forests, though very critical for life on Earth, are threatened by various factors and the frequently occurring forest fires are one of the significant causes. Forest fires drastically contribute to climate change on both regional and global scales. Forest fires—of both natural and anthropogenic origins—induce aerosols in the atmosphere and have a significant impact on the health and climate of the region. In this study, we simulate the Uttarakhand (29–31° N, 78–80° E) fire event in India, which occurred in April 2016, using the Weather Research and Forecasting with Chemistry (WRF-Chem) model to estimate the radiative impact of the aerosols emitted due to this fire event and probe into the extent of their transport into the atmosphere. Multiple data from ground-based and satellite observations are used to access the model performance. Our analysis showed that the high values of aerosol optical depths (AODs) during the fire event simulated by WRF-Chem compared very well with MODIS AODs over the Uttarakhand region. The model simulations of the vertical profile of BC corroborate with elevated smoke aerosols derived from CALIPSO. An enhancement of smoke aerosols is observed up to 5-km altitude during the fire event both in the model simulations and observations. The fire has increased the near-surface air temperatures by 1–3 °C and decreased the relative humidity by 10