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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. [ABSTRACT FROM AUTHOR]

A Winter Fog Experiment (WiFEX) was conducted to study the genesis of fog formation between winters 2016–17 and 2017–18 at Indira Gandhi International Airport (IGIA), Delhi, India. To support the WiFEX field campaign, the Weather Research and Forecasting (WRF) Model was used to produce real-time forecasts at 2-km horizontal grid spacing. This paper summarizes the performance of the model forecasts for 43 very dense fog episodes (visibility < 200 m) and preliminary evaluation of the model against the observations. Similarly, near-surface liquid water content (LWC) from models and continuous visibility observations are used as a metric for model evaluation. Results show that the skill score is relatively promising for the hit rate with a value of 0.78, whereas the false alarm rate (0.19) and missing rate (0.32) are quite low. This indicates that the model has reasonable predictive accuracy, and the performance of the real-time forecast is better for both dense fog events and no-fog events. For success cases, the model accurately captured the near-surface meteorological conditions, particularly the low-level moisture, wind fields, and temperature inversion. In contrast, for failed cases, the WRF Model shows large error in near-surface relative humidity and temperature compared to the observations, although it captures temperature inversions reasonably well. Our results also suggest that the model is able to capture the variability in fog onset for consecutive fog events. Errors in near-surface variables during failed cases are found to be affected by the errors in the initial conditions taken from the Indian Institute of Tropical Meteorology Global Forecasting System (IITM-GFS) spectral model forecast. Further evaluation of the operational forecasts for dense fog cases indicates that the error in predicting fog onset stage is relatively large (mean error of 4 h) compared to the dissipation stage. [ABSTRACT FROM AUTHOR]

The Indian subcontinent is a major hotspot of aerosol loading and several studies have demonstrated the importance of interaction between Asian dust aerosols and the Indian Summer Monsoon Rainfall (ISMR). However, a few studies have suggested a declining trend in the pre-monsoon dust loading over the Indian region. The present study investigates how the decrease in dust emissions influences the intensity of the monsoon rainfall over the Indian region using a fully coupled Weather Research and Forecasting model coupled with chemistry (WRF-Chem). We have conducted two sets of simulation experiments for the June-July-August-September (JJAS) season of 2017: (1) a control experiment with the default dust aerosol emissions, and (2) a sensitivity experiment by reducing dust emissions by 60%. Our analyses show that the reduction in dust emissions significantly alters the spatial distribution of seasonal mean monsoon rainfall with enhanced rainfall over the Bay of Bengal (BoB) and eastern coastal regions, and decreased rainfall over the lower Indo-Gangetic Plain (IGP), and parts of Central India (CI). It is found that the low aerosol loading conditions alter the large-scale circulation patterns along the tropical/sub-tropical region centred between 15°N and 35°N and 40°E −130°E, and through dynamical feedback, strengthens the convective rainfall over the BoB region. As a result, the 10–20 days sub-seasonal mode of the ISMR strengthens, facilitating enhanced westward propagation of rain-bearing systems formed over northern BoB which provides substantial rainfall over vast regions of the Indian subcontinent. • The impact of dust emissions on Indian Summer Monsoon Rainfall (ISMR) is examined. • Reduction in dust emissions significantly alters spatial rainfall distribution. • Strengthening of cyclonic circulation and increase in convective activity are noted over Bay of Bengal in reduced dust scenario. • Significant impact of reduced dust emissions is noted in the 10–20 days sub-seasonal variability of ISMR. • Westward propagating systems are strengthened in the reduced dust scenario. [ABSTRACT FROM AUTHOR]

The Indian subcontinent is a major hotspot of enhanced aerosol loading and emission of various chemical compounds due to extensive anthropogenic activities that profoundly impact the hydrological cycle of this region. Using coupled regional atmospheric chemistry transport model, WRF-Chem (Weather Research and Forecasting model coupled with chemistry) with fully interactive chemistry and dynamics, we carried out two sets of 122-days simulation experiments: (1) a control experiment without embedding chemistry and (2) a sensitivity experiment with embedding fully coupled MOZART-MOSAIC chemistry, to examine the impact of chemistry on the Indian Summer Monsoon Rainfall (ISMR) of 2017. The results show that inclusion of chemistry in the model plays a role in altering the spatial distribution of ISMR, reflected by a reduced mean rainfall by about 3 mm/day over the Indian subcontinent. The mean bias of model simulated rainfall reduces from 2.73 mm/day (excluding chemistry) to 1.6 mm/day (including chemistry), and from 1.2 mm/day (excluding chemistry) to 0.81 mm/day (including chemistry) when compared with IMD (India Meteorological Department) and TRMM (Tropical Rainfall Measuring Mission) rainfall data, respectively. Additionally, we found that inclusion of interactive chemistry causes a decrease in the daily convective rainfall by about 3 mm that is contributing largely to the mean rainfall differences. Further, analysis of hydrometeors indicates that the chemistry interacts with the cloud microphysics by modulating the hydrometeor paths of different cloud hydrometeors that consequently impacts the simulation of ISMR over this region. Results indicate that the virtue of detailed and more realistic representation of chemistry in the model has led to more realistic simulation and cannot be ignored in numerical forecasting of ISMR. • Chemistry-weather interaction on Indian Summer Monsoon Rainfall (ISMR) is examined. • Inclusion of chemistry plays a role in altering the spatial distribution of ISMR. • Mean rainfall decreases by 3 mm/day after inclusion of chemistry in the model. • Mean bias in rainfall improves in case of chemistry inclusive simulation. • Chemistry interacts with cloud microphysics by modulating cloud hydrometeor paths. [ABSTRACT FROM AUTHOR]

Air quality has become one of the most important environmental concerns for Delhi, India. In this perspective, we have developed a high-resolution air quality prediction system for Delhi based on chemical data assimilation in the chemical transport model -- Weather Research and Forecasting with Chemistry (WRF-Chem). The data assimilation system was applied to improve the PM2.5 forecast via assimilation of MODIS aerosol optical depth retrievals using threedimensional variational data analysis scheme. Near real-time MODIS fire count data were applied simultaneously to adjust the fire-emission inputs of chemical species before the assimilation cycle. Carbon monoxide (CO) emissions from biomass burning, anthropogenic emissions, and CO inflow from the domain boundaries were tagged to understand the contribution of local and non-local emission sources. We achieved significant improvements for surface PM2.5 forecast with joint adjustment of initial conditions and fire emissions. [ABSTRACT FROM AUTHOR]

This study presents the successful implementation of an operational Air Quality Early Warning System (AQEWS) in forecasting an unprecedented dust storm event that occurred in the western part of India from 21–24 January 2022. The AQEWS generates a daily 72-h forecast using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at the core, which is initialized with assimilated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals and in-situ measurements of fine particulate matter (PM 2.5). The AQEWS was able to forecast the outbreak of this dust storm reasonably well over the north of the Arabian Sea on 21 January, as well as its movement and dispersal towards the Indian landmass in the subsequent 72 h, resulting in a significant amount of transported dust that engulfed the west Indian region on 23–24 January. We utilize satellite observations and in situ measurements to quantify the ability of the forecasting system to predict the spatiotemporal characteristics of the dust storm. Notably, the AQEWS aerosol data assimilation process played a critical role in forecasting the anomalous increase of PM 10 in several west Indian cities, as it significantly improved the initial conditions of PM 10 by approximately 24–40 μg/m3 and PM 2.5 concentration by approximately 30–38 μg/m3. Without assimilation, the system fails to capture the intensity, and pattern of variability of the dust storm. The mean bias of the 72-h prediction of PM 10 was substantially reduced with assimilation, from −44% to 0.35% in Mumbai and from −57% to 13% in Gujarat. In selected regions, in situ measurements confirmed that the AQEWS adequately captured the peak intensity and pattern of the dust storm event. The AQEWS thus demonstrated improved forecasting skills in predicting an extreme dust event during winter and the adoption of chemical data assimilation enhanced the accuracy of the real-time air quality forecasting system. • The Air Quality Early Warning System (AQEWS) efficiently forecasted an unusual dust storm in January 2022 over Western India. • The incorporation of chemical data assimilation substantially improved the accuracy of the forecasts. [ABSTRACT FROM AUTHOR]