This study investigates the synergistic impacts of conventional and non‐conventional atmospheric data assimilation (DA) and fine‐gridded soil state assimilation on wintertime fog formation over the Indo‐Gangetic Plain (IGP), with a specific focus on Delhi. Two DA experiments were conducted using the Weather Research and Forecasting (WRF) model: the first (DA1) assimilated temperature and humidity profiles from a microwave radiometer (MWR) using 3DVar/GSI‐based system, while second (DA2) extended DA1 by incorporating fine‐gridded initial soil fields from the High‐Resolution Land Data Assimilation System (HRLDAS). The effectiveness of these data sets in improving the forecast accuracy of wintertime meteorological parameters within the boundary layer was evaluated. MWR profiles were validated against simultaneous radiosonde (RS) measurements during the winter seasons of 2017–2019, and bias correction using RS data was implemented to enhance MWR profile accuracy. The results indicated that the assimilation of MWR profiles (DA1) improves the accuracy of near surface temperature and humidity forecasts, conducive for fog conditions. The inclusion of soil state assimilation (DA2) further improves the representation of soil states, thereby better capturing the physical processes associated with fog formation. With DA2, biases in near‐surface meteorological and soil variables were significantly reduced (50% in T 2 , 16% in RH 2 , 66% in SM, 46% in ST) compared to DA1. DA2 also improved the representation of surface fog heterogeneity and lifecycle across the IGP, with a spatial skill score of 0.36, versus 0.29 for DA1 and 0.24 without assimilation. Additionally, DA2 achieved a higher critical success index (CSI) of 0.75, compared to 0.50 for DA1.

The presence of persistent heavy fog in northern India during winter creates hazardous situations for transportation systems and disrupts the lives of about 400 million people. The meteorological factors responsible for its genesis and predictability are not yet completely understood in this region. Given its high potential for socioeconomic impact, there is a pressing need for extensive research that understands the inherently complex nature of the phenomena through field observations and modeling exercises. WiFEX is a first-of-its-kind multi-institutional initiative dealing with intensive ground-based measurement campaigns for developing a suitable fog forecasting capability under the aegis of the smart cities mission of India. Measuring campaigns were conducted during the 2015-20 winters at the Indira Gandhi International Airport, New Delhi, covering more than 90 dense fog events. The field experiments involved extensive suites of in situ instruments and gathered simultaneous observations of micrometeorological conditions, radiative fluxes, turbulence, droplet/aerosol microphysics, aerosol optical properties, fog water chemistry, and vertical thermodynamical structure to describe the environmental stability in which fog develops. An operational modeling framework, the WRF Model, was set up to provide fog predictions during the measurement campaign. These field observations helped to interpret the strengths and deficiencies in the numerical modeling framework. Four scientific objectives were pursued: (i) the life cycle of optically thin and thick fog, (ii) microphysical properties in the polluted boundary layer, (iii) fog water chemistry, gas-aerosol partitioning during the fog life cycle, and (iv) numerical prediction of fog. This paper presents an overview of WiFEX and a synthesis of selected observational and modeling analyses/findings related to the abovementioned scientific topics.

The present study highlights the role of high-resolution land data assimilation in improving the prediction of the radiation fog and near-surface meteorological variables. The performance of the Weather Research and Forecasting (WRF) model coupled with the High-Resolution Land Data Assimilation System (HRLDAS) is evaluated for a dense fog event that occurred on 24-25 January 2018 using detailed observations from the Winter Fog EXperiment (WiFEX) over the Delhi (India) region. The Noah-MP Land-Surface Model (LSM) based HRLDAS framework was executed in uncoupled mode to develop fine-grid soil states for soil moisture (SM) and soil temperature (ST) covering the Indo Gangetic Plain (IGP) region at 2 km horizontal grid resolution. The quality of soil states (SM/ST) from the HRLDAS and reanalysis (CNTL) dataset was verified with observed SM/ST at the Indira Gandhi International (IGI) airport, New Delhi, during 2017-18 winter months (December-January). It was found that the soil state in the CNTL dataset is moist, despite the actual soil condition being dryer at the observation site. HRLDAS simulated SM reasonably agrees with observations at IGI by reducing wet mean bias by about 56%. Subsequently, four sensitive experiments were carried out using Noah-MP (NM) and Pleim-Xiu (PX) land-surface parameterisations in the WRF model initialised with CNTL and HRLDAS soil states. We found that the bias in micro-meteorological variables (T2, RH2, WS10) and Turbulent Kinetic Energy (TKE) during the fog event was significantly improved with Pleim-Xiu (PX) land-surface parameterisations and HRLDAS soil states (HRLDAS_PX). Statistical performance of the micro-meteorological variables (T2, RH2 and WS10) exhibited low variance (0.13 degrees C, 0.10% and 0.75 m s-1), high correlation (0.93, 0.92 and 0.82) and high index of agreement (0.92, 0.87 and 0.78). As a result, the error in fog onset timing was notably reduced to 02 h, and the vertical representation of fog was skillfully demonstrated in the HRLDAS_PX simulation. The sensitivity experiments revealed that there is a need to revisit soil states to improve the skill of the fog forecast.

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.

The Indian Institute of Tropical Meteorology (IITM), in partnership with the National Center for Atmospheric Research (NCAR), has developed a high resolution (400 m) air-quality Early Warning System (EWS) for Delhi using an advanced approach of assimilating aerosol optical depth, fire emissions from a space-borne platform, and real-time aerosol observations from in-situ network in WRF-Chem model to produce a 72-h forecast. The present study summarizes the performance of EWS forecast and prevailing meteorological conditions for winter months of 2020-2021. We examined the performance of model-simulated meteorology by comparing against the in-situ observations. The model shows a positive bias in downwelling shortwave radiation (approximate to 34 Wm(-2)) and warm bias in near-surface temperature (approximate to 3 K). The simulated winds while having realistic magnitudes depict more southwesterly behavior compared to the observations. The model struggles to accurately predict Planetary Boundary Layer Height. The model realistically simulates the relationship between wind and air quality parameters. The air quality forecast from the model is found to be skillful in three different AQI categories, with accuracy > 88% for critical category events. The model also exhibits good statistical performance in predicting AQI, with low mean bias (< 8%), high correlation (> 0.68), and low variance (NMSE < 0.09).

Three major sequential widespread dust events were experienced in the northern parts of India in May 2018. A significant impact of these pre-monsoon dust storms on the aerosol characteristics over the Indian National capital region (NCR) has been studied using remotely sensed ceilometer and ground-based measurements at Indira Gandhi International airport, New Delhi, India. The results show that after each dust activity, the significant inclusion of dust aerosols loaded in the free troposphere. Consequently, the direct impact on the lower atmospheric parameters like increase in daily average temperature (by 4–5 K), stepped up (stepped down) diurnal cycles of longwave fluxes (shortwave fluxes), has been recorded within 15 days of dust span. Mainly, the adverse meteorological and radiation features noticed before the first dust storm (DS1), which pinpoints the sudden dust intrusion over NCR, Delhi. However, this dust storm has extensively impacted on the atmospheric vertical dust loading, surface boundary layer mechanisms, and socioeconomic way. Therefore, the detailed analysis of vertical dust distribution and its interaction with mid-tropospheric processes has been carried by using the vertical normalized attenuated backscatter coefficients accompanying the radiosonde observation. The aloft floating dust layer up to 3–4 km has been noticed even after shallow rainfall and persisted at almost the same height for the next 34 h due to low-level clouds. Meanwhile, the sub-dust layer below 1 km is formed due to local activity, which also sustains for a long time.

The size-resolved compositional analysis of non-refractory submicron aerosol (NR-PM1) was conducted using the Aerodyne High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) instrument over Pune, India during the COVID-19 lockdown period. The aerosol composition data shows the predominant presence of organics (Org) in the mass fraction followed by sulfate, ammonium, nitrate, and chloride during the pre-lockdown and lockdown periods. The size-resolved analysis showed the unimodal size distribution of organic and inorganic constituents with peaks at 550 nm, implying the dominant presence of mixed and aged aerosol species. The stoichiometric neutralization analysis showed the almost neutralized nature of submicron aerosol with an average aerosol neutralization ratio (ANR) of 0.8. The back trajectories, cluster analysis, and potential source contribution function (PSCF) showed the industrial belt located in the western part of the study location to be the potential source regions of NR-PM1. Positive matrix factorization (PMF) analyses have been applied to investigate the source apportionments of organic aerosols (OA). Four distinct OA factors, i.e., hydrocarbon-like OA (HOA), biomass burning OA (BBOA), low-volatile oxygenated OA (LVOOA), and semi-volatile oxygenated OA (SVOOA) were identified during the study period. Among these factors, HOA contributes nearly a quarter to the OA mass, and OOA accounted for nearly 60% of the total OA mass. The high-resolution positive matrix factorization (HR-PMF) analysis and the elemental ratios of H/C, O/C, and OM/OC showed distinct characteristics during different periods. The density of organic aerosol has been estimated using the elemental ratios and found to be 1.14, 1.28, and 1.35 respectively during the different lockdown periods, similar to 1.30 g cm-3 as mentioned in the literature. This study provides new insights into the chemical composition and source apportionment of the organic fraction of submicron aerosols for the first time over Pune using HR-ToF-AMS and HR-PMF.

The campaign mode observational program 'Winter Fog Experiment' (WiFEX) was set up at the Indira Gandhi International Airport (IGIA), New Delhi, during the winter months of 2016–17 and 2017–18. Using the WiFEX data, in this study, we examine the microphysical structure of fog formed in a polluted environment and attempt to predict visibility ( V is ) using the fog index approach. The examination of eleven fog events demonstrates that the mean droplet concentration (up to 674.94 #/cm −3 ) and liquid water content (LWC, up to 0.29 g m −3 ) are high in dense fog cases ( V is < 200 m). The droplet spectrum shows bi-modal distribution and dominance of smaller droplets in the 3–7 µm range. For most fog cases, the droplet spectrum extends up to 50 µm. The mature phase of the fog depicts a relatively increased population of droplets in the higher-sized bins, highlighting the formation of larger droplets. Moreover, we found that V is is inversely related to the liquid water content and the fog droplet number concentration. Fog index-based visibility parameterization has been developed to diagnostically compute visibility for the different categories of fog events, namely category-IIIB (CAT-IIIB) and category-IIIC (CAT-IIIC), using the meteorological variables. Out of 14 CAT-IIIB and 19 CAT-IIIC fog events, the 'WiFEX-in' could predict seven CAT-IIIB and 12 CAT-IIIC fog events, respectively. However, significant under-prediction was evident for the total CAT-IIIB fog hours and over-prediction for the total CAT-IIIC fog hours. It is found that the observed and predicted fog hour differences were related to the errors in the fog onset, dissipation, and magnitude of predicted liquid water content during CAT-IIIB and CAT-IIIC events and the same are discussed.

Widespread catastrophic fog episodes in polluted northern India have been attributed to tropical cyclone activity in the Bay of Bengal & Arabian Sea; however, limited studies have been conducted on the effect of tropical cyclone intensity ('T' Numbers) on different fog characteristics in Indo Gangetic Basin, Northern India. In this study, different characteristics, including persistence, intensity, and areal extension, were analyzed at the Indira Gandhi International Airport, New Delhi during 1998–99, 2013–14, and 2016–17. A high-intensity tropical cyclone (Severe to Very Severe Cyclonic Storm) has been found to significantly increase the persistence, intensity, and areal extension of fog by inducing strong subsidence over the IGI Airport/Indo-Gangetic Basin. This knowledge is vital for improving the short-term forecasting of fog in the Indo-Gangetic Basin of Northern India and will further support the Government agencies to take preventive safety measures and planning well in advance time.

The occurrence of thick fog for longer duration in the northern regions of India disturbs the aviation, road transportation and other day to day activities. To understand the turbulence properties during fog period, we measured the atmospheric turbulent parameters along with carbon dioxide concentrations in the atmospheric boundary layer using eddy covariance system. These measurements were conducted over the agricultural station, Hisar, India, during the months of January–February of the year 2017 and 2018. During this period, total five thick fog events and three moderate fog events were captured. The turbulent parameter such as friction velocity, stability, sensible and latent heat fluxes are presented with respect to fog events. During the study period, the western disturbance persists over the north Pakistan and neighborhood region which advects the large amount of moisture into the lower troposphere and further through evaporation. It enforces stable and clear sky atmospheric conditions and reduces the surface temperature leading to the formation of strong surface-based temperature inversion which facilitates the fog formation in the study region. The land surface processes with neutral stability conditions in the surface layer, play significant role to sustain fog in the study region. The observations show substantial increase of carbon dioxide concentration during the thick fog events. The foggy days did not depict the diurnal pattern in flux of CO2. The anomalies of the meteorological parameters during foggy days and clear sky are analyzed. The foggy conditions (04:00–10:00 h, IST) are found to be characterized with low wind speed, high relative humidity with remarkable fluctuations in dew point temperature. Also, the sensible and latent heat flux shows remarkable changes during foggy and clear sky conditions.

In this study, we used remotely sensed backscattered profiles from a ceilometer to characterize the vertical and horizontal mixing of aerosols in the polluted planetary boundary layer (PBL). These profiles revealed the structure of the boundary layer, which included the mixed layer, the nocturnal residual layer and the elevated aerosol layer far above the mixed layer over Delhi. The accumulation of aerosols near the surface during feeble turbulence and the mixing of aerosols from the residual layer into the surface layer during convection was captured very well by a ceilometer. The backscattered signal from a height of 45 m above the ground was strongly correlated (82

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.

3 Avinash N Parde1, Sachin D. Ghude2*, Prakash Pithani2, Narendra G Dhangar2, Sandip 4 Nivdange3, Gopal Krishna2, D.M. Lal2, R. Jenamani4, Pankaj Singh5, Chinmay Jena2, 5 Ramakrishna Karumuri2, P.D. Safai2, D.M. Chate2 6 7 1Dept of Atmospheric and Space Sciences, Savitribai Phule Pune University 8 2 Indian Institute of Tropical Meteorology, Pune 9 3 Dept of Environmental Science, Savitribai Phule Pune University 10 4 India Meteorological Department, New Delhi 11 5 Dept of Physics, Deshbandhu College, University of Delhi, Kalkaji, New Delhi 12 13 Abstract 14 15 In this study, we used remotely sensed backscattered profiles from the ceilometer to 16 understand the vertical and horizontal mixing of aerosols in the polluted planetary boundary 17 layer (PBL). Backscattered profiles from the ceilometer were able to reveal information on 18 the boundary layer structure including mixed layer, nocturnal residual layer and aerosol 19 elevated layer far above the mixed layer over Delhi. Backscattered profiles also showed the 20 accumulation of aerosols near the surface under the feeble turbulent conditions and mixing of 21 aerosols from the residual layer to the surface layer under the convective conditions. We 22 found that the ceilometer backscattered signal from a height of 45 m above the ground was 23 strongly correlated (82%) with the surface PM2.5 and PM10 mass concentration. We developed 24 an empirical model based on regression between the ceilometer backscattered signal and 25 surface PM2.5 and PM10 observations in Delhi. The regression model was then tested and 26 validated against the independent measurement of surface PM2.5 and PM10 observations 27 during winter 2019. Local meteorological conditions, particularly cloudy and rainy conditions, 28 were found to influence the quality of the relationship between the PM2.5 and PM10 mass 29 concentration and backscattered signal. The performance statistics indicated that the 30 magnitude of mean bias between observed and estimated PM2.5 (-21 μg m-3, RMSE = 75) and 31 PM10 (31 μg m-3, RMSE = 118) was significantly close to the observations. On clear days, 32 estimated PM2.5 and PM10 mass concentration using the empirical model was overestimated 33 by 7% and underestimated by 6%, respectively. 34 35

Drought has negative impact on agriculture, ecosystem and livelihood. Several indices are developed to monitor and measure the intensity of drought. Standardized Precipitation Evapotranspiration Index (SPEI) is a new drought index that considers the rainfall and evapotranspiration to monitor drought. The data for last 34 years (1980-2014) were utilized in the study to study the drought in India. The gridded precipitation and temperature data from the India Meteorological Department (IMD) were considered for the generation of SPEI for mapping of drought The SPEI were calculated for normal (2013) and (1985, 1987, 2002, 2004, 2009 and 2014) drought year. The SPEI were classified into nine classes for extremely wet to extremely dry conditions. It is evident that SPEI is in acceptance with Standardized Precipitation Index (SPI) and can identify drought affected areas. The correlation coefficient (r) between SPI and SPEI ranged from 0.63-0.79 for the study years. The spatial comparison between SPEI and Vegetation Health Index (VHI) describes the ability of SPEI for drought monitoring. The finding shows that SPEI can be used as an advanced index for drought monitoring and assessment in India.

Under the present study estimation of high resolution soil moisture (SM) under Pan India mode using simple water balance method and from satellite data has been explored. It aims at the simple calculation of soil moisture followed by verification with ground truth data of SM on spatial and temporal scale (WC) as climatic input. The model has been verified for winter (January-February), pre-monsoon (March-May), monsoon (June-September) and post monsoon (October-December) seasons of year 2013. The comparison of model estimates with the in-situ data from 17 ground stations (for 396 paired datasets) over different seasons produced a better correlation coefficient varying from 0.46 to 0.60. The spatial comparison of SM estimated from model and satellite SM for the monsoon season shows a greater degree of coherence over most parts of India. Model derived weekly gridded SM combined with higher resolution satellite SM could use simple formulation and minimum inputs in conjunction with geographic information system (GIS). The SM is calculated on weekly basis and using gridded rainfall, potential evapotranspiration (PET) and field capacity (FC) and wilting point be used for better accuracy of the proposed block level agrometadvisory services.

Single Doppler analysis techniques known as velocity azimuth display (VAD) and volume velocity processing (VVP) are used to analyze kinematics of mesoscale flow such as horizontal wind and divergence using X-band Doppler weather radar observations, for selected cases of convective, stratiform, and shallow cloud systems near tropical Indian sites Pune (18.58 degrees N, 73.92 degrees E, above sea level (asl) 560 m) and Mandhardev (18.51 degrees N, 73.85 degrees E, asl 1297 m). The vertical profiles of horizontal wind estimated from radar VVP/VAD methods agree well with GPS radiosonde profiles, with the low-level jet at about 1.5 km during monsoon season well depicted in both. The vertical structure and temporal variability of divergence and reflectivity profiles are indicative of the dynamical and microphysical characteristics of shallow convective, deep convective, and stratiform cloud systems. In shallow convective systems, vertical development of reflectivity profiles is limited below 5 km. In deep convective systems, reflectivity values as large as 55 dBZ were observed above freezing level. The stratiform system shows the presence of a reflectivity bright band (similar to 35 dBZ) near the melting level. The diagnosed vertical profiles of divergence in convective and stratiform systems are distinct. In shallow convective conditions, convergence was seen below 4 km with divergence above. Low-level convergence and upper level divergence are observed in deep convective profiles, while stratiform precipitation has midlevel convergence present between lower level and upper level divergence. The divergence profiles in stratiform precipitation exhibit intense shallow layers of "melting convergence" at 0 degrees C level, near 4.5 km altitude, with a steep gradient on the both sides of the peak. The level of nondivergence in stratiform situations is lower than that in convective situations. These observed vertical structures of divergence are largely indicative of latent heating profiles in the atmosphere, an important ingredient of monsoon dynamics.