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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 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 mu m range. For most fog cases, the droplet spectrum extends up to 50 mu m. 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. WOS:000843086400002

Dense fog events and their micrometeorological characteristics and structural evolution at Indira Gandhi International Airport (IGIA), New Delhi, during the Winter Fog Experiment (WiFEX) are illustrated in this study. Four dense fog events that occurred in January 2016 for which visibility dropped below 200 m have been selected. Depending on the visibility and micrometeorological structure, the fog processes were classified into (i) an initial formation as a thermally stable optically thin fog and (ii) a subsequent mature, weakly unstable deep fog. Surface radiative cooling supported by a deep saturated layer in the nocturnal surface layer promotes the rapid development and intensification of the initial shallow fog into the extremely dense fog. Optically thin fog appeared to develop when a thin saturated layer of air formed near the ground under low-turbulence kinetic energy (

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%) with the observed surface PM2.5 and PM10 mass concentrations. We developed an empirical regression model based on this relationship, which was then tested and validated against independent measurements of the concentrations from November 2018. Although local meteorological conditions, particularly cloudiness and rain, influenced the strength of the correlation between the observed PM2.5 and PM10 mass concentrations and the backscattered signal, the magnitude of the mean bias between the observed and the values for PM2.5 (–21 µg m–3, RMSE = 75) and PM10 (31 µg m–3, RMSE = 118) indicated that the predicted values were fairly accurate. The model overestimated the PM2.5 by 7% and underestimated the PM10 by 6% on clear days.