The Indo-Gangetic Plain (IGP) encompassing parts of the north-central and northeastern regions of the Indian subcontinent frequently experiences periods of prolonged dense fog episodes at both mesoscale and synoptic scales throughout each peak winter season (December–January). The occurrence of a very thick blanket of large-scale fog creates a typical weather situation that often severely impacts air traffic, rail traffic, road transport, and daily lives in one of the world’s most densely inhabited areas, viz. the IGP region. The distinctive geographical structure, extensive irrigation system, extremely fertile agricultural land, land surface processes, urban expansion, and significant anthropogenic emissions in the IGP all contribute to an abundance of pollution supply as well as moisture feeding, which favors the long-term maintenance of hazy and foggy conditions over the IGP (Badarinath et al. 2007; Gautam et al. 2007). The synoptic- and mesoscale features greatly influence moisture availability, atmospheric stability, and strong low-level inversions (Dimri and Chevuturi 2016; Pithani et al. 2019a, 2020; Gunturu and Kumar 2021), which facilitate fog formation and its sustainment in the area. The intervention of the Siberian high pressure system with large-scale negative Arctic Oscillation creates the most favorable conditions for persistent fog in the IGP region over an extended period (Hingmire et al. 2019). Therefore, the widespread fog events in northern parts of India represent a unique regional-scale weather phenomenon during the winter. The increase in fog frequency, intensity, and duration in the past four decades in northern India has become a topic of intense study with profound socioeconomic implications (Srivastava et al. 2016; Ghude et al. 2017; Kulkarni et al. 2019; Kutty et al. 2019). Although our understanding of fog has deepened over the last couple of decades, its prediction using numerical weather prediction (NWP) models remains a challenge (Wilkinson et al. 2013; Steeneveld et al. 2014; Román-Cascón et al. 2016; Pithani et al. 2020) for the scientific community.
Observational studies of fog have a rich history dating back to Taylor (1917). The challenges of understanding fog are compounded by the complex and multifaceted processes that govern its life cycle, as well as the incomplete understanding of its genesis. Several factors contribute to this inherent complexity, including the intricate interplay of multiscale and multiphase dynamics (Roach et al. 1976; Pruppacher and Klett 1980; Bott 1991; Gultepe et al. 2007; Kaul et al. 2012; Liu et al. 2016; Sathiyamoorthy et al. 2016; Ghude et al. 2017). Extensive global research and observational studies have been conducted to understand the nonlinear processes that impact fog formation across various landscapes. Examples include ParisFog (Haeffelin et al. 2010), FRAM (Gultepe et al. 2014), LANFEX (Price et al. 2018), Namibian Coastal fog (Spirig et al. 2019), C-Fog (Fernando et al. 2021), and the European Action COST-722, which involved 14 nations and aimed to improve understanding of fog genesis (Michaelides 2005). Despite these efforts, several critical issues remain unclear, such as the conditions determining the exchange of heat and moisture fluxes between the radiatively cooling surface and the adjacent air, the generation and maintenance of turbulent mixing, the delicate balance of dynamics and thermodynamics, and the linkage among soluble organic compounds (aerosols), cloud condensation nuclei (CCN), and microphysical parameters [e.g., droplet number concentration (Nd), droplet effective radius (re), and liquid water content (LWC)] that pertain to the formation of saturation for fog formation. These issues remain central to fog research. Subsequently, these processes need to be simulated well with conventional empirical models or numerical prediction models to forecast fog or visibility conditions near the surface. This balance is challenging to achieve in the existing NWP models (Brown and Roach 1976; Nakanishi 2000; Bang et al. 2008; Hu et al. 2014; Steeneveld et al. 2014; Pithani et al. 2019a,b, 2020). Notwithstanding the significant progress in this field, the challenges and difficulties in fog forecasting remain the same (Pagowski et al. 2004; Bergot et al. 2005; Bergot 2013; Gultepe et al. 2006, 2007, 2009; van der Velde et al. 2010; Steeneveld et al. 2014; Román-Cascón et al. 2016, 2019; Pithani et al. 2019a, 2020). This underlines the need for developing better physical parameterization for the mesoscale numerical models to improve fog forecast.
Recent studies carried out by national and international research groups (Hingmire et al. 2019, 2021; Kutty et al. 2019, 2021; Dhangar et al. 2021; Gunturu and Kumar 2021; Verma et al. 2022) indicate significant interest in the research related to the development of fog over the IGP. However, little progress has been made in understanding the interlinking mechanism for the rapid onset and rapid thickening of the fog layer over this area due to a lack of precise collocated observations on micrometeorological, radiative, chemical, and microphysical parameters. Several studies attempted to predict the fog using empirical-based models (Bhowmik et al. 2004; Roy et al. 2011; Chaurasia et al. 2011; Saraf et al. 2011; Ahmed et al. 2015) fuzzy inference system (Mitra et al. 2008), artificial neural network algorithm (Dutta and Chaudhuri 2015), three-dimensional model–based multi-rule diagnostic method (Payra and Mohan 2014), and analog dynamical model (Goswami and Sarkar 2017). All these studies indicated ample scope for further improvement in the skill for fog onset, intensity, and duration for the implementation of operational fog forecasting. Recognizing the criticality of a scientific approach in fog research, a successful initiative has been launched in India to conduct a comprehensive measurement campaign, utilizing both ground- and space-based techniques. The program underscores the importance of science-based research in the field of fog.
The main objectives of the campaign were as follows: 1) understand and evaluate the life cycle and genesis of warm fog, 2) analyze fog microphysics in polluted boundary layer, 3) examine fog water chemistry and gas–aerosol partitioning throughout the fog life cycle, and 4) develop numerical forecasting capabilities to enhance service-oriented products for aviation and transportation sectors. To define the whole environment in which fog develops, a field campaign was designed that included collocated observations of surface meteorological conditions, radiation balance, dynamical stability and turbulence, the thermodynamic structure of the surface layer, droplet and aerosol microphysics, aerosols and fog water chemistry, the profile of winds, temperature, and humidity, supplemented by outputs from satellite and numerical modeling platforms. The campaign was conducted with the participation of national research institutes, local airport authorities, stakeholders, and international collaborators from the United States and Canada. The project is centered on a field campaign, “Winter Fog Experiment” (WiFEX), conducted at the Indira Gandhi International (IGI) Airport, New Delhi, India. The experimental phase ran for the winter seasons of 2015–20.
The present research paper is structured into five sections. The first section provides an overview of the characteristics of widespread and persistent fog formation over the IGP, detailing the underlying factors that lead to this phenomenon. The second section provides an overview of the WiFEX campaign, experimental sites, and its comprehensive instrumentation setup in detail. The third section discusses the methodology and data utilized, including the observational dataset and fog forecasting setup. The methodology is focused on understanding the physical and chemical processes contributing to fog formation. The fourth section presents the research results, including the genesis of the fog life cycle, its microphysical properties, and the role of aerosols in the fog life cycle, with insights and results on WiFEX modeling and forecasting. The final section of the paper is the epilogue and challenges, where the main findings of the research are summarized, and the potential implications of the research for future studies are discussed. Additionally, the challenges and limitations associated with the study are highlighted, underscoring the need for further research to address the outstanding issues in the field of fog formation. Overall, the research paper provides a comprehensive and detailed analysis of the physical and chemical processes underlying widespread and persistent fog formation in the IGP region, offering critical insights into this complex phenomenon.
Widespread and persistent fog over IGP
Previous studies and our WiFEX observations collectively indicate widespread and dense radiation fog is common over IGP and shows significant spatiotemporal variability at different scales depending on the local and synoptic-scale weather conditions during December and January. Long-term data at IGI Airport in Delhi show notable interannual variability and a steady rise in the frequency of fog days/hours over the past few decades (Table ES1 and Fig. ES1 in the online supplemental material; https://doi.org/10.1175/BAMS-D-21-0197.2). The process of widespread fog development over the IGP is associated with deep and extensive atmospheric subsidence (Hingmire et al. 2019), which occurs as a result of large-scale forcing (Hingmire et al. 2021), inhibiting cloud formation and increasing radiative cooling of the surface and atmosphere. This maintains the stable stratification and turbulence in a shallower stable boundary layer (SBL) and facilitates the development of saturation by turbulent mixing of heat and moisture between the surface and air adjacent to it (Dhangar et al. 2021; Gunturu and Kumar 2021). Sometimes, the fog persists for weeks over a vast area with only partial lifting in the late afternoon. As an example, Fig. 1a shows the blanket of widespread fog spell observed through INSAT-3D satellite fog retrieval product over IGP from 15 December 2019 to 1 January 2020. Figure 1b presents daily visibility hours for different intensities like 501 < Vis < 1,000 m (haze), 201 < Vis < 500 m (moderate fog), and Vis < 200 m (dense fog, which also includes severe dense fog Vis < 50 m) at WiFEX site from 1 December 2019 to 26 January 2020. Both visible satellite images (Fig. ES2a for 0215 UTC and Fig. ES2b for 0515 UTC) and WiFEX visibility observations indicate that fog blanketed a vast area of IGP (including IGI Airport) until late morning. Visibility measurements (Fig. 1b) at IGI Airport show persistent dense fog conditions (Vis < 200 m) of about 2–10 h duration from 15 December 2019 to 1 January 2020, with partial lifting in the late afternoon as indicated by low-visibility conditions for about 12–24 h every day. ECMWF reanalysis data reveal that the deep subsidence in the whole IGP due to large-scale forcing maintained the stable stratification in the region (Fig. ES3). Persistent low-visibility conditions throughout the day attenuate the incoming solar radiation (Fig. 2a), causing day temperatures to remain colder by about 4°–10°C compared to the long-term mean on most of the days (Fig. 2b). The persistence of low-visibility conditions depends on factors such as moisture availability, irradiance, turbulence and mixing processes, advection, and aerosol concentration. Effective moisture supply mechanisms that help to sustain fog and haze for longer days include evaporation at the surface due to surface-layer turbulence (Gunturu and Kumar 2021) and moisture advection (Koračin et al. 2001). WiFEX meteorological tower observations indicate the availability of sufficient moisture in the mixing layer during the entire fog spell due to a drop in the day temperature, which maintains the moisture levels well above 85% throughout the day (Fig. 2c). Observations of backscatter signals from the ceilometer and PM2.5 concentration at the WiFEX site show high aerosol loading in a shallow SBL during the fog spell due to enhanced stability and shallow boundary layer (Figs. 3a,b).
(a) Blanket of fog/haze layer formed over the IGP from 15 Dec 2019 to 1 Jan 2020 observed through the INSAT-3D satellite (source: www.mosdac.gov.in/) and (b) daily fog hours of different intensities observed at IGIA, New Delhi, during the winter season of 2019/20.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Time evolution of (a) daily mean insolation (W m−2) at IGIA, (b) the maximum temperature and the temperature departure from the long-term mean for Safdarjung station, Delhi, and IGIA, and (c) daily mean of RH (%) during boreal winter at IGIA observed from 10 Dec 2019 to 5 Jan 2020.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
(a) Cross section of attenuated backscatter coefficient (β) derived from ceilometer and (b) particulate matter concentration (PM2.5) from 15 Dec 2019 to 1 Jan 2020 at IGIA.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Chemical speciation of fine mode aerosols (PM1 and PM2.5) from WiFEX observations indicates a significant fraction of water-soluble inorganic aerosols (chloride, sulfate and nitrate, ammonium) exist during high aerosol loading, which grows exponentially in size after deliquescence and remain in a hydrated state (Acharja et al. 2020). Enhanced loading of hydrated aerosols plays a major role in visibility reduction in a subsaturated condition and the radiation balance (Hammer et al. 2014; Elias et al. 2015), aggravating the cold day conditions during longer fog spells. Cessation of large-scale forcing after a long fog spell (around 3 January 2020) eroded the stable stratification resulting in the rise in surface temperature, lowering the humidity, thus suppressing favorable conditions for the fog formation.
WiFEX field campaign and observations
Field campaign.
WiFEX site was located inside the IGI Airport, New Delhi (28.56°N, 77.09°E; 290 m above mean sea level) with a homogeneous region of around 5,100 acres of open land (Fig. 4). A suite of state-of-the-art instruments was deployed during the WiFEX intense observation period (IOP). The instruments deployed fall into four categories: atmospheric profilers, radiation and turbulent fluxes sensors, ground properties, and surface-layer meteorology. In addition, a laboratory was set up at the fire station of the airport near the observation site to obtain detailed optical and chemical properties of aerosol and fog. The micrometeorological tower, sensors, and instruments used and the laboratory setup are shown in Fig. 5.
The geographical location of the WiFEX site at IGIA and Hisar in the IGP.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
A suite of instruments deployed during WiFEX at IGIA. A 20 m micrometeorological tower equipped with weather sensors and instruments such as (in a clockwise manner) nephelometer, aethalometer, microwave radiometer, MARGA, ceilometer, CCN counter, integrated setup of visibility meter and automatic weather station, sodar, PM samplers, soil sensor (soil temperature and soil moisture), eddy covariance setup, automatic weather station, net radiometer, fog monitor, Ramdas layer sensors, and tethered balloon sonde. Technical details and periods of operation are recapitulated in Table ES1, while subset of suite of instruments deployed during WiFEX is also reported in Ghude et al. (2017).
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
This configuration was aimed at obtaining detailed information on all key processes in the fog life cycle. Every winter, Delhi experiences a combination of severe air pollution (Ghude et al. 2020; Jena et al. 2021) and dense fog events (Jenamani 2007), which also makes it an ideal place to study fog chemistry in a polluted environment. Another WiFEX station was established in Hisar, Haryana (29.14°N, 75.70°E), about 200 km northwest of the IGI Airport, in a significantly less built-up region (huge open space with higher plant cover, Fig. 4), which set observations collected at the main WiFEX site into a broader perspective. The comprehensive list of instruments deployed at these two sites is presented in Table ES2.
At the IGI Airport site, air temperature, relative humidity, wind speed, wind direction, and pressure are measured at 2, 6, 10, 16, and 20 m. Soil temperature and moisture were measured at 2, 5, 15, 25, 70, and 100 cm depths. The surface-layer fluxes of sensible and latent heat were measured using eddy covariance sensors mounted on the micrometeorological tower at 6 and 12 m. Four components of radiation fluxes and net radiation were measured at 2 m height. For ground heat flux measurements, heat flux plates (placed at 5 cm in depth) were used. These measurements collected at IGI Airport allow the complete energy balance to be calculated. Eddy-covariance sensors included a 3D-ultrasonic anemometer–thermometer and collocated CO2/H2O open-path infrared gas analyzer allowing turbulent fluxes to be calculated. Sensible and latent heat fluxes are calculated as the covariance between fluctuations in the measured vertical velocity and air temperature/water vapor mixing ratio, following several processing techniques such as spike removal (Vickers and Mahrt 1997), linear detrending (Rannik and Vesala 1999), and coordinate rotation (Mason 1995) of the original time series. Wind profiles in the surface layer were obtained up to 200 m using a mini-sodar with a vertical resolution of 5 m. At IGI, for measurements of vertical air temperature profiles, water vapor mixing ratio, and cloud liquid water, the airport site used measurements from a ground-based microwave radiometer that was constantly operational during the experiment. Radiosonde observations were routinely carried out at 0000 and 1200 UTC by India Meteorology Department (IMD), and the radiosonde site was located nearly 10 km from the IGI Airport site.
Visibility at 1 min resolution was monitored inside the airport area using a visibility meter at 2 m height near the flux tower base, and the Runway Visual Range (RVR) system of IMD was installed at each runway. Fog droplet size distribution (3–50 μm) and LWC were monitored at 2 m by a fog monitor instrument. For characterizing the absorption and scattering properties of the aerosols, an aethalometer, nephelometer, and photoacoustic extinction meter (PAX) were deployed to collect data at 1 min intervals. Measurements of water-soluble inorganic ions (Cl−,
Observations and forecast.
The field campaign was conducted in a pilot mode from 15 December 2015 to 15 February 2016 (Ghude et al. 2017). In line with the pilot campaign, a detailed field experiment was set up every winter between 2016 and 2020 at IGI Airport. Table ES3 shows the number of dense fog events (Vis < 200 m) and the main characteristics observed during the entire experimental period (2015–20). During the 5 years of the field experiment, a total of 86 dense fog events for at least 2 h and 83 moderate fog events (not shown in the table) for visibility less than 1,000 m but greater than 500 m occurred during WiFEX. It can be seen that a significant variability of onset, dissipation, length, and intensity of fog was sampled. Out of 86 dense fog events, 44% of events were triggered during midnight (1830–2130 UTC) and the remaining 46% in the early morning (2330–0130 UTC) hours. On some days, fog events with Vis < 50 m (for more than 3 h) events were observed, representing an opportunity to sample sufficient fog water to investigate the chemical composition of the dissolved material in fog droplets. Table ES3 also indicates the events when fog water samples were collected during the 2015–17 period, and a summary of the chemical analysis of fog water is presented in the fifth section. In parallel, during the 2016/17, 2017/18, 2018/19, and 2019/20 winters, several real-time simulations using the Weather Research and Forecasting (WRF) Model were conducted in an experimental mode which provided deterministic fog forecasts at 2 km resolution. These forecasts were also used to plan the IOPs during WiFEX. There were 60 vertically stretched model levels with 20 levels nearest to the surface in the lower 1 km of the atmosphere (see Pithani et al. 2020). Prior to this, hindcast sensitivity experiments were conducted to validate the model configuration based on fog events observed during the pilot campaign in 2015/16. The best suite of physical parameterizations was identified that show relatively better skill in predicting the fog (Pithani et al. 2019b) at IGI Airport.
Results
Selected typical results from a few IOPs on the key surface-layer, thermodynamical, microphysical, and chemical and aerosol processes involved in the development of thick fog and model simulations are given below. Comprehensive technical results on the above aspects are available in various peer-reviewed papers (Bisht et al. 2016; Ghude et al. 2017; Ali et al. 2019; Safai et al. 2019; Kulkarni et al. 2019; Pithani et al. 2019a,b, 2020; Parde et al. 2020; Acharja et al. 2020, 2022; Ahmed et al. 2021; Dhangar et al. 2021; Parde et al. 2022a,b; Wagh et al. 2022; Yadav et al. 2022). Analysis is currently underway (Dhangar et al. 2022) and will be further documented in forthcoming publications (Theethai Jacob et al. 2023).
Dense fog events.
Dense fog events with Vis < 200 m are often observed over IGP (Table ES3). Here we detail the environment of a dense fog event that happened on 28 January 2018 and is presented as an example (Figs. 6–8). The data provided comprehensive insights into key processes involved in the life cycle of an interesting case of extremely dense fog that started at midnight (Vis < 200 m, optically dense fog) and lasted for about 6.5 h, while fog conditions (Vis < 1,000 m) lasted for about 15 h. INSAT-3D-based fog product (at 0300 UTC) and MODIS (Terra) satellite images (at 0500 UTC) indicated the presence of a dense and widespread fog blanket over the IGP, including the WiFEX site (Fig. ES4). The synoptic-scale conditions showed subsidence of high pressure and low wind speeds (<5 m s−1) dominated by northerly and northwesterly cold winds conducive to the fog formation.
Cross sections of attenuated backscatter from (a) ceilometer, (b) equivalent potential temperature (shading) and integrated water vapor (black line), (c) temperature, (d) RH, and (e) LWC derived from microwave radiometer installed at IGIA.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Time series of (a) visibility at 2.5 m, (b) air temperature, (c) cooling rate, (d) relative humidity, (e) relative humidity gradient (2 and 20 m) and that between (10 and 20 m), (f) turbulent kinetic energy, (g) vertical velocity variance, (h) wind speed and wind direction, (i) longwave radiation (incoming and outgoing), (j) soil heat flux, (k) soil temperature, and (l) volumetric soil water content at 0 and −2 cm on 27–28 Jan 2018 at IGI Airport.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Time series of (a) aerosol concentration measured by SMPS, (b) CCN concentration at various supersaturations for the period, and (c) PM1 and PM2.5 mass concentration measured by MARGA. The gray shaded area denotes the foggy period for the 28 Jan 2018 case.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Sunset (1130 UTC, 1700 IST) was marked by a steady decrease in mixing-layer depth (Fig. 6a) and surface temperature (Fig. 7b). Accumulation of large amounts of water-soluble aerosols ions [
Figure 7a illustrates the temporal variation in surface visibility and the presence of extremely dense fog at IGI Airport. Just after the sunset, the fog initially developed as a haze (1430–1800 UTC, Vis between 1,000 and 500 m), then transformed into the moderate fog (1830–2100 UTC) and remained patchy and optically thin for about 2.5 h (with visibility fluctuating between 500 and 300 m). Eventually, the fog suddenly burst at 2100 UTC and afterward became dense, deeper, persistent, and weakly stable until late morning (0330 UTC). Fog again transformed into an optically thin phase until 0500 UTC (about 1.5 h), when dissipation occurred due to solar heating. These observations highlight the complexity of the fog in the aerosol-rich environment and give insight into the many thermodynamical and aerosol processes that were at play during the whole life cycle of the dense fog event. Of particular relevance are factors that contribute to the development of optically thin phase and trigger optically thick and deep fog, a key process for the development of haze and dense fog.
Fog development and haze formation.
Figure 7b shows temperature evolution from the tower-mounted sensors (2, 10, and 20 m), soil temperature (−1 cm), and surface heat flux, which shows the marked transition during the early evening (around 1200 UTC) from unstable to strong stably stratified conditions. Immediately after sunset, soil temperature dropped from 21°C at 1200 UTC to 7.5°C at 2100 UTC. The cooler surface allowed heat flux to reverse the heat flow from +10 to −25 W m−2. Because of the radiative cooling of the surface, the air close to the surface exchanged radiant energy directly with the cooled surface, and the surface temperature dropped quickly and diverged at vertical levels reaching a cooling rate of −2°C h−1 (20 m) around 2200 UTC. As a result, the surface air temperature dropped by about 10°C before the onset of the dense phase and showed a strong vertical gradient in temperature of about 1.5°–2°C between 2 and 20 m before the onset of dense fog. Observations of total aerosol particle number count (cm−3) and CCN number concentration (at 0.1%, 0.3%, 0.5%, 0.7%, and 0.9% supersaturation level) indicate a large increase in aerosols and CCN activity, respectively (Figs. 8a,b). Between 1430 and 1830 UTC, gradual cooling of the surface increased the relative humidity by about 22% (62%–85%), and a large accumulation of water-soluble fine-aerosol mass concentration in PM1 (up to 150 μg m−3) and PM2.5 (up to 230 μg m−3) in the surface layer (Fig. 8c) was noted. This indicated that a significant load of hydrated aerosol (that had not yet reached delinquencies) grew in mass due to increased humidity. As a consequence, visibility dropped from 1,000 to 500 m. Persistent cooling of the surface dropped the 2 m temperature to near the saturation point at around 1830 UTC, which led to a further increase in the RH (above 95%–97% at 2 m) and triggered the optically thin fog at the site. Weakening of the surface net longwave radiation was seen due to a slight increase in downward longwave radiation (Fig. 7i). However, surface temperature and soil thermal flux showed little change. Hence, the dominance of radiative cooling persisted. This allowed the continued cooling of the air inside the optically thin fog at vertical levels and the persistence of thermal stability at the lower level, which is necessary for an optically thin fog to persist (Zhou and Ferrier 2008).
For the next 1.5 h interval (1830–2100 UTC), humidity slowly increased and attained the value above the delinquency point of water-soluble ions, which promoted enhanced gas-to-particle conversion (Acharja et al. 2022). Significant water vapor uptake led to a large increase in the mass of hygroscopic particles (Fig. 8c). Arguably, some fractions of water-soluble ions subsequently grew larger beyond the PM1 and PM2.5 cutoff size of the instruments due to hydration and were responsible for an apparent slight decrease in water-soluble PM1 and PM2.5 mass concentration.
However, these larger particles remained in the hydrated stage but were not activated in the liquid droplet as the liquid droplet concentrations and the LWC measured at 2.5 m height remained very low during this phase (Fig. 9b). On the other hand, CCN count (∼8,000 cm−3 at 0.9% supersaturation) and total aerosol particle number count (∼100 k cm−3) remained high because of the constant supply of aerosols. As a result of the hydration of aerosols (but not its activation), visibility further dropped below 500 m and fluctuated between 500 and 300 m. It can be seen that the saturated air near the surface was thin and did not grow deeper as the layer between the surface and 20 m remained subsaturated and maintained a difference of about 5% (Fig. 7e).
The temporal evolution of (a)–(d) microphysical parameters and visibility obtained using FMD and visibility sensors for the dense fog case observed on 28 Jan 2018 and (e) scattering coefficient (σ) observed by nephelometer at two wavelengths: 525 and 635 nm.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Microphysical structure.
The cooling of the surface caused air near the surface to supersaturate and grow deeper rapidly. Eventually, the entire layer became saturated with water vapor (Fig. 6d). This triggered the transition from the optically thin phase to the optically dense fog layer at around 2100 UTC. Sudden activation of a large number of hydrated aerosols into fog droplets was evident. As a result, a significant decrease in the mass concentration of water-soluble ions in PM1 and PM2.5 are discerned at the cost of large droplet formation and wet scavenging. The fog droplets concentration (Nd) measured at 2.5 m height reached up to 800 cm−3 in the next 10 min (Fig. 9a), causing a sudden increase in the particle surface area (Fig. 9e) and a large drop in visibility (<200 m). LWC derived from the fog monitor at the surface (Fig. 9b) showed a rapid increase in LWC (0.32 g m−3), and liquid water profiles from the radiometer showed that it remained high from the surface up to 400 m (0.3 g m−3) until the dissipation stage. The mean value of Nd, LWC, and effective radius (Reff) during the entire dense phase was found to be 409.38 cm−3 (σ = 274.23 cm−3), 0.16 g m−3 (σ = 0.11 g m−3), and 6.52 μm (σ = 1.97 μm), respectively. A significant variation in Nd, LWC, Reff, and liquid water profiles indicates that the fog layer was dynamic but remained deep and extensive. Mean droplet size distribution (DSD) shows that the droplets in the size range of 3–7 μm are present in large number (∼200 cm−3 at 2.5 m) during the formation phase, whereas the tail of DSD spectra was extended to a drop diameter of ∼40 μm during mature and dissipation stage (Fig. 10a).
(a) Mean droplet size distribution during various phases of the fog event, (b) the relation between Nd and LWC scaled to Reff for the entire fog event. (c)–(f) The variation of LWC with Nd during the different fog phases, namely, (c) formation, (d) development, (e) maturation, and (f) dissipation.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Interestingly, small droplets were also present in significant numbers (∼100 cm−3 at 5 μm) during the development and mature phase, indicating a continuous supply of hygroscopic aerosol and activation of hydrated aerosols into fog droplets. When the condition changed from the formation–development–mature stage, small fog particles eventually grew larger, and the concentration of larger droplets increased with time due to vapor deposition and collection process (Figs. 9c and 10b–f) induced by the increase in turbulent fluxes in a saturated fog layer (Fig. 7f). As a result, Reff increased from 6 to 9 μm, whereas the total number concentration slightly decreased during the mature phase and the contribution of larger droplets in LWC increased (Fig. 10e).
Soon after the dense fog formation, temperatures at 4, 10, and 20 m showed a slight increase (0.5°C). The intensity of downward longwave radiation increased and became nearly equal to upward radiation intensity due to the emission and absorption of the longwave radiation by fog droplets. Due to radiative heating inside the fog layer, the thermally stable structure of the surface layer quickly changed to an approximately saturated adiabatic profile. This led to a weakly unstable surface layer with a slightly warmer temperature near the surface (∼0.5°C). Warming of the fog bottom contributed to an increase in upward soil heat flux by about 16 W m−2 (Fig. 7j). It led to an increase in canopy temperature by about 2.5°C (Fig. 7k). Radiative warming inside the fog layer caused enhancement of turbulent kinetic energy (TKE) (and vertical velocity variance) from 0.08 (0.02) to 0. 35 (0.12) m2 s−2 (Fig. 7) which promoted the rapid increase in the mixing process and saturation level and broadening of the DSD spectra. Interestingly, the fog layer could endure TKE values up to 0.35 m2 s−2 and maintain a steady balance for about the next 6.5 h. Higher values of TKE in dense fog regimes imply that the fog layer was deep, and the cooling of the fog top was strong enough to prevail over the heating near the surface.
Complete dissipation of fog occurred at around 0500 UTC (Vis > 1,000 m) due to enhanced turbulent fluxes (TKE ∼ 0.6 m2 s−2) and possible intrusion of drier air aloft due to vertical mixing (vertical velocity variance ∼ 0.25 m2 s−2) induced by the shortwave heating of the surface (ground heat flux ∼ +10 W m−2), thus completing the fog life cycle. Interestingly, at around 0300 UTC, the total droplet number count and LWC quickly reduced to a near-zero value, possibly due to the evaporation of fog droplets. This notion is supported by reasonable enhancement in the mass of water-soluble ions of size less than 2.5 μm (Fig. 8e) during the ephemeral drop of surface fog density. During this period, it is possible that most aerosol particles remained hydrated, as data did not indicate a significant change in the near-surface humidity. As a result, dense fog transformed into an optically thin phase (500 < Vis < 1,000 m) for about 1.5 h before dissipating completely.
Aerosol and droplet distribution during moderate and dense fog events.
The persistence and dissipation of fog during moderate (2–3 January 2018, IOP1) and dense fog (25–26 January 2018, IOP2) events provided insight into the aerosol fog interaction during the fog life cycle (Fig. 11). On 3 January 2018, when the fog was moderate, visibility fluctuated between 300 and 800 m for about 3.5 h and quickly dissipated in the midnight hours (Fig. 11a).
(a),(b) Time evolution of Nd (cm−3), LWC (g m−3), and Vis (m) for the fog cases observed on 3 and 26 Jan 2018. (c),(d) SMPS-measured aerosol concentration before, during, and after the fog for both the cases. (e),(f) Variation in mass concentration of NO3 and SO4 on 3 and 26 Jan 2018, respectively. The blue arrow indicates the fog dissipation time of the respective fog events.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
Observations of fog droplet number concentration show significantly low values of LWC (Fig. 11a, red) (fluctuating between 0.002 and 0.015 g m−3) and droplet number concentration (fluctuating between 50 and 250 cm−3) (Fig. 11a, black). This indicates that a small number of aerosols could activate into the fog droplets, and the population of hydrated aerosols largely dominated the low-visibility conditions. On the contrary, during dense fog days, visibility remained below 200 m (lowest 100 m) throughout the night and late morning hours (about 9 h) (Fig. 11b). A significant number of hydrated aerosols activated into fog droplets (up to 800 cm−3) (Fig. 11b, black), and a substantial increase in LWC (0.4 g m−3) (Fig. 11b, red) was observed. The fog droplet spectra for both cases show the dominance of small fog droplets (mode diameter ∼5 μm). Although the mode diameter is the same for both cases, moderate fog showed a narrow droplet spectrum, while a broad spectrum with a tail extending up to 50 μm was evident for dense fog (Fig. ES7; Wagh et al. 2022). During the mature phase, the concentration of large fog droplets (Reff > 30 μm) increased, possibly due to vapor deposition and collection process, which was not the case for moderate fog observed on 3 January (Fig. 7). Humidity profiles from the microwave radiometer show that the surface layer was moister and deeper during 26 dense fog events, which possibly supplied more moisture for the sustainment of fog and growth of the fog droplets in the conducive environment (Fig. ES8).
The aerosol size distribution spectra (7–300 nm) one hour prior to the onset of fog on both moderate (IOP1) and dense fog (IOP2) days show a nearly similar bimodal spectrum (Figs. 11c,d). During the transition to the fog phase, particle number concentration decreased with a modest shift/increase in mode diameter for both IOP1 and IOP2. The number of small particles reduced, and subsequent growth of Nd was observed, distinct in the case of IOP1. The smaller particles undergo hygroscopic growth and act as fog condensation nuclei (FCN), depending upon the chemical composition and size of the particle. Nevertheless, some particles could not achieve the critical radius for hygroscopic growth and remained suspended in the atmosphere with droplets. The decrease in the concentration of the fine particles with the development of fog is observed on both IOP1 (Fig. 11c, red) and IOP2 (Fig. 11d, red); scavenging of fine particles with droplets or particles becoming FCN is the reason for the decrease in fine mode particle concentration. A striking feature is the appearance of new particle formation immediately after the dissipation of the dense fog (IOP2) (Fig. 11d, blue). Aerosol spectra show a large increase in particle number concentration in size < 20 nm (Fig. 11d, blue), which was not the case for moderate fog events (IOP1). An increase in particle number concentration is possibly related to the gas-to-particle secondary organic aerosol formation and release of secondary aerosols by droplet phase reaction after the droplet evaporation. Fog in a polluted environment has the potential to increase aerosol concentration by droplet phase reaction. Pandis et al. (1992) estimated that more than half of the sulfate in a typical aerosol air pollution episode was produced inside a fog layer. MARGA-based chemical measurements show a large increase in hygroscopic sulfate
Physicochemical properties of aerosols and fog water samples during WiFEX
Physicochemical and thermodynamical properties of fine aerosols and trace gasses were characterized during WiFEX to improve the scientific understanding of the life cycle of fog and aerosol fog interaction. Measurements characterizing the compositions of aerosols are important as it affects the exchange of constituents between the gas and aerosol phase and determines in which physical state (solid, semisolid, or liquid solution phase) aerosol remains in the atmosphere. With more than 3 years of data, the aerosol chemistry of fine aerosols during winter periods over Delhi showed the dominance of chloride, nitrate, sulfate, and ammonium in the total measured inorganic ionic mass (Fig. 12).
Mass concentrations and percentage contributions of the major inorganic constituents of (a),(b) PM2.5 during the winter period of 2015/16, (c) PM1 and PM2.5, and (d) trace gases during December 2017–February 2018 over Delhi, India. (c-i) Chemical composition in PM1, (c-ii) chemical composition in PM2.5, and (d-i) contribution of the precursor gasses.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
The simultaneous hourly measurements of the inorganic constituents (Cl−,
Though the concentrations of the acidic ions
WiFEX modeling
WiFEX objectives include numerical modeling activities aimed at improving understanding and enabling the prediction of fog. The high-resolution WRF simulation was an integral part of the fog process studies and was used as a tool for identifying the strengths and weaknesses of the model to provide a real-time deterministic fog forecast during WiFEX 2016–20. WRF was used to simulate a dense fog event on 28 January 2018, demonstrating its utility for fog prediction and NWP model validation. WRF-ARW V3.6 (Skamarock et al. 2008) simulations at 2 km horizontal grid resolution were conducted with MYNN2.5 PBL and WSM6 microphysics schemes at 20 vertical levels below 1 km (Collins et al. 2004; Hong and Lim 2006; Nakanishi and Niino 2006; Gilliam and Pleim 2010; Pithani et al. 2020). The detailed model configuration is given in the supplement. The time evolution of simulated surface LWC and its validation with observed visibility and LWC (FM-120 and MWR) at the WiFEX site is shown in Fig. 13a. The forecast captures the fog event quite well and shows a reasonable agreement with observed LWC, but with a smaller vertical depth (80 m) than the observation (∼300 m) (Figs. 13k,l). The sounding and MWR profiles at 0000 UTC showed deeper inversion and moist conditions. The model forecast correctly predicted the low-level inversion intensity and near-surface moisture, showing that near-surface atmospheric conditions are efficiently replicated during this event. Although the skin temperature (T2) and surface temperature (−1 cm) have a cool bias, the comparison demonstrates that the simulated dewpoint depression is often similar to the measurements. This might be because of the quick drop in the simulated heat flux after sunset.
Comparison of the model-simulated (a) LWC, (b) top-level soil temperature, (c) temperature at 2 m, (d) relative humidity at 2 m, (e) dewpoint depression at 2 m, (f) turbulent kinetic energy, (g) downward longwave radiation, (h) soil heat flux, (i) vertical profile of temperature at 0000 UTC 28 Jan 2018, and (j) relative humidity with observed data. Time–height cross sections of (k) MWR-derived LWC and (l) model-derived LWC at WiFEX site, IGI Airport during 27–28 Jan 2018. Horizontal gray and blue shading in (a) indicate the temporal hazy and foggy condition. Vertical gray shading in (b)–(h) indicates the dense fog period.
Citation: Bulletin of the American Meteorological Society 104, 5; 10.1175/BAMS-D-21-0197.1
The model also simulates observed TKE satisfactorily; however, more stable conditions are seen in the model (0.05 m2 s−2) than in the observations (0.1 m2 s−2) before the fog onset. The model showed a significant increase in downward longwave radiation, but it was underestimated due to the small vertical depth of the modeled fog. Interestingly, the onset (2230 UTC) and duration (7.5 h) of the optically thick phase (Vis < 200 m) and the timing of the model-forecasted fog appearance were in general agreement with the visibility and LWC observations. However, the duration of the observed fog (11 h) was much longer than the model (7.5 h). In the absence of detailed atmospheric chemistry and chemical/thermodynamic mass balance parameterization, the model follows the default prescribed aerosol number concentration in the microphysics scheme. Therefore, the model could not capture the optically thin phase, perhaps due to the inability of the model to simulate the realistic hydration of the water-soluble inorganic ions in subsaturated conditions. This demonstrates the need for the model to include detailed aerosol chemistry processes for realistic simulations of fog in polluted environments.
The performance of the real-time forecast, that is, whether the fog is predicted or not, was assessed using the contingency table (Table ES4). During winters (December–January) 2016–20, forecasts for 67 dense fog (Vis < 200 m) events were recorded, and the model predicted 39 events successfully. The model gave more false alarms (57 events) and missed 28 events. The skill score for hit rate (0.54), false alarm (0.56), and missing rate (0.47) was nearly equal, which indicates that the model had moderate accuracy for predicting fog events at the IGI Airport. This demonstrates the need for the model to precisely capture detailed physical processes and work toward addressing inaccuracy in the forecast is being pursued.
Epilogue and challenges
The WiFEX interdisciplinary field experiment was undertaken at IGI Airport in New Delhi over six winter seasons (2015–20) to increase scientific knowledge and prediction of fog in a polluted IGP region. The complex environment in which fog develops has been characterized by an impressive collection of in situ and remote sensing instruments. In a heavily urbanized and polluted area like IGP, a few examples are provided to understand the mechanism underlying fog formation. The WiFEX dataset now contains over 89 dense fog (Vis < 200 m) events and a similar number of moderate fog events (500 > Vis > 201 m). WiFEX data collection also includes long-spell (7–15 days) fog episodes, which are unusual in other regions of the world. This is another distinctive aspect of the WiFEX dataset. These events are particularly suited to enhance our understanding of how large-scale meteorological forcing plays a role in the persistence of fog and haze for weeks with partial lifting in the afternoon. The broad results drawn from the investigation thus far show that 1) the large mass of water-soluble inorganic ions, where these ions hydrate in subsaturated conditions, strongly modulates the formation of optically thin fog before and after the thick phase and 2) the progressive growth of the saturated surface layer in the nocturnal boundary layer promotes the rapid development and intensification of the initial shallow fog into the extremely dense fog; therefore, the shape of the relative humidity profile in the fog onset is important.
One of the key conclusions from a model case study revealed that the WRF Model is incapable of simulating realistic hydration of water-soluble inorganic ions in subsaturated environments. This can seriously affect fog visibility predictions. Results have shown that micrometeorological, thermodynamic, surface dynamical processes, microphysical properties, and synoptic-scale systems are alone not governing the fog genesis and evolution, but the detailed chemical processes also play a crucial role in the fog life cycle and should necessarily be considered in fog modeling efforts over polluted regions. Although the real-time numerical fog forecast was issued using WRF during WiFEX 2016–20, the forecasting accuracy of fog during WiFEX was ∼54%. This low forecasting skill might largely be related to initial and boundary conditions, boundary layer and microphysical parameterization, and the land surface process, which play a decisive role in stimulating conducive conditions for fog in the model environment. The WiFEX data are anticipated to provide a basis for further research into the physicochemical parameters that affect the genesis of fog production and its life cycle and undermine the accuracy of fog forecasting.
Acknowledgments.
We thank the Director Indian Institute of Tropical Meteorology (IITM), Pune; the Director-General of the India Meteorological Department (IMD), New Delhi; Vice Chancellor, CCS Haryana Agricultural University, Hisar, India, Director, National Centre for Medium-Range Weather Forecasting (NCMRWF), Noida, and Director, Indian Institute of Science Education and Research (IISER) Mohali for their encouragement and support during WiFEX; the Ministry of Earth Sciences (MoES), Government of India; Savitribai Phule University, Pune, for support throughout the campaign and Grandhi Mallikarjuna Rao (G.M.R.) group and Airports Authority of India (AAI) for providing logistic support and cooperation to conduct the experiment inside IGI Airport, New Delhi. We are thankful to C-DAC for providing financial support through the National Supercomputing Mission (NSM) programme to PP, PA, AV, PY, and PL. We also thank Dr. K. J. Ramesh (former IMD director) and IMD staff for providing satellite images and synoptic charts; MODIS satellite images for public access used in this study; we sincerely thank Prof. G. S. Bhat (IISc) for key advice at various stages of the project. We also thank scientists of IITM: Dr. Kaushar Ali, Dr. P. Mukhopadhyay, Dr. Phani Murali Krishna, Dr. M. N. Patil, and Dr. Shivsai Dixit. The authors also acknowledge the IITM, Pune, GFS data used in this study. The authors also thank scientists of NCMRWF: Dr. V. S. Prasad. We acknowledge MOSDAC for INSAT-3D data products (www.mosdac.gov.in). Google Maps wase used for the geographical representation of the observation site inside IGI Airport, New Delhi. Also, we appreciate the ECMWF for making available the reanalysis data of ERA5. WiFEX real-time fog forecasting and data processing were carried out on the Aditya and Pratyush High-Performance computing system at the IITM, Pune, India.
Data availability statement.
Campaign data are stored at the data repository at the Indian Institute of Tropical Meteorology and are publicly available as per the Ministry of Earth Science (MoES), Government of India data sharing guidelines (https://ews.tropmet.res.in/wifex/).
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