On the Conditions for Onset and Development of Fog Over New Delhi: An Observational Study from the WiFEX

Dhangar, Narendra G.; Lal, D.; Ghude, Sachin D.; Kulkarni, Rachana; Parde, Avinash N.; Pithani, Prakash; Kandula, Niranjan; Prasad, D. S. V. V. D.; Jena, Chinmay; Sajjan, Veeresh S.; Prabhakaran, Thara; Karipot, Anandakumar; Jenamani, Rajendra Kumar; Singh, Surender; Rajeevan, M.

doi:10.1007/s00024-021-02800-4

Cited by 19 publications

(19 citation statements)

References 42 publications

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“…Figure 1 illustrates that the fog persists in the moderate fog category (includes both CAT I and CAT II; 550 m < Vis < 300 m) for almost 111 h (~49%), while for 115 h (~51%), fog was recorded in the dense fog category (CAT III). Typically, this optically thick and deep dense fog category [47] is the most challenging period for pilots and air traffic control (ATC) is to decide on take-off and landing of flights at runways.…”

Section: Observational Datasetmentioning

confidence: 99%

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

et al. 2022

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One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts.

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Section: Observational Datasetmentioning

confidence: 99%

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

et al. 2022

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“…The enhanced surface warming from the UHI can increase the nocturnal turbulence due to unstable conditions near the surface and thus more turbulent mixing in the fog layer. Dhangar et al (2021) found an increase in turbulent kinetic energy (TKE) from 0.15 to 0.4 m 2 s −2 during their optically thick phase of the fog at IGIA. This is indicative of adiabatic fog conditions, wherein the stability is close to neutral, vertical mixing is rapid, and the surface and fog top are strongly coupled (Porson et al, 2011;Price, 2011;2019;Price et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Further, Dhangar et al . (2021) found that, during the “optically thick” phase of their fog events, the downwelling long‐wave (LW) radiation increased and nearly equalled the upwelling LW intensity (the net longwave radiation at the surface being almost zero), indicating that the fog layer became thick and effectively well mixed. Zhou and Ferrier (2008) showed that generally, for dense fog cases, the LW radiative cooling is maximum at the fog top, followed by a steady decrease and slight warming near the surface.…”

Section: Introductionmentioning

confidence: 99%

“…Observations by Ghude et al (2017) at IGIA, New Delhi, had suggested the prevalence of a mini convective boundary layer structure, as inferred from the potential temperature (θ E ) estimates, which sustains turbulence at the bottom and top of the fog layer, during their winter fog case studies. Further, Dhangar et al (2021) found that, during the "optically thick" phase of their fog events, the downwelling long-wave (LW) radiation increased and nearly equalled the upwelling LW intensity (the net longwave radiation at the surface being almost zero), indicating that the fog layer became thick and effectively well mixed. Zhou and Ferrier (2008) showed that generally, for dense fog cases, the LW radiative cooling is maximum at the fog top, followed by a steady decrease and slight warming near the surface.…”

Section: Introductionmentioning

confidence: 99%

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Delhi Model with Chemistry and aerosol framework (DM‐Chem) for high‐resolution fog forecasting

Jayakumar

Gordon

Francis

et al. 2021

Quart J Royal Meteoro Soc

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We introduce here the Delhi Model with Chemistry and aerosol framework (DM-Chem), a high-resolution (330 m) visibility and fog forecasting model with the UK Chemistry and Aerosol scheme. DM-Chem is developed at the National Centre for Medium-Range Weather Forecasting (NCMRWF) in collaboration with UK Met Office and Carnegie Mellon University. We present a revised visibility scheme here which uses prognostic aerosols (mass and number concentration) from the two-moment prognostic Global Model of Aerosol Processes (GLOMAP) -mode aerosol scheme. This scheme represents aerosol microphysical processes and the internal mixing of chemical components within five log-normal size modes. The cloud microphysics parameterization also makes use of the GLOMAP scheme to predict cloud droplet number concentration (N d ).Therefore we are able to evaluate the performance of the scheme indirectly by comparing the predicted N d with that derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals over the Indo-Gangetic Plain. Aerosol emissions for the inner (330 m) nest are prescribed with a high-resolution inventory for Delhi, from the System of Air Quality and Weather Forecasting project (SAFAR). Winter-time stubble burning is represented using the MODIS-based Fire Energetics and Emissions Research inventory, updated daily. In this article we consider case studies spanning both foggy and haze days of winter 2019-2020. The model compares well with the observed visibility and its spatial and diurnal patterns. The SAFAR emissions help improve the near-surface model fields. For the "haze" case, the single scattering albedo (SSA) is high along the regions of high AOD, and the surface radiative cooling is enhanced. During the "foggy" case, the SSA is not clearly related to AOD, but aerosol absorption does lead to a minor surface warming via the semi-direct effect at the time of onset of fog. The DM-Chem entered operational mode at NCMRWF from winter 2020 and provides high-resolution visibility forecast for Delhi and the adjoining region.

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“…During winter, a strong radiative thermal inversion causes the megacity to be enveloped by a shallow planetary boundary layer (PBL) in the nighttime, resulting in high relative humidity (RH) and aerosol mass burden (Arun et al, 2018;Murthy et al, 2020). The cold, humid and polluted conditions coupled with low wind speeds make the landlocked atmosphere conducive to fog and haze formation (Dhangar et al, 2021;Dumka et al, 2019;Ojha et al, 2020). Moreover, the aerosols in Delhi have enhanced water uptake ability as reported in the companion study by Gunthe et al (2021), which can facilitate multiphase processes for formation of aerosols and thereby cause drastic visibility deterioration.…”

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confidence: 99%

Planetary Boundary Layer Height Modulates Aerosol—Water Vapor Interactions During Winter in the Megacity of Delhi

et al. 2021

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The megacity of Delhi is located within the Indo-Gangetic Plain (IGP) and is one of the major sources of anthropogenic air pollution. It is a continental metropolitan area in a large valley south of the Himalayas, which causes the air masses to be constrained within the IGP (Figure 1). The air masses are vulnerable to high levels of particulate matter emissions from the megacity all year round (Bhandari et al., 2020), since it is a fast-growing urban agglomeration (Jain et al., 2016;Paul et al., 2021). During winter, a strong radiative thermal inversion causes the megacity to be enveloped by a shallow planetary boundary layer (PBL) in the nighttime, resulting in high relative humidity (RH) and aerosol mass burden (Arun et al., 2018;Murthy et al., 2020). The cold, humid and polluted conditions coupled with low wind speeds make the landlocked atmosphere conducive to fog and haze formation (Dhangar et al., 2021;Dumka et al., 2019;Ojha et al., 2020). Moreover, the aerosols in Delhi have enhanced water uptake ability as reported in the companion study by Gunthe et al. (2021), which can facilitate multiphase processes for formation of aerosols and thereby cause drastic visibility deterioration.

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On the Conditions for Onset and Development of Fog Over New Delhi: An Observational Study from the WiFEX

Cited by 19 publications

References 42 publications

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

Delhi Model with Chemistry and aerosol framework (DM‐Chem) for high‐resolution fog forecasting

Planetary Boundary Layer Height Modulates Aerosol—Water Vapor Interactions During Winter in the Megacity of Delhi

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