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Assessment of Forecast Skill of High- and Coarse-Resolution Numerical Weather Prediction Models in Predicting Visibility/ Fog Over Delhi, India


Affiliations
1 Ministry of Earth Sciences, Lodi Road, New Delhi 110 003, India
2 National Centre for Medium Range Weather Forecasting, Noida 201 309, India
 

Accurate forecasts of visibility are important to avoid disruption in air and highway traffic caused due to the formation of dense fog. However, accurate forecasting of visibility/fog remains a challenge as the genesis and development of fog is a result of many processes. In view of this, models have been developed in recent years to forecast visibility and the occurrence of fog is measured in terms of visibility. The global Unified Model of the National Centre for Medium Range Weather Forecasting, known as NCUM, provides direct output of visibility. As aviation is severely affected at the Indira Gandhi International Airport, New Delhi, India, a high-resolution model was set up to forecast visibility over the airport. The present study analyses the performance of the coarse-resolution global model and high-resolution model in predicting visibility over Delhi. Visibility is categorized into three ranges – very poor (0–200 m), poor (200–1000 m) and clear conditions beyond 1 km. The accuracy of forecast in different ranges of visibility is determined using different statistical scores. Evaluation of the results shows that the performance of both high and coarse resolution model remains low in poor visibility conditions. Though the high-resolution model performs better than the coarse-resolution model in predicting a drop in visibility, it also has higher number of false alarms. None of the model is able to predict the very poor visibility conditions. The prediction of visibility from the high-resolution model can further be improved by inclusion of real-time aerosol fields in the model.

Keywords

Aerosol, Forecast Skill, Visibility, Fog, Numerical Weather Prediction Model.
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  • Kulkarni, R. G., Wintertime fog in Delhi and its effect on aviation economy. M.Sc. Project Report Submitted to Savitribai Phule University, Pune, 2016.

  • Basu, S. C., Fog over upper Assam. Indian J. Meteorol. Geophys., 1957, 8, 67–71.

  • Natrajan, G. and Banerji, R. C., Fog over Agartala Airfield. Indian J. Meteorol. Geophys., 1959, 10, 161–167.

  • Gupta, R. K., On techniques of forecasting fog/stratus over the Dundigal airfield of Hyderabad. Mausam, 1987, 38, 401–406.

  • Tulsidas, A. and Mohapatra, M., Analysis and forecasting fog over Bangalore Airport. Mausam, 1998, 49(1), 135–142.

  • Dimri, A. P. et al., Western disturbances: a review. Rev. Geophys., 2015, 53, 225–246; https://doi.org/10.1002/2014rg000460.

  • Jenamani, R. K., Alarming rise in fog and pollution causing a fall in maximum temperature over Delhi. Curr. Sci., 2007, 93, 314– 322.

  • Pithani, P. et al., WRF model sensitivity to choice of PBL and microphysics parameterization for an advection fog event at Barkachha, rural site in the Indo-Gangetic basin. India Theor. Appl. Climatol., 2018; https://doi.org/10.1007/s00704-018-2530-5.

  • Payra, S. and Mohan, M., Multirule based diagnostic approach for the fog predictions using WRF modelling tool. Adv. Meteorol., 2014; https://doi.org/10.1155/2014/456065.

  • Goswami, P. and Sarkar, S., An analogue dynamical model for forecasting fog-induced visibility: validation over Delhi. Meteorol. Appl., 2017, 24, 360–375.

  • Petersen, C. and Nielsen, N. W., Diagnosis of visibility in DMIHIRLAM. Scientific Report 00-11. DMI, Copenhagen, Denmark, 2000.

  • Stoelinga, M. T. and Warner, T. T., Nonhydrostatic, mesobetascale model simulations of cloud ceiling and visibility for an East Coast winter precipitation event. J. Appl. Meteorol., 1999, 38, 385–404.

  • Richard, M. Chmielecki, and Adrian, E. R., Probabilistic visibility forecasting using Bayesian model averaging. Mon. Weather Rev., 2011, 139, 1629–1636.

  • Haywood, J. et al., Prediction of visibility and aerosol within the operational Met Office Unified Model. II. Validation of model performance using observational data. Q. J. R. Meteorol. Soc., 2008, 134, 1817–1832.

  • Aditi, George, J. P., Gupta, M. D., Rajagopal, E. N. and Basu, S., Verification of visibility forecasts from NWP model with satellite and surface observations. Mausam, 2015, 66, 603–616.

  • Aditi, S., George, J. P. and Iyengar, G. R., Prediction of fog/ visibility over India using NWP model. J. Earth Syst. Sci., 2018, 127, 26; https://doi.org/10.1007/s12040-018-0927-2.

  • Jayakumar, A., Rajagopal, E. N., Boutle, I. A., George, J. P., Mohandas, S., Webster, S. and Aditi, S., An operational fog prediction system for Delhi using 330 m Unified Model. Atmos. Sci. Lett., 2018, 19, e796.

  • George, J. P., Indira Rani, S., Jayakumar, A., Mohandas, S., Mallick, S., Lodh, A. and Rajagopal, E. N., NCUM data assimilation system, 2016, NMRF/TR/01/2016.

  • Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., White, A. A. and Wood, N., A new dynamical core for the Met Office’s global and regional modelling of the atmosphere. Q. J. R. Meteorol. Soc., 2005, 131, 1759–1782.

  • Edwards, J. M. and Slingo, A., Studies with a flexible new radiation code, Part 1: choosing a configuration for a large-scale model. Q. J. R. Meteorol. Soc., 1996, 122, 689–719.

  • Wilson, D. R. and Ballard, S. P., A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Q. J. R. Meteorol. Soc., 1999, 125, 1607–1636.

  • Lock, A. P., The numerical representation of entrainment in parametrizations of boundary layer turbulent mixing. Mon. Weather Rev., 2001, 129, 1148–1163.

  • Brown, A. R., Beare, R. J., Edwards, J. M., Lock, A. P., Keogh, S. J., Milton, S. F. and Walters, D. N., Upgrades to the boundarylayer scheme in the Met Office numerical weather prediction model. Bound. Layer Meteorol., 2008, 128, 117–132.

  • Smith, R. N. B., A scheme for predicting layer clouds and their water content in a general circulation model. Q. J. R. Meteorol. Soc., 1990, 116, 435–460; doi:10.1002/qj.49711649210.

  • Best et al., The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes. Geosci. Model Dev., 2011, 4, 677–699.

  • Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M. and Smith, R. N. B., A new boundary layer mixing scheme. Part I: scheme description and single-column model tests. Mon. Weather Rev., 2000, 128, 3187–3199.

  • Boutle, I. A., Eyre, J. E. J. and Lock, A. P., Seamless stratocumulus simulation across the turbulent gray zone. Mon. Weather Rev., 2014, 142, 1655–1668.

  • Lilly, D. K., The representation of small-scale turbulence in numerical simulation experiments. In Proceedings of IBM Scientific Computing Symposium on Environmental Sciences (ed. Goldstine, H. H.), NY, USA, 1967, pp. 195–210.

  • Wyngaard, J. C., Toward numerical modeling in the ‘terra incognita’. J. Atmos. Sci., 2004, 61, 1816–1826.

  • Beare, R., A length scale defining partially-resolved boundarylayer turbulence simulations. Bound. Layer Meteorol., 2014, 151, 39–55; doi:10.1007/s10546-013-9881-3.

  • Efstathiou, G. A. and Beare, R. J., Quantifying and improving sub-grid diffusion in the boundary-layer grey zone. Q. J. R. Meteorol. Soc., 2015, 141, 3006–3017; doi:10.1002/qj.2585.

  • Clark, P. A., Harcourt, S. A., Macpherson, B., Mathison, C. T., Cusack, S. and Naylor, M., Prediction of visibility and aerosol within the operational Met Office Unified Model. I: Model formulation and variational assimilation. Q. J. R. Meteorol. Soc., 2008, 134, 1801–1816.

  • Koschmeider, H., Therie der horizontalen sichtweite. Beitr. Phys. Freien Atmos., 1924, 12, 33–35.

  • Singh R. D., Singh, D. and Dube, R. K., Climatology and trend of visibility of IGI Airport, New Delhi and their use in forecasting. In Proceedings of the National Symposium TROPMET-1999, India Meteorological Society, Chennai, pp. 623–628.

  • Jenamani, R. K. and Tyagi, A., Monitoring fog at IGI airport and analysis of its runway-wise spatio-temporal variations using Meso-RVR network. Curr. Sci., 2011, 100, 491–501.

  • Boutle, I. A., Finnenkoetter, A., Lock, A. P. and Wells, H., The London model: forecasting fog at 333 m resolution. Quart. J. R. Meteorol. Soc., 2016, 142, 360–371; https://doi.org/10.1002/ qj.2656.


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  • Assessment of Forecast Skill of High- and Coarse-Resolution Numerical Weather Prediction Models in Predicting Visibility/ Fog Over Delhi, India

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Authors

Aditi
Ministry of Earth Sciences, Lodi Road, New Delhi 110 003, India
Raghavendra Ashrit
National Centre for Medium Range Weather Forecasting, Noida 201 309, India

Abstract


Accurate forecasts of visibility are important to avoid disruption in air and highway traffic caused due to the formation of dense fog. However, accurate forecasting of visibility/fog remains a challenge as the genesis and development of fog is a result of many processes. In view of this, models have been developed in recent years to forecast visibility and the occurrence of fog is measured in terms of visibility. The global Unified Model of the National Centre for Medium Range Weather Forecasting, known as NCUM, provides direct output of visibility. As aviation is severely affected at the Indira Gandhi International Airport, New Delhi, India, a high-resolution model was set up to forecast visibility over the airport. The present study analyses the performance of the coarse-resolution global model and high-resolution model in predicting visibility over Delhi. Visibility is categorized into three ranges – very poor (0–200 m), poor (200–1000 m) and clear conditions beyond 1 km. The accuracy of forecast in different ranges of visibility is determined using different statistical scores. Evaluation of the results shows that the performance of both high and coarse resolution model remains low in poor visibility conditions. Though the high-resolution model performs better than the coarse-resolution model in predicting a drop in visibility, it also has higher number of false alarms. None of the model is able to predict the very poor visibility conditions. The prediction of visibility from the high-resolution model can further be improved by inclusion of real-time aerosol fields in the model.

Keywords


Aerosol, Forecast Skill, Visibility, Fog, Numerical Weather Prediction Model.

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DOI: https://doi.org/10.18520/cs%2Fv120%2Fi4%2F676-683