Prediction of fog/visibility over India using NWP Model

  • Aditi Singh
  • John P George
  • Gopal Raman Iyengar
Article
  • 27 Downloads

Abstract

Frequent occurrence of fog in different parts of northern India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at different airports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. The present study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication of the spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable to that from simple fog model driven by NWP forecast of relative humidity and wind speed.

Keywords

Visibility fog Insat-3D METARS NCUM north India 

Notes

Acknowledgements

Authors would like to acknowledge India Meteorological Department (IMD) for providing the INSAT-3D fog images. They also thank the Head, NCMRWF, for his support in carrying out this work.

References

  1. Ballard S P, Golding B W and Smith R N B 1991 Mesoscale model experimental forecasts of the Haar of northeast Scotland; Mon. Wea. Rev. 119 2107–2123.CrossRefGoogle Scholar
  2. Bang C H, Lee J W and Hong S Y 2008 Predictability experiments of fog and visibility in local airports over Korea using the WRF model; J. Korean Soc. Atmos. Environ. 24 92–101.Google Scholar
  3. Bergot T and Guedalia D 1994 Numerical forecasting of radiation fog. Part I: Numerical model andsensitivity tests; Mon. Wea. Rev. 122 1218–1230.CrossRefGoogle Scholar
  4. Best et al. 2011 The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes; Geosci. Model Dev. 4 677–699.CrossRefGoogle Scholar
  5. Brown R and Roach W T 1976 The physics of radiation fog: II – A numerical study; Quart. J. Roy. Meteor. Soc. 102 335–354.Google Scholar
  6. Brown R 1980 A numerical study of radiation fog with an explicit formulation of the microphysics; Quart. J. Roy. Meteor. Soc. 106 781–802.CrossRefGoogle Scholar
  7. Brown A R, Beare R J, Edwards J M, Lock A P, Keogh S J, Milton S F and Walters D N 2008 Upgrades to the boundary-layer scheme in the Met Office numerical weather prediction model; Bound.-Layer Meteorol. 128 117–132.CrossRefGoogle Scholar
  8. Clark P A and Hopwood W P 2001 One-dimensional site-specific forecasting of radiation fog. Part I: Model formulation and idealized sensitivity studies; Meteorol. Appl. 8 279–286.CrossRefGoogle Scholar
  9. Clark P A, Harcourt S A, Macpherson B, Mathison C T, Cusack S and Naylor M 2008 Prediction of visibility and aerosol within the operational Met Office Unified Model. I: Model formulation and variational assimilation; Quart. J. Roy. Meteor. Soc. 134 1801–1816.CrossRefGoogle Scholar
  10. Clark et al. 2011 The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics; Geosci. Model Dev. 4 701–722.CrossRefGoogle Scholar
  11. Davies T, Cullen M J P, Malcolm A J, Mawson M H, Staniforth A, White A A and Wood N 2005 A new dynamical core for the Met Office’s global and regional modelling of the atmosphere; Quart. J. Roy. Meteor. Soc. 131 1759–1782.CrossRefGoogle Scholar
  12. Dey S and Girolama D 2010 A climatology of the aerosol optical and microphysical properties over Indian subcontinent from 9 years (2000–2008) of Multiangle Imaging Spectroradiometer (MISR) data; J. Geophys. Res. 115 D15204.CrossRefGoogle Scholar
  13. Duynkerke P G 1999 Turbulence, radiation and fog in Dutch stable boundary layers; Bound.-Layer Meteor. 90 447–477.CrossRefGoogle Scholar
  14. Edwards J M and Slingo A 1996 Studies with a flexible new radiation code. Part 1: Choosinga configuration for a large-scale model; Quart. J. Roy. Meteor. Soc. 122 689–719.CrossRefGoogle Scholar
  15. Fu G, Guo J, Xie S P, Duan Y and Zhang M 2006 Analysis and high-resolution modeling of a dense sea fog event over the Yellow Sea; Atmos. Res. 81 293–303.CrossRefGoogle Scholar
  16. Grell G A, Peckham S A, Schmitz R, McKeen S A, Frost G, Skamarock W C and Eder B 2005 Fully coupled online chemistry within the WRF model; Atmos. Environ. 39 6957–6975.CrossRefGoogle Scholar
  17. Gregory D and Rowntree P R 1990 A massflux convection scheme with representation of cloud ensemble characteristics and stability dependent closure; Mon. Wea. Rev. 118 1483–1506.CrossRefGoogle Scholar
  18. Goswami P and Tyagi A 2007 Advance forecasting of onset, duration and hourly fog intensity over Delhi; Research Report RR CM 0714, Centre for Mathematical Modelling and Computer Simulation, Bangalore, India.Google Scholar
  19. Gultepe I, Pagowski M and Reid J 2007 Using surface data to validate a satellite based fog detection scheme; Wea. Forecast. 22 444–456.CrossRefGoogle Scholar
  20. Gupta R K 1987 On the technique of forecasting fog/stratus over the dundigal airfield of Hyderabad; Mausam 38 401–406.Google Scholar
  21. Holtslag A A M, De Bruijn E I F and Pan H L 1990 A high resolution air mass transformation model for short-range weather forecasting; Mon. Wea. Rev. 118 1561–1575.CrossRefGoogle Scholar
  22. Jenamani R K and Tyagi A 2011 Monitoring fog at IGI airport and analysis of its runway wise spatio-temporal variations using Meso-RVR network; Mausam 4 491–501.Google Scholar
  23. Lock A P 2001 The numerical representation of entrainment in parametrizations of boundary layer turbulent mixing; Mon. Wea. Rev. 129 1148–1163.CrossRefGoogle Scholar
  24. Lock A P, Brown A R, Bush M R, Martin G M and Smith R N B 2000 A new boundary layer mixing scheme, Part 1: Scheme description and single-column model tests; Mon. Wea. Rev. 128 3187–3199.CrossRefGoogle Scholar
  25. Mohapatra M and Thulsidas A 1998 Analysis and forecasting of fog over Bangalore airport; Mausam 49 135–142.Google Scholar
  26. Pagowski M, Gultepe I and King P 2004 Analysis and modeling of an extremely dense fog event in southern Ontario; J. Appl. Meteorol. 43 3–16.CrossRefGoogle Scholar
  27. Petersen C and Nielsen N W 2000 Diagnosis of visibility in DMIHIRLAM Scientific Report 00-11; DMI, Copenhagen, Denmark.Google Scholar
  28. Prasad V S 2012 Conversion of NCEP Decoded data to UK MET Office Obstore format; NMRF/OB/01/2012, 33p.Google Scholar
  29. Prasad V S and Indira Rani S 2014 Data Pre-Processing for NCMRWF Unified Model (NCUM): Version 2; NMRF/RR/01/2014, 19p.Google Scholar
  30. Pruppacher H R and Klett J D 1978 Microphysics of clouds and precipitation; D. Reidel Publishing Company, Dordrecht, Netherlands.CrossRefGoogle Scholar
  31. Rajagopal E N, Iyengar G R, George J P, Das Gupta M, Mohandas S, Siddharth R, Gupta A, Chourasia M, Prasad V S, Aditi Sharma K and Ashish A 2012 Implementation of Unified Model based Analysis-Forecast System at NCMRWF; NMRF/TR/2/2012, 45p.Google Scholar
  32. Rao G V and Sullivan J O 2003 A review of some recent radiation fog prediction studies and the results of integrating a simple numerical model to predict radiation fog over Brunei; Pure Appl. Geophys. 160 239–250.Google Scholar
  33. Roybhowmik S K, Sud A M and Singh C 2004 Forecasting fog over Delhi – An objective method; Mausam 55 313–322.Google Scholar
  34. Smirnova T G, Benjamin S G and Brown J M 2000 Case study verification of RUC/MAPS fog and visibility forecasts; In: \(9^{{th}}\) Conference on Aviation, Range, and Aerospace Meteorology, Orlando, FL, American Meteorological Society, Boston.Google Scholar
  35. Swagat P and Mohan M 2014 Multirule based diagnostic approach for fog prediction using WRF modeling tool; Adv. Meteorol. 2014 Article ID 456065.Google Scholar
  36. Van der Velde I R, Steeneveld G J, Wichers Schreur B G J and Holtslag A A M 2010 Modeling and forecasting the onset and duration of severe radiation fog under frost conditions; Mon. Wea. Rev. 138 4237–4253.CrossRefGoogle Scholar
  37. Wilson D R and Ballard S P 1999 A microphysically based precipitation scheme for the UK meteorological office unified model; Quart. J. Roy. Meteor. Soc. 125 1607–1636.CrossRefGoogle Scholar
  38. Wilson D R, Bushell A C, Kerr-Munslow A M, Price J D and Morcrette C J 2008a PC2: A prognostic cloud fraction and condensation scheme. 1: Scheme description; Quart. J. Roy. Meteor. Soc. 134 2093–2107.CrossRefGoogle Scholar
  39. Wilson D R, Bushell A C, Kerr-Munslow A M, Price J D, Morcrette C J and Bodas-Salcedo A 2008b PC2: A prognostic cloud fraction and condensation scheme: Climate model simulations; Quart. J. Roy. Meteor. Soc. 134 2109–2125.CrossRefGoogle Scholar
  40. Zdunkowski W and Nielsen B 1969 A preliminary prediction analysis of radiation fog; Pure Appl. Geophys. 19 45–66.Google Scholar
  41. Zhou B, Du J, Ferrier B S, McQueen J and DiMego G 2007 Numerical forecast of fog – central solutions; In: Proceedings of the \(2^{{nd}}\) Conference on Weather Analysis and Forecasting and \(18^{{ th}}\) Conference on Numerical Weather Prediction, American Meteorological Society, Park City, Utah, USA, http://ams.confex.com/ams/pdfpapers/123669.pdf.
  42. Zhou B and Du J 2010 Fog prediction from a multimodel mesoscale ensemble prediction system; Wea. Forecast. 25(1) 303–322.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  • Aditi Singh
    • 1
  • John P George
    • 1
  • Gopal Raman Iyengar
    • 1
  1. 1.National Centre for Medium Range Weather Forecasting, Earth System Science OrganizationMinistry of Earth SciencesNoidaIndia

Personalised recommendations