Fog and Low Level Stratus Forecasting using Satellite Products; A Case Study of Jomo Kenyatta International Airport, Kenya

K A Oundo, V Ongoma


The occurrence of fog and low level stratus at airports causes a number of negative impacts ranging from delays, diversions, cancellations, extra fuel leading to reduced loading capacity and customer discomfort. Some of the impacts can be greatly minimized if the occurrence of fog and low level stratus are accurately, reliably and timely forecasted. The study aimed at investigating the utilization of METAR and satellite products, as well as their performance in issuance of Terminal Aerodrome Forecast at the Jomo Kenyatta International Airport. The study is based on a case study of 20th and 21st August 2012, utilizing TAFs, Water Vapour imagery of satellite and METARs, High Resolution Visible, Infra-red channels. The fog and low level stratus were observed to form at around 0100 and 0500 UTC and dissipate at around 0500 UTC. The dissipation is mainly attributed to the incoming solar radiation. The satellite observations replicated the METARs issued. The study therefore recommends further utilization of satellite products and METAR reports in the issuance of Terminal Aerodrome Forecasts to help in minimizing the impacts associated with fog and low level stratus at the airport. However, the study calls for quantitative verification of the performance of the satellite products is however recommended to ascertain the accuracy of the products.

Full Text:



ASMET, 2013: Forecasting Fog for Aviation: Kenya Case Study. University Corporation for Atmospheric Research. (Accessed on 30.06. 2014). Baker, R., J. Cramer, and J. Peters, 2002: Radiation fog: UPS Airlines conceptual models and forecast methods. Preprints, 10th Conference on Aviation, Range, and Aerospace, Portland, OR, Amer. Meteor. Soc., 5, 11. - (Accessed on 30. 06.2014). Bendix, J., 2002: A satellite-based climatology of fog and low-level stratus in Germany and adjacent areas. Atm. Res., 64, 3-18. Bendix, J., and M. Bachmann, 1991: A method for detection of fog using AVHRR imagery of NOAA satellites suitable for operational purposes (in German). Meteorol. Rdsch., 43,169-178. Bendix, J., B. Thies, J. Cermak, and T. Nauss, 2005: Ground Fog Detection from Space Based on MODIS Daytime Data - A Feasibility Study. Wea. Forecasting, 20, 989- 1005.

COMET, 2003a: Synoptic Weather Consideration: Forecasting Fog and Low Stratus. University Corporation for Atmospheric Research. Z5SSySo–(Accessed on 30.06.2014) COMET, 2003b: Local influences on Fog and Low Stratus. University Corporation for Atmospheric Research. – (Accessed on 30.06.2014) COMET, 2003c: Applying Diagnostic and Forecasting Tools: Forecasting Fog and Low Stratus. University Corporation for Atmospheric Research. 117#.U7z9jJSSySo–(Accessed on 30.06.2014) COMET, 2003d: Customer Impacts: Forecasting Fog and Low Stratus. University Corporation for Atmospheric Research. (Accessed on 30.06.2014) Cotton, W. R., and R. A. Anthes, 1989: Storm and cloud dynamics. Academic press, Inc, New York Dupont, J. C., M. Haeffelin, A. Protat, D. Bounil, N. Boyouk, and Y. Morille, 2012: Stratus-fog formation and dissipation: a 6-day case study. Boundary-Layer Meteorol., 143, 207–225. Elias, T., M. Haeffelin, P. Drobinski, L. Gomes, J. Rangognio, T. Bergot, P. Chazette, J. C. Raut, and M. Colomb, 2009: Particulate contribution to extinction of visible radiation: pollution, haze, and fog. Atmos Res. doi:10.1016/j.atmosres.2009.01.006 Glickman, T., Ed., 2000: Glossary of Meteorology. 2nd ed. Amer. Meteor. Soc., 855 pp. (Accessed on 02.01.2015). Guidard, V., and D. Tzanos, 2007: Analysis of fog probability from a combination of satellite and ground observation data. Pure Appl Geophys, 164, 1207 - 1220 Gultepe, I., G. Pearson, J. A. Milbrandt, B. Hausen, S. Platnick, P. Taylor, M. Gordon, J. P. Oakley, S. G. Cober, 2009: The fog remote sensing and modelling field project. Bull. Amer. Meteor. Soc., 90(3), 341–359doi:10.1175/2008BAMS2534 Gultepe, I., M Pagowski and J. Reid, 2007: Using surface data to validate a satellite based fog detection scheme. Wea. Forecast, 22, 444 - 456. Hu, H, Q. Zhang, B. Xie, Y. Ying, and J. Zhang, 2014: Predictability of an Advection Fog Event over North China. Part I: Sensitivity to Initial Condition Differences, Mon. Wea. Rev., 142, 1803 – 1822. DOI: 10.1175/MWR-D-13-00004.1 Jacobs, W., V. Nietosvaara, S.C. Michaelides, H. Gmoser, (eds.), 2003; COST 722 Phase 1 Report: Very Short Range Forecasting of Fog and Low Clouds: Inventory Phase on Current Knowledge and Requirements by Forecasters and Users. 184pp. Muiruiri, S., 2011: Assessment of adverse weather on operation of air transport at Jomo Kenyatta Airport and Wilson airport. MSc Thesis, University of Nairobi- Kenya Mwebesa, M. N., 1980: Studies on fog occurrence at Jomo Kenyatta Airport. East Africa Institute for Meteorological Training and Research. Nairobi. Kenya. Niu, S., C. Lu, H. Yu, L. Zhao, J. Lu, 2010: Fog research in China: an overview. Adv Atmos Sci, 27(3), 639 - 662. Doi: 10.1007/s00376-009-8174-8 Peak, J.E., and P.M. Tag, 1989: An expert system approach for prediction of maritime visibility obscuration. Mon. Wea. Rev., 117, 2641-2653. Petersen, S., 1956: Weather Analysis and Forecasting, Vol. 1 and 2. McGraw-Hill, New York, Toronto, London. Tardif, R., and R. M. Rasmussen, 2007: Event-based climatology and typology of fog in the New York City region. J. Appl. Meteor. Climatol., 46, 1141–1168. Willett, 1928: Fog and haze, their causes, distribution and forecasting. Mon. Wea. Rev., 56, 435-468 World Meteorological Organization (WMO), 1992: International Meteorological Vocabulary, WMO 182, 782pp. Zhang, X., L. Musson-Genon, E. Dupont, M. Milliez, B. Carissimo, 2014: On the Influence of a Simple Microphysics Parametrization on Radiation Fog Modelling: A Case Study During Paris Fog. Boundary-Layer Meteorol, 151, 293–315. DOI 10.1007/s10546-013-9894-y Zhou, and Ferrier, 2008: Asymptotic analysis of equilibrium in radiation fog. J. Appl. Meteor. Clim. 47, 1704 - 1722. Zhou, B., and J. Du, 2010: Fog prediction from a multimodel mesoscale ensemble prediction system. Wea. Forecasting, 25, 303-322.


  • There are currently no refbacks.