مطالعه ی آماری و شناسایی نوع مه به کمک یک الگوریتم در مهمترین فرودگاه های کشور

نوع مقاله: مقاله پژوهشی

نویسنده

عضو هیات علمی پژوهشکده هواشناسی و علوم جو

چکیده

نظر به اهمیت کاهش دید افقی ناشی از وقوع مه برای عملیات فرود هواپیما در یک فرودگاه ، در این مطالعه کوشش شده است ضمن بررسی آماری باد، ارتفاع کف ابر و دید افقی کمتر از حداقل دید فرودگاه در موارد فرودگاه های مهم و پرترافیک کشور شامل مهرآباد تهران، مشهد، رشت، تبریز، اهواز، کرماشاه و بندر عباس، الگوی میانگین جوی شاخص های فشار تراز دریا، باد و مقدار ابر تعیین و  با استفاده از الگوریتم شناسایی مه، انواع مه ایجاد شده در فرودگاه های منتخب و در دوره ی 1995-2014 (20 سال) شناسایی شوند. مهمترین نتایج این بررسی نشان می دهد در ماه های سرد سال (دسامبر، ژانویه و فوریه) بیشترین رخداد مه در کل کشور گزارش شده است. غالب گزارش ها قبل از طلوع و بعد از غروب خورشید و به ترتیب در ساعت های 03، 00 و UTC21 دیدبانی شده اند. الگوی میانگین سمت وسرعت باد در ارتفاع 10 متری نشان دهنده ی هوای آرام در غرب کشور و بادهای غربی با سرعت میانگین 5 تا 10 نات در نواحی مرکزی و شرق کشور دیده می شوند که در حاشیه شمال شرق ایران گردش چرخندی کوچکی را نشان می دهند. بیشترین آمار رخداد مه در فرودگاه های اصفهان، کرمانشاه و رشت مربوط به مه تابشی نوع دوم ( وزش باد ملایم و ارتفاع کف ابر 300 پا یا کمتر) می باشد. مه تابشی نوع یک (وزش ملایم باد و آسمان بدون ابر) بیشترین هوای مه آلود را در فرودگاه های به خود اختصاص داده است. در فرودگاه های مهرآباد تهران و تبریز ضمن اینکه تعداد رخداد ها کمتر از سایر فرودگاه ها می باشد، بیشترین رخداد به مه ناشی از پایین آمدن ارتفاع کف ابر برمی گردد. این امر می تواند در اثر وقوع بارش جبهه ای ایجاد شود. مه فرارفتی تنها در فرودگاه مشهد قابل بررسی و توجه می باشد. به نظر میر سد گردش چرخندی در الگوهای میانگین باد موجب شده شکل گیری مه فرارفتی که نیازمند وزش بادهای حداقل 6 ناتی می باشد، در فرودگاه مشهد بیشتر از دیگر فرودگاه ها باشد که نیازمند بررسی های موردی سایر عوامل در این فرودگاه می باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Forecasting fog using some experimental methods (Tehran and Mashhad airports)

نویسنده [English]

  • sahar tajbakhsh
Assistant Professor, Atmospheric Survey Research Group
چکیده [English]

Introduction
Fog is among the most important weather hazards from the aviation perspective. This phenomenon can lead to horizontal visibility reduction. Therefore, accurate prediction is essential for flight safety and easing air traffic. Fog consists of a weather condition in which water drops and ice crystals reduce the horizontal visibility to less than 1000 meters. Various methods are suggested for fog forecasting. Numerical and statistical methods, experimental approaches, and very short range fog forecasting are some of the most common methods. Experimental methods are commonly used for first guess in forecasting centers. Saunders technique is one of the forecasting methods for radiation fogs using radio sounds data. Although this technique goes back to many years ago, it is being used in many parts of the world, including UK Met Office, and is recommended by World Meteorological Organization.
Material and method
Present study tries to evaluate the performance of two experimental methods using real data after studying synoptic condition of fog occurrence in two selected airports. The validity of them is measured then with the real occurrence in a number of case studies of fog occurrence for the selected airports using the bias technique in order to choose the more appropriate method. In the next step, the more appropriate method is administered using the numerical prediction model output and is again evaluated with the bias technique. In both these methods, an index called fog point temperature has been used, and the fog occurrence has been determined by calculating this temperature and comparing it with the minimum temperature. The selected airports are Mehrabad Airport, Tehran and Shahid Hasheminezhad Airport, Mashhad, which have been chosen because of high flight traffic (in Tehran) and high fog occurrence (in Mashhad). Experimental methods examined in this study are Saunders and Prichars-Crodack techniques, which 25 case studies in selected Airports tried to offer the best results for first guess of fog occurrence.  The accuracy of these relations was evaluated comparing real conditions using Bias technique. After choosing the more appropriate method, a similar process has been gone through using numerical prediction model of WRF for the next 12 hours.
Results and discussion
Results of synoptic evaluations show that high-pressure systems are a major factor in creating coldness in lower levels of the atmosphere. Evaluation of pressure field in this study doesn't show figures below 1020 hPa. Specific humidity values were 6-8 g/kg and 4-6 g/kg for 1000 and 925 hPa levels respectively. Winds are frequently northern or eastern and cold weather advection is seen in selected stations.
 In Saunders technique, using radio sound data of 1200 UTC in 25 case studies for mentioned airports, the fog point is calculated. This temperature is then compared with next day's minimum temperature and if the difference is less than -2°C, fog occurrence would be ruled out. Saunders considers this method mostly useful for radiation fog. In Crodack-Prichars technique, which is performed by creating a regression association between temperature and dew point temperature, the fog point temperature is determined. Here again, fog is not formed If the temperature difference is less than -2°C.
After calculating fog point temperature using Saunders technique and comparing it with actual observation, it was found that among 25 cases, 15 fog observations were consistent with Saunders technique calculations. In the five cases of fog nonoccurrence, the results of this method were consistent with reality. So, Bias evaluation technique shows 75% for probability of detection.
The same process has been gone through for Crodack-Prichars technique. In this method, a linear relationship exists between temperature, due point temperature, and fog point temperature. Wind condition and cloudiness are also presented experimentally in the form of a table. For different amounts of these two factors, a numerical amount of 1.5 to -1.5 is added to the fog point temperature. Fog occurrence is determined by calculating fog point temperature using Crodack-Prichars technique and comparing it with the minimum temperature according to table 2. This evaluation showed that in 13 of 20 fog occurrence cases, the right answer were obtained, and 5 cases of fog nonoccurrence, were consistent with reality.
Conclusion
Therefore, POD index was reduced to 65%. Based on the results, Saunders technique has been considered as the more appropriate method for initial guessing in fog forecasting in the airports under study. Now, the amounts of temperature and due point temperature were determined for the next 12 hours using WRF numerical prediction model, and Saunders technique was used again for predicting fog (using predicted data). The results of this evaluation were also investigated using Bias evaluation technique, which were not so agreeable, so that it was consistent with reality in 50% of cases. Hence, it seems that careful consideration of numerical prediction models output is needed.

کلیدواژه‌ها [English]

  • fog
  • Sanders Method
  • fog point
  • WRF numerical weather prediction

 

  1. Airport and Air Navigation Company, Iran Sky, 2015.
  2. Bang, C.-H. (2007) A numerical simulation study of fog and visibility in local airports over the Korean Peninsula using the WRF model, Master thesis, Yonsei University, Seoul, Korea, pp. 84.
  3. Costa ,Saulo B .and [other],2006,proceedings of 8 icshmo,foz do ,MACEIÓ, Brazil using artificial unreal network, lguacu,Brazil,April 24-28.
  4. Federal Aviation Administration (FAA), 2010, WEATHER-RELATED AVIATION ACCIDENT STUDY 2003–2007.
  5. Friedlein, M.T., 2004, Dense Fog Climatology, Chicago O’Hare International Airport, July
  6. 1996–April 2002, Bulletin of the American Meteorological Society, 85, PP. 515–517.
  7. ICAO, 2010: Technical specifications related to meteorological observations and reports: Appendix 3. Annex 3 to the Convention on nternational Civil Aviation: Meteorological Service for International Air Navigation, 17th ed. International Civil Aviation Organization, APP 3-1–APP 3-5.
  8. Iran airports and air navigation companies, 2017, Aeronautical Information publications(AIP),
  9. Jacobs, W., Vesa Nietosvaara, Andreas Bott, Jörg Bendix, Jan Cermak, Silas Chr. Michaelides, and Ismail Gultepe, 2007: COST Action 722, Earth System Science and Environmental Management, Final report on Short Range Forecasting Methods of Fog, Visibility and Low Clouds. Available from COST-722, European Science Foundation, 500 pp.
  10. Jahanbakhsh, S., Zahedi, M., Hosseini, A., 2003, Analysis of Temperature & Frost in Relation to Airport Climate, Researches in Geography, No. 50, PP. 19-33.
  11. Jahanbakhsh, S., Sari Sarraf, B., Hosseini, A., 2006, Evaluation of Flight Line xtension in Ardabil Airport by Analyzing the Wind Element, Research in Geography, No. 57, PP. 113-126.
  12. Khodabakhsh, H., 2004, Investigation of Synoptic Condition of Fog Occurrence in Shahid-Beheshti Airport of Isfahan, Applied Research, Group of Physical Geography, Isfahan Meteorology Office.
  13. Lee, J., W., Bang, Ch., H. and Hong, S., 2008, Predictability Experiments of Fog and Visibility in Local Airports over Korea using the WRF Model., Journal of Korean Society for Atmospheric Environment, Vol. 24, No. E 2 (2008) pp. 92~101.
  14. Salahi, B. and S., mohammadi, 2000, Synoptical and statistical analysis of fog in Ardebil airport., Journal of natural Geography Research , No. 77, pp 69-92.
  15. Stolaki, S. N., Kazadzis, S.A., Foris, D.V., Karacostas, T, S., 2009, Fog Characteristics at the Airport of Thessaloniki, Greece, Nat. Hazards Earth Syst. Sci., 9, PP. 1541–1549.
  16. 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.
  17. Van Schalkwyk L., and DYSON, L., L., 2013, Climatological Characteristics of Fog at Cape Town International Airport., J. Appl. Meteor. Climatol., vol28, pp 631-646.
  18. Willet, H. C., 1928: Fog and haze, their causes, distribution, and forecasting. Mon. Wea. Rev., 56, 435–468.
  19. WMO, No. 306,2014, Manual on Codes, International Codes, Volume I., Part A – Alphanumeric Codes 
  20. WMO/TD-No. 1292, 2005, Guidelines on integration severe weather warning into disaster risk management.
  21. WMO, No. 782,2008, Aerodrome Reports and Forecasts
  22. WMO, No. 407,2012, Manual on the observation of clouds and other meteors.