Sabiha Gökçen Havalimanı için lojistik regresyon yöntemi ile sis analizi
Fog analysis with logistic regression for Sabiha Gökcenairport
Yazar: GUPSE YILMAZ
Danışman: PROF. DR. MİKDAT KADIOĞLU
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Meteoroloji Mühendisliği Ana Bilim Dalı / Atmosfer Bilimleri Bilim Dalı
Derece: Yüksek Lisans, 2019
Konu: Meteoroloji = Meteorology
Dizin: Havacılık meteorolojisi = Aeronautic meteorology ; Lojistik regresyon analizi = Logistic regression analysis

Özet:
Sis, başta ulaşım olmak üzere birçok alanda insan hayatını etkileyen en önemli hava olaylarından biridir. Ulaşımda, özellikle deniz ve hava trafiğinde aksamalara neden olan sis, hava ulaşımında daha kısa sürede daha çok insanı etkilediğinden dolayı daha büyük önem arz etmektedir. Bu sebeple, havaalanları için yapılan sis tahminlerinin önemi büyüktür.
Bu tez çalışmasında, Sabiha Gökçen Havalimanı'nda gözlemlenen ve uçuşların aksamasına sebep olan sisin daha iyi tahmin edilebilmesi için SPSS programı aracılığıyla istatistiksel bir yöntem geliştirerek, regresyon tahmin modeli oluşturulması amaçlanmıştır.
Çalışmada ECMWF'in 2010-2014 yıllarını kapsayan ERA-Interim tahmin verileri ile, Sabiha Gökçen Meydan Meteoroloji Müdürlüğü'nden alınan gözlem verileri (METAR rasatları) kullanılmıştır. ECMWF veritabanından elde edilen tahmin verileri 9 km çözünürlüklü olup İstanbul'un anadolu yakasını kapsayan toplam 36 gride ait verilerdir. 
Meteoroloji Genel Müdürlüğü Sabiha Gökçen Meydan Meteoroloji Müdürlüğü'nden alınan bilgilere göre bu havalimanında görülen sisin genellikle lokal radyasyon sisi ve adveksiyon sisi olduğu anlaşılmaktadır. Çalışmada kullanılan verilerin kapsadığı alan ve saatleri buna göre belirlenmiştir. Sabiha Gökçen Havalimanı'nda yapılan geçmiş yıllara ait METAR rasatları incelendiğinde, bu bölgede oluşan sislerin genellikle gece saatlerinde gözlemlendiği belirlenmiştir. Buna göre, ECMWF tahmin verileri her gün 21:00 GMT'den başlayarak 3'er saat arayla ertesi gün 06:00 GMT'ye denk gelecek şekilde 9 saatlik olarak alınmış, düzenlenmiş ve bu verilerden yola çıakrak sis oluşumunda etkisi olan birtakım parametreler hesaplanmıştır. Aynı saatlerdeki METAR rasatlarından faydalanarak, gerçekleşen sis bilgileri elde edilmiş, analize dahil edilen veri setine uyacak şekilde düzenlenmiştir.
Konu ile ilgili yapılan literatür araştırmasında, daha önceki benzer çalışmalarda sis analizi için sıcaklık, çiy noktası sıcaklığı, toplam bulut kapalılığı, deniz seviyesine indirgenmiş basınç, yer rüzgarı yön ve şiddeti parametrelerinin kullanıldığından, bu çalışmada daha başarılı bir sonuç elde etmek adına farklı parametreler de eklenmiştir. Yer sıcaklığı, çiy noktası sıcaklığı, denize indirgenmiş basınç, alçak bulut kapalılığı, yer rüzgar yönü ve hızı, 850 milibar seviyesi rüzgar yönü ve hızı, 850 milibar sıcaklığı parametrelerinin yanı sıra, sis tahmininde önemli yer tutan FSI (Fog Stability Index), bağıl nem yüzdesi (RH), enverziyon, sıcaklık ile çiy noktası sıcaklığı farkı değerleri hesaplanarak veri setine eklenmiştir. Tüm veriler öncelikle çoklu bağlantı ve korelasyon analizi yöntemleriyle kendi aralarında ve sis gözlem verileri ile ilişkileri saptanmıştır. Buna göre, analize dahil edilecek ve analizden çıkartılacak olan parametreler belirlenmiştir. Regresyon analizinde kullanılacak veriler normallik analizi ile test edilmiş ve normal dağılıma uymayan veriler uygun veri dönüştürme (transformasyon) yöntemleri ile istatistiksel analize uygun hale getirilmiştir. Mevcut veriler, SPSS programı kullanılarak ikili lojistik regresyon analizi ile yordanmış, en anlamlı ve başarılı sonuçlar elde edilene kadar çeşitli yöntemler ve farklı parametreler denenmiştir. Elde edilen sonuçlar adım adım yorumlanmıştır. Son olarak başarıyı arttırmak adına lojistik regresyonun ileriye ve geriye doğru metodları da denenmiş ve sonuç olarak bir regresyon denklemi elde edilmiştir.

Abstract:
Fog is a surface based cloud composed of either water droplets or ice crystals. Fog is the most frequent cause of surface visibility, and is one of the most common and persistent weather hazards encountered in aviation. The rapidity with which fog can form makes it especially hazardous. Since effects of fog are more intense for many people in less time, it has much more importance in aviation industry. Besides, the biggest fatal crash that has been recorded in aviation history was happened as a result of fog. For this reason, fog forecast and writing beneficial TAFs (Terminal Aerodrome Forecast) is very criticle and vital. 
The operational impacts of fog are usually very significant. Fog conditions often reduce aircraft arrival and departure flow rates and can become dense enough to close an airfield. In terms of disruption potential, fog has the ability to cause operational delays or to even prevent passengers from arriving at their destination. When fog is anticipated, crews will have to consider airport alternates and carry additional fuel due to the conditions generated by fog. When there is a rapid change in visibility in an airport due to advection fog and the visibility has become under the critical level, aircraft crew sometimes will have to decide to turn back to the airport where they had come from. These situations cause not only social problems but also financial problems for passengers, airline companies and airport authorities. For this reason, fog forecasting especially in airports when writing TAFs, is really important. 
In this study, many statistical methods have been used in order to create a regression prediction model that is expected to be helpful for fog prediction which is so hard in Sabiha Gokcen International Airport area. The statistical calculations have been made via SPSS program step by step and more than one method had been tried until the best prediction model was found. 
The first type of parameters used in this study is ERA-Interim forecast data which was downloaded from the database of ECMWF, by the help of Numerical Weather Prediction Department of Turkish State Meteorological Service, who had given the opportunity to get access to ECMWF database. The data had been chosen for 5 years, so that it would be enough to make a regression analysis and get reliable results. The years which has been chosen are from 2010 to 2014, including 2014. The dataset belongs to 36 grids, that are made up of 6 to 6 grid matrix. They have a resolution of 9 kilometers and that is the highest resolution in the studies that were made previously. So it is expected to be more successful than the other similar studies. In addition to the forecast data, observation data has also been got from Sabiha Gokcen International Airport Meteorology Office which is affiliated to Turkish State Meteorological Service. Those observation data is originally derived from the METAR observations, that are made every half an hour. The visibility and the types of fog observations were taken from those METARs and a new hourly observation dataset was created. This data has been converted to a binary categorized data, like "1" and "0", so that it was going to be easier and more meaningful to make a regression analysis. "1" stands for the existing fog, while "0" means that there is no fog. 
Many important informations about fog formation in Sabiha Gokcen Airport has been got from the forecasters and observers in Sabiha Gokcen Airport Meteorology Office. It is said that the common fog in Sabiha Gokcen Airport is usually seen at night and it is likely to dissipate when the sun rises and begins to heat the surface after 8 a.m. in the morning. It is understood that the fog observed around this place generally occurs above the lake in the north side of the airport and is brought to the airport by northern winds, so it is not only a radiation fog, but also an advection fog. The study area for this thesis was chosen by the help of those informations and it covers the airport and the topographical elements near in all the ways, as it involves the Black Sea and Omerli Lake in the north, Marmara Sea in the south, the bosphorus on the left and the hills around. As fog is seen at night, and regarding to the analysis of the time that fog occurs, the data was arranged to be starting from 21:00 GMT to the 06:00 GMT on the next day, which is defined as 30 GMT in this thesis. So the data time is handled as 21, 24, 27 and 30 GMT, which leads to 21, 24, 03 and 06 GMT. This arrangement has been done for both model forecast data and the observation data. 
As a study search for other studies about similar topics has been made, it is seen that many parameters like temperature, dew point temperature, wind components and wind speed, sea level pressure, total cloud cover and even precipitation had been used, but the success of the results were not so high. So it is decided to use different parameters in this study. Before the parameters are chosen and some of the parameters are eliminated, multicollinearity test was made in order to choose the best reliable parameters that would directly have impact on fog formation. The parameters which have been used are, low cloud cover, mean sea level pressure, temperature of 850 milibar, surface temperature and dew point difference, surface (10 meters) wind speed, surface wind speed direction which is transformed to categories as north and south and FSI (Fog Stability Index) and RH (relative humidity) which was calculated by the help of the other parameters. The parameters except these ones were eliminated and didn't have been used because of the multicollinearity problem. 
In the first step, binary logistic regression analysis is made with those parameters. Then other several parameters that could be reliable on fog formation are added step by step to the analysis. The process went on until the highest success score is obtained. The logistic regression analysis was tried to be improved with many ways, like transforming FSI data into a categorical parameter, but because of getting lower success scores, those methods could not be used. As a result of standart type binary logistic regression analysis, 68 hours of fog which had been formed were predicted correctly by the regression model. The success score of the correct prediction was %51,5 and the overall success score percentage is 98,7. 
After the theory of fog prediction with selected parameters has been made successfully, the backward stepwise type of logistic regression is apllied in order to get more successful predictive results. In this step, Wald method is selected and applied, because it is the most common and most successful method used in statistics. However, the results are the same with the results obtained from standart logistic regression analysis, this method is useful to create a logistic regression equation. Also, the variables which are selected by the regression model in the step 82nd, where the most successful analysis was made are revealed. 
As a result, it can be said that the binary logistic regression is quite successful in prediction of fog formation. But many other  improvements can be made in order to get higher scores in success, like using data from other forecast models that has higher resolution and studying with a larger data set.