Bulutluluk Verisinin Çoklu Doğrusal Regresyon Modeli Kullanılarak Tahmin Edilmesi

Year 2023, , 33 - 41, 15.03.2023

Mine Tulin Zateroglu

https://doi.org/10.31466/kfbd.1150879

Cited By: 1

Abstract

Bu çalışma, iklim verileri ve hava kalite indisi gibi meteorolojik değişkenler kullanılarak bulutla kaplılık verisini tahmin etmektedir. Bu çalışmada kullanılan tüm meteorolojik parametrelerin günlük ortalama gözlem değerleri 1990-2015 dönemi için aylık ortalama verilere dönüştürülmüştür. Aylık ortalama bulutluluk değerleri, Kayseri'de kentsel alanda diğer iklim unsurları ve değer hava kalitesi indeksi kullanılarak tahmin edilmiştir. Bulutluluğu tahmin etmek için matematiksel ilişkileri belirlemek için Çoklu Doğrusal Regresyon modeli oluşturulmuştur. Meteorolojik parametrelerin bulutluluğu en fazla Mayıs ve Ekim aylarında, en az ise Eylül ve Ocak aylarında etkilediği gösterilmiştir. Ayrıca tahmin edilen modellere göre hava kalitesi indeks değeri Ocak, Temmuz, Ekim ve Kasım aylarındaki bulutluluk verileri üzerinde istatistiksel olarak anlamlı etkiye sahiptir.

Keywords

Bulutluluk, İklimsel Değişkenler, Hava Kalitesi İndis Değeri, Çoklu Doğrusal Regresyon Modeli

Estimation of Cloudiness Data Based on Multiple Linear Regression Model

Abstract

This study estimates cloudiness data using meteorological parameters which include climatic variables and air quality index. Daily average observed values of all meteorological parameters used in this study were transformed to monthly mean data for 1990-2015 period. The monthly mean values of cloudiness were estimated by using the other climatic elements and the value air quality index at urban area in Kayseri. Multiple Linear Regression model was built to determine the mathematical relationships for predicting cloudiness. It has been shown that meteorological parameters affect cloudiness the most in May and October, and the least in September and January. Additionally, according to the estimated models, air quality index value has effect on cloudiness data on January, July, October and November as statistically significant.

Keywords

Cloudiness, Climatic Variables, Air Quality Index Value, Multiple Linear Regression Model

References

• Badescu, V., Paulescu, M., and Brabec, M. (2016). Reconstruction of effective cloud geometry from series of sunshine number. Atmospheric Research, 176, 254-266.
• Changnon, S.A. (1981). Midwestern Cloud, Sunshine and Temperature Trends since 1901: Possible Evidence of Jet Contrail Effects. Journal of Applied Meteorology, 20: 496-508.
• Cuhadaroglu, B., and Demirci, E. (1997). Influence of some meteorological factors on air pollution in Trabzon city. Energy and Buildings, 25, 179-184.
• Dominick, D., Latif, M.T., Juahir, H. Aris, A.Z., and Zain S.M. (2012). An assessment of influence of meteorological factors on PM10 and NO2 at selected stations in Malaysia. Sustain. Environ. Res., 22(5), 305-315.
• Elnesr, M.K., and El-Sabban, A.F. (1964). Cloudiness and sunshine duration measurements in the U.A.R. Pure and Applied Geophysics PAGEOPH, 59(1), 256-260.
• Essa, K.S., and Etman, M.S. (2004). On the Relation Between Cloud Cover Amount and Sunshine Duration, Meteorology and Atmospheric Physics, 87, 235-240.
• Hoyt. D.V. (1977). Percent of possible sunshine and the total cloud cover. Monthly Weather Review, 105, 648-652.
• Kaiser, D.P. (1998). Analysis of total cloud amount over China, 1951-1994. Geophysical Research Letters, 25(19), 3599-3602.
• Kaiser, D.P. (2000). Decreasing cloudiness over China. An updated analysis examining additional variables. Geophysical Research Letters, 27(15), 2193-2196.
• Liepert, B. (1997). Recent changes in solar radiation under cloudy conditions. Int. J. Climatol. 17:1581–93. doi:10.1002/(ISSN)1097-0088.
• Liou, K.N., Ou, S.C., and Koenig G. (1990). An investigation of the climatic effect of contrail cirrus. In Air Traffic And the Environment: Background, Tendencies, and Potential Global Atmospheric Effects, ed. U. Schumann, 154–169. Berlin, Germany: Springer-Verlag.
• Mateos, D., Anton, M, Sanchez-Lorenzo, Calbo, J., and Wild, M. (2013). Long-term changes in the radiative effects of aerosols and clouds in a mid-latitude region (1985-2010). Global Planet Change, 111, 288-295, http://dx.doi.org/10.1016/j.gloplacha.2013.10.004.
• Matuszko, D. (2012). Influence of cloudiness on sunshine duration. International Journal of Climatology, 32(10), 1527– 1536.
• Montgomery, D.C., Peck, E.A., and Vining, G.G., (2001). Introduction to Linear Regression Analysis, 3rd Edition, John Wiley & Sons, New York.
• Neske, S. (2014). About the Relation between Sunshine Duration and Cloudiness on the Basis of Data from Hamburg, Journal of Solar Energy, ID:306871, 7 pages, http://dx.doi.org/10.1155/2014/306871.
• Norris, J.R., and Slingo, A. (2009). Trends in Observed Cloudiness and Earth’s Radiation Budget. From the Strüngmann Forum Report, Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation. Edited by Jost Heintzenberg and Robert J. Charlson. MIT Press, ISBN 978-0-262-01287-4.
• Robaa, S.M. (2008). Evaluation of Sunshine Duration From Cloud Data in Egypt. Energy, 33(5), 785-795.
• Twomey, S. (1974). Pollution and the planetary albedo. Atmos. Environ. 8:1251-56. doi:10.1016/0004-6981(74)90004-3.
• Twomey, S. (1977). The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci. 34:1149–52. doi:10.1175/1520-0469(1977)034.
• U.S. Environmental Protection Agency (1999). Guideline for reporting of daily air quality - air quality index (AQI). EPA-454/R-99-010. Office of Air Quality Planning and Standards, Research Triangle Park, NC 27711.
• Weber, G.R. (1994). On the Seasonal Variation of Local Relationships Between Temperature, Temperature Range, Sunshine and Cloudiness. Theoretical and Applied Climatology, 50(1-2), 15-22.
• Webster, F.B. (1969). A short investigation into the relationship between the duration of sunshine and total cloud amount. Meteorological Magazine, 98, 87-92.
• Zateroglu, M.T. (2021a). Evaluating the Sunshine Duration Characteristics in Association with Other Climate Variables. European Journal of Science and Technology, 29, 200-207, https://doi.org/10.31590/ejosat.1022639.
• Zateroglu, M.T. (2021b). Statistical Models For Sunshine Duration Related To Precipitation and Relative Humidity. European Journal of Science and Technology, 29, 208-213. https://doi.org/10.31590/ejosat.1022962
• Zateroglu, M.T. (2021c). Assessment of the effects of air pollution parameters on sunshine duration in six cities in Turkey. Fresenius Environmental Bulletin, 30(02A), 2251-2269.
• Zateroglu, M.T. (2021d). The Role of Climate Factors on Air Pollutants (PM10 and SO2). Fresenius Environmental Bulletin, 30(11), 12029-12036.
• Zateroglu, M.T. (2022). Modelling The Air Quality Index For Bolu, Turkey. Carpathian Journal of Earth and Environmental Sciences, 17(1), 119 – 130. https://doi.org/10.26471/cjees/2022/017/206