Serisel Korelasyonun Toplam Zenit Gecikmesi Zaman Serilerinde Parametrik Olmayan Trend Belirleme Üzerindeki Etkisi

Year 2019, Volume: 9 Issue: 1, 180 - 188, 15.01.2019

Cansu Beşel , Emine Tanır Kayıkçı

https://doi.org/10.17714/gumusfenbil.417853

Cited By: 2

Abstract

GNSS
gözlemlerinin analizinden elde edilen toplam zenit gecikmesi (ZTD) hava
şartlarının belirlenmesinde önemli bir parametre olması sebebiyle GNSS
tekniğinin iklimsel çalışmalarda da önemli bir yeri vardır. ZTD verilerinden
oluşturulan zaman serilerinin trend analizi ile, uzun dönemlerde veride meydana
gelen değişimler incelenebilmektedir. Bu çalışmada, Türkiye ve Avrupa’dan
seçilen 10 adet IGS (Uluslararası GNSS Servisi) istasyonu için 1995-2010
yılları arasındaki ZTD ürünlerinin COST Aksiyonu ES1206, GNSS4SWEC (Advanced Global Navigation Satellite
Systems Tropospheric Products for Monitoring Severe Weather Events and Climate)
kapsamında yeniden kestirimi ile elde edilmiş (IGS Repro1) verileri
kullanılarak oluşurulan zaman serilerindeki trend ve serisel korelasyon etkisi
belirlenmeye çalışılmıştır. Trend analizinde parametrik olmayan yöntemlerden
Mann-Kendall testi kullanılmıştır. Serisel korelasyonu belirlemek için
otokorelasyon katsayısı hesaplanmış olup seriyi korelasyondan arındırmak için
Trend-Free Prewhitening yöntemi uygulanmıştır. Korelasyondan arındırılan seriye
tekrar trend analizi yapılarak serisel korelasyonun trendin belirlenmesi üzerindeki
etkisi ve Trend-Free Prewhitening yönteminin performansı üzerine irdelemeler
yapılmaya çalışılmıştır. Çalışmada IGS istasyonlarındaki ZTD verilerinin
tümünde anlamlı serisel korelasyonlar elde edilmiştir. Aynı zamanda LAMA, ONSA
ve PENC istasyonlarında Trend-Free Prewhitening uygulandıktan sonra trend
olduğu sonucuna varılmıştır. Çalışma kapsamında uygulanan yöntemlerle ilgili
hesaplamalar MATLAB program kodları kullanılarak yapılmıştır.

Keywords

Mann-Kendall Testi, Serisel Korelasyon, Trend, Trend-Free Prewhitening, Toplam Zenit Gecikmesi

The Effect of Serial Correlation on Nonparametric Trend Determination at Zenith Total Delay Time Series

Abstract

GNSS
technique is important in climate studies because of obtaining Zenith Total
Delay which is significant parameter to determine weather conditions with
analysis of GNSS observations. Interchanges of long term data can be detected
via trend analysis produced by ZTD time series data. In this study, trend and
serial correlation effect of time series of (IGS Repro1) were investigated
using between 1995-2010 reprocessed ZTD data in the framework of COST Action
ES1206, GNSS4SWEC (Advanced Global Navigation Satellite Systems Tropospheric
Products for Monitoring Severe Weather Events and Climate). Mann-Kendall test
was performed for nonparametric method. 
The autocorrelation coefficient was calculated to set the serial
correlation. Trend-Free Prewhitening method has been applied to eliminate the
correlation. The effect on the trend determination of serial correlation and
the performance of Trend-Free Prewhitening method were tried to made
examination by making re-trend analysis in series which eliminated correlation.
As a result of the performed methods, serial correlation is substantial whole
ZTD data. At the same time, there are trends in LAMA, ONSA and PENC stations
after applying Trend-Free Prewhitening. In this work, MATLAB programming
language was handled in all of the tests.

Keywords

Mann-Kendall Test, Serial Correlation, Trend, Trend-Free Prewhitening, Zenith Total Delay

References

• Adib A., Kalaee M.M.K., Shoushtari M.M., Khalili K., 2017. Using of Gene Expression Programming and Climatic Data for Forecasting Flow Discharge by Considering Trend, Normality, and Stationarity Analysis, Arabian Journal of Geosciences, 10: 208, DOI 10.1007/s12517-017-2995-z.
• Akgül, I., 2003. Zaman Serilerinin Analizi ve ARIMA Modelleri, Der Yayınevi, İstanbul.
• Baldysz, Z., Nykiel, G., Figurski, M., Szafranek, K. ve Kroszczyński, K., 2015. Investigation of the 16-year and 18-year ZTD Time Series Derived from GPS Data Processing, Acta Geophysica, 1103-1125.
• Baldysz, Z., Nykiel, G., Araszkiewicz, A., Figurski, M., and Szafranek, K., 2016. Comparison of GPS Tropospheric Delays Derived from Two Consecutive EPN Reprocessing Campaigns from the Point of View of Climate Monitoring, Atmos. Meas.Tech. Discuss., doi:10.5194/amt-2016-5, 1.
• Beşel C., 2017. IGS İstasyonları Zenit Troposferik Gecikme Parametresi Zaman Serilerinde Trend ve Mevsimsel Etki Analizleri, Yüksek Lisans Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
• Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A. ve Ware, R.H., 1992. GPS Meteorology: Remote Sensing of Atmospheric Water Vapour Using the Global Positining System, Journal of Geophysical Research, 97, D14, 15787– 15801.
• Blain, G.C., 2012. Revisiting the Probabilistic Definition of Drought: Strengths, Limitations and an Agrometeorological Adaptation, Bragantia, 71, 1,132-141.
• Blain G.C.,2015. The Influence of Nonlinear Trends on the Power of the Trend-Free Pre-Whitening Approach, Acta Scientiarum. Agronomy,37, 1, 21-28, DOI 10.4025/actasciagron.v37i1.18199.
• COST, 2012. Memorandum of Understanding for the Implementation of a European Concerted Research Action, COST Action ES1206, Advanced Global Navigation Satellite Systems Tropospheric Products for Monitoring Severe Weather Events And Climate (GNSS4SWEC), European Cooperation in Science and Technology.
• Fleming, S.W ve Clarke, G.K.C., 2002. Autoregressive Noise, Deserialization, and Trend Detection and Quantification in Annual River Discharge Time Series, Canadian Water Resources Journal, 27, n. 3, p. 335-354.
• Guerova, G., 2013. Ground-Based GNSS Meteorology, Gfg Summer School, Potsdam, Almanya.
• Hamed, K.H. ve Rao, A.R.A., 1998. Modified Mann-Kendall Trend Test for Autocorrelated Data, J. Hydrol., 204,182–196.
• Kendall, M.G., 1975. Rank Correlation Methods, Charles Griffin, London.
• Kulkarni A. ve Von Storch H., 1995. Monte Carlo Experiments on the Effect of Serial Correlation on the Mann–Kendall Test of Trend, Meteorologische Zeitschrift, 4(2):82–85.
• Mann, H.B, 1945. Non-Parametric Tests Aganist Trend, The Econ. Society, 3, 245-259.
• Olofintoye, O., Adeyemo, J., ve Otieno, F., 2012. Impact of Regional Climate Change on Freshwater Resources and Operation of the Vanderkloof Dam System in South Africa, Global Warming-Impact and Future Perspective, doi: 10.5772/50414 165-184.
• Rocken C., Hove T.V., Johnson J., Solheim F., Ware R., Bevis M., Chiswell S. ve Businger S., 1994. GPS/STORM-GPS Sensing of Atmosferic Water Vapor for Meteorology, Journal of Atmospheric and Oceanic Technology, 12, 468-478.
• Jin, S., Park, J., Cho, J. ve Park, P., 2007. Seasonal Variability of GPS-Derived Zenith Tropospheric Delay (1994-2006) and Climate Implications, Journal of Geophysical Research, 112, D09110, doi:10.1029/2006JD007772.
• Tanır Kayıkçı E. ve Beşel C., 2017. Trend Analysis of The Zenith Tropospheric Delay Time Series, Proceedings, International Symposium on GIS Applications in Geography and Geosciences, 445-455.
• Von Storch H. ve Navarra A., 1995. Analysis of Climate Variability, Applications of Statistical Techniques, Springer, Berlin.
• Wang W., Chen Y., Becker S. and Liu B., 2015. Linear Trend Detection in Serially Dependent Hydrometeorological Data Based on a Variance Correction Spearman Rho Method, Water 7(12):7045-7065, DOI 10.3390/w7126673.
• URL-1: https://doi.org/10.14768/06337394-73a9-407c-9997-0e380dac5590. Aralık 2017.
• URL-2 https://tr.wikipedia.org/wiki/Otokorelasyon. 25 Ocak 2017.
• Yong, W., Binyun, Y., Debao, W. ve Yanping, L., 2008. Zenith Tropospheric Delay from GPS Monitoring Climate Change of Chinese Mainland, Education Technology and Training, 2008 and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008, International Workshop on, 1, doi: 10.1109/ETTandGRS.2008.43.
• Yue S. ve Wang C., 2002. Applicability of Prewhitening to Eliminate the Influence of Serial Correlation on the Mann–Kendall Test, Water Resour Res 38(6):41–47.
• Yue, S., Pilon, P. ve Phinney, B., 2003. Canadian Streamflow Trend Detection: Impacts of Serial and Cross-Correlation, Hydrogical, Sciences Journal, 48,1,51-64.