Performance evaluation of the penalized maximal T and F algorithms in the quality control of monthly and daily climatic time series on the southwest coast of the Caspian Sea

Document Type : Original Article

Authors

1 MA student, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.

2 Associate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

3 Assistant Professor, Department of Geography, University of Guilan, Rasht, Iran

4 Associate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.

Abstract

Given the broad application of long-term meteorological data in various sciences and the need to predict their possible changes at local and global scales, it is very important to ensure the accuracy and homogeneity of such data. The penalized maximal T and F tests in the Rhtests software package were used to control the climatic parameters of the western half of the Caspian region on a daily and monthly scale. The data included precipitation, maximum and minimum temperature, wind speed, relative humidity, and sunshine hours, during 1979-2017 in Bandar Anzali, Rasht, and Ramsar stations. The missing data were estimated using the standards of the World Meteorological Organization and using the k-nearest neighbors algorithm. For the monthly time series, 23 change points were identified, which were homogenized in two stages, before and after reconstruction. The standard normal method was less sensitive than the penalized maximal F method by identifying 9 change points. Then, the daily data of the mentioned parameters were homogenized, which was homogenized by identifying a total of 32 points of change. However, it was not possible to thoroughly homogenize the sunshine hours due to the consecutive missing data. Data homogenization reversed the trend in 33% of cases. The studied method had acceptable results in homogenizing meteorological data in the study area based on the obtained results

Keywords


 
Aguilar, E., P. Llanso (2003). Guidelines on climate metadata and homogenization. WMO.
Alexandersson, H. (1986). A homogeneity test applied to precipitation data. International Journal of Climatology, 6: 661–675.
Cao, L.J., Z.W. Yan (2012). Progress in research on homogenization of climate data. Adv. Clim. Change Res., 3(2), doi: 10.3724/SP.J.1248.2012.00059.
Conrad, V., L.W. Pollak (1950). Methods in climatology (No. QC981 C714 1950).
Costa, A.C., A. Soares (2009). Homogenization of climate data: review and new perspectives using geostatistics. Math. Geosci. 41 (3): 291–305, from https://doi.org/10.1007/s11004-008-9203-3.
Gower, J.C. (1971). A general coefficient of similarity and some of its properties. Biometrics, pp. 857–871.
Helsel, D.R., R.M.  Hirsch (2002). Statistical methods in water resources (Vol. 323). Reston, VA: US Geological Survey.
https://www.climate.gov/maps-data/climate-data-primer/how-do-weather-observations-become-climate-dataKendall, M.G. (1938). A new measure of rank correlation: Biometrika 30: 81-93.
Kendall, M.G. (1975). Rank correlation methods. 4th ed. Charles Griffin, London, 202 p.
Kowarik.A., M. Templ (2016). Imputation with the R Package VIM. Journal of Statistical Software, Volume 74, Issue 7. doi: 10.18637/jss.v074.i07.
Mann, H.B. (1945). Non-parametric tests against trend. Econometrica, 13: 245-259.
Marcolini, G.; A. Bellin, and G. Chiogna. 2017. Performance of the Standard Normal Homogeneity Test for the homogenization of mean seasonal snow depth time series. International Journal of Climatology, 37: 1267-1277.
Peterson, T.C. (2013). Introduction to Quality Control, Nanjing Workshop, Nanjing University, China, 6 March.
Peterson, T.C., D.R. Easterling, T.R. Karl, P. Groisman, N. Nicholls, N. Plummer, S. Torok. I. Auer, R. BoehmD. Gullett, L. Vincent, R. Heino, H. Tuomenvirta, O. MestreT. Szentimrey J. Salinger, E.J. FørlandI.H. Bauer, H. Alexandersson, P. Jones, D. Parker (1998). Homogeneity adjustments of in situ atmospheric climate data: A review. Int. J. Climatol., 18: 1493-1517.
Rahimzadeh, F., M. Nassaji Zavareh (2014). Effects of adjustment for non-climatic discontinuities on determination of temperature trends and variability over Iran. Int. J. Climatol. 34: 2079–2096.
Ribeiro, S., J. Caineta, A.C. Costa (2015). Review and discussion of homogenization methods for climate data. Physics and Chemistry of the Earth, Parts A/B/C 94, 167-179.
Si. P., Ch. Luo, D. Liang (2018). Homogenization of Tianjin monthly near surface wind speed using RHtestsV4 for 1951–2014. Theor Appl Climatol, 132: 1303–1320.
Szentimrey, T., L. Hoffmann, M. Lakatos (2017). Abstract book. 9th Seminar for Homogenization and Quality Control in Climatological Databases and 4th Conference on Spatial Interpolation Techniques in Climatology and Meteorology. Hungarian Meteorological Service (OMSZ), Budapest. ISBN 978-963-7702-96-9
Tsinko, Y., A. Bakhshaii, E.A. Johnson, E.Y. Martin (2018). Comparisons of fire weather indices using Canadian raw and homogenized weather data. Agricultural and Forest Meteorology, 262: 110–119.
Venema, V.K.C., O. Mestre, E. Aguilar, I. Auer, J.A. Guijarro, P. Domonkos, et al., (2012). Benchmarking homogenization algorithms for monthly data. Climate Past, 8 (1): 89–115.
Vincent, L.A., X.L. Wang, E.J. Milewska, H. Wan, F. Yang, V. Swail (2012). A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. J. Geophys. Res.: Atmos. 117 (D18110). https://doi.org/10.1029/2012JD017859.
Wang, X.L. (2008a). Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or F test. J. Appl. Meteorol. Climatol. 47 (9): 2423–2444.
Wang, X.L. (2008b). Penalized maximal F-test for detecting undocumented mean-shifts without trend-change. J. Atmos. Oceanic Tech., 25 (No.3), 368-384. DOI:10.1175/2007/JTECHA982.1.
Wang, X.L., H. Chen, Y. Wu, Y. Feng, Q. Pu (2010). New techniques for the detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteorol. Climatol. 49(12): 2416–2436. https://doi.org/10.1175/2010JAMC2376.1.
Wang, X.L., Q.H. Wen, Y. Wu (2007). Penalized maximal t test for detecting undocumented mean change in climate data series. J. Appl. Meteor. Climatol., 46: 916–931, https://doi.org/10.1175/JAM2504.1
Wang, X.L., Y. Feng (2015). Overview of the RHtests_dlyPrcp software package for homogenization of daily precipitation. EMS 2015, Sofia, Bulgaria, 7-11.
WMO (2003). Global ozone research and monitoring project, in Scientific Assessment of Ozone Depletion: 2002, Rep. 47, Geneva.
WMO (2008). Guide to Meteorological Instruments and Methods of Observation, 7th Ed., WMO No. 8, World Meteorological Organization, Geneva, Switzerland.
WMO (2002). Scientific assessment of ozone depletion. Global Ozone Research and Monitoring Project. Report no. 47. World Meteorological Organization.
Yozgatligil, C., C. Yazici (2016). Comparison of homogeneity tests for temperature using a simulation study. Int. J. Climatol. 36: 62–81.