Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data

Atmospheric Research

Volumn 155, Issue , 2015, Pages 52-72

  Araghi, A ^a   Mousavi Baygi, M ^a   Adamowski, J ^b   Malard, J ^b   Nalley, D ^b   Hasheminia, S M ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

Discrete wavelet transform; Gridded data; Iran; Mann Kendall; Temperature; Trend

Indexed keywords

ATMOSPHERIC TEMPERATURE; DISCRETE WAVELET TRANSFORMS; SURFACE PROPERTIES; TEMPERATURE;

DIFFERENT TIME SCALE; DOMINANT TIME SCALE; GRIDDED DATA; IRAN; MANN-KENDALL; MONTHLY TIME SCALE; SURFACE TEMPERATURES; TREND;

TIME MEASUREMENT;

PERIODICITY; REGIONAL CLIMATE; SURFACE TEMPERATURE; TEMPERATURE PROFILE; TIME SERIES ANALYSIS; TIMESCALE; WAVELET ANALYSIS;

IRAN;

EID: 84918530339     PISSN: 01698095     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosres.2014.11.016     Document Type: Article

References (73)

1. Adamowski J.F. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J. Hydrol. 2008, 353:247-266.
2. Adamowski J., Chan H.F. A wavelet neural network conjunction model for groundwater level forecasting. J. Hydrol. 2011, 407:28-40.
3. Adamowski J., Prokoph A. Assessing the impacts of the urban heat island effect on streamflow patterns in Ottawa, Canada. J. Hydrol. 2013, 496:225-237.
4. Adamowski J., Sun K. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J. Hydrol. 2010, 390:85-91.
5. Adamowski J., Adamowski K., Bougadis J. Influence of trend on short duration design storms. Water Res. Manage. 2010, 24:401-413.
6. Adamowski J., Chan H., Prasher S., Sharda V.N. Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J. Hydroinf. 2012, 3:731-744.
7. Adamowski J., Adamowski K., Prokoph A. Quantifying the spatial temporal variability of annual streamflow and meteorological changes in eastern Ontario and southwestern Quebec using wavelet analysis and GIS. J. Hydrol. 2013, 499:27-40.
8. Adamowski J., Adamowski K., Prokoph A. A spectral analysis based methodology to detect climatological influences on daily urban water demand. Math. Geosci. 2013, 45:49-68.
9. Ashraf B., Yazdani R., Mousavi-Baygi M., Bannayan M. Investigation of temporal and spatial climate variability and aridity of Iran. Theor. Appl. Climatol. 2013, 1-12.
10. Barry R.G., Chorley R.J. Atmosphere, Weather and Climate 2009, Routledge, New York, (536 pp). 9th ed.
11. Belayneh A., Adamowski J., Khalil B., Ozga-Zielinski B. Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet-support vector regression models. J. Hydrol. 2014, 508:418-429.
12. Boccolari M., Malmusi S. Changes in temperature and precipitation extremes observed in Modena, Italy. Atmos. Res. 2013, 122:16-31.
13. Box G.E.P., Jenkins G.M., Reinsel G.C. Time Series Analysis, Forecasting and Control 2008, John Wiley & Sons, NJ, (746 pp). 4th ed.
14. Campisi-Pinto S., Adamowski J., Oron G. Forecasting urban water demand via wavelet-denoising and neural network models. Case study: city of Syracuse, Italy. Water Resour. Manag. 2012, 26:3539-3558.
15. Chellali F., Khellaf A., Belouchrani A. Wavelet spectral analysis of the temperature and wind speed data at Adrar, Algeria. Renew. Energy 2010, 35:1214-1219.
16. de Artigas M.Z., Elias A.G., de Campra P.F. Discrete wavelet analysis to assess long-term trends in geomagnetic activity. Phys. Chem. Earth 2006, 31:77-80.
17. De Lima M.I.P., Santo F.E., Ramos A.M., De Lima J.L.M.P. Recent changes in daily precipitation and surface air temperature extremes in mainland Portugal, in the period 1941-2007. Atmos. Res. 2013, 127:195-209.
18. del Río S., Herrero L., Pinto-Gomes C., Penas A. Spatial analysis of mean temperature trends in Spain over the period 1961-2006. Glob. Planet. Chang. 2011, 78:65-75.
19. Diodato N., Bellocchi G., Bertolin C., Camuffo D. Climate variability analysis of winter temperatures in the central Mediterranean since 1500AD. Theor. Appl. Climatol. 2013, 1-8.
20. Fatichi S., Barbosa S.M., Caporali E., Silva M.E. Deterministic versus stochastic trends: detection and challenges. J. Geophys. Res. Atmos. 2009, 114:D18121.
21. Franzke C. Long-range dependence and climate noise characteristics of Antarctic temperature data. J. Clim. 2010, 23:6074-6081.
22. Grover V.I. Impact of Climate Change on Water and Health 2013, CRC Press, NY, (428 pp).
23. Hamed K.H., Ramachandra Rao A. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 1998, 204:182-196.
24. Hernández E., Weiss G. A First Course on Wavelets 1996, CRC Press, USA, (489 pp).
25. Hirsch R.M., Slack J.R. A nonparametric trend test for seasonal data with serial dependence. Water Resour. Res. 1984, 20:727-732.
26. Hirsch R.M., Slack J.R., Smith R.A. Techniques of trend analysis for monthly water quality data. Water Resour. Res. 1982, 18:107-121.
27. Hsu K.-C., Li S.-T. Clustering spatial-temporal precipitation data using wavelet transform and self-organizing map neural network. Adv. Water Resour. 2010, 33:190-200.
28. Karran D., Morin E., Adamowski J. Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes. J. Hydroinf. 2014, 16:671-689.
29. Kirchgässner G., Wolters J., Hassler U. Introduction to Modern Time Series Analysis 2013, Springer, Berlin, (331 pp). 2nd ed.
30. Lackmann G. Midlatitude Synoptic Meteorology 2012, American Meteorological Society, USA, (348 pp).
31. Lauritzen P., Jablonowski C., Taylor M.A., Nair R.D. Numerical Techniques for Global Atmospheric Models 2011, Springer, NY, (577 pp).
32. Lutgens F.K., Tarbuck E.J. The Atmosphere, An Introduction to Meteorology 2013, Prentice Hall, USA, (528 pp). 12th ed.
33. Makokha G.L., Shisanya C.A. Trends in mean annual minimum and maximum near surface temperature in Nairobi City, Kenya. Adv. Meteorol. 2010, 2010.
34. Marofi S., Soleymani S., Salarijazi M., Marofi H. Watershed-wide trend analysis of temperature characteristics in Karun-Dez watershed, southwestern Iran. Theor. Appl. Climatol. 2012, 110:311-320.
35. Martinez C.J., Maleski J.J., Miller M.F. Trends in precipitation and temperature in Florida, USA. J. Hydrol. 2012, 452-453:259-281.
36. Matsuura K., Willmott C.J. Terrestrial Air Temperature: 1900-2010 Gridded Monthly Time Series 2012.
37. McGuffie K., Henderson-Sellers A. A Climate Modelling Primer 2005, John Wiley and Sons, (296 pp). 3rd ed.
38. Misiti M., Misiti Y., Oppenheim G., Poggi J.-M. Wavelet Toolbox, User's Guide 2013, MathWorks Inc, USA, (593 pp).
39. Nalley D., Adamowski J., Khalil B. Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954-2008). J. Hydrol. 2012, 475:204-228.
40. Nalley D., Adamowski J., Khalil B., Ozga-Zielinski B. Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967-2006 using the discrete wavelet transform. Atmos. Res. 2013, 132-133:375-398.
41. Nicolay S., Mabille G., Fettweis X., Erpicum M. 30 and 43months period cycles found in air temperature time series using the Morlet wavelet method. Clim. Dyn. 2009, 33:1117-1129.
42. Nievergelt Y. Wavelets Made Easy 2001, Birkhäuser, (297 pp).
43. Nourani V., Baghanam A.H., Adamowski J., Gebremichael M. Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. J. Hydrol. 2013, 476:228-243.
44. Nourani V., Baghanam A., Adamowski J., Kisi O. Applications of hybrid wavelet-artificial intelligence models in hydrology: A review. J. Hydrol. 2014, 514:358-377.
45. Olkkonen H. Discrete Wavelet Transform - Biomedical Application 2011, InTech, Croatia, (366 pp).
46. Özger M., Mishra A.K., Singh V.P. Scaling characteristics of precipitation data in conjunction with wavelet analysis. J. Hydrol. 2010, 395:279-288.
47. Partal T., Kahya E. Trend analysis in Turkish precipitation data. Hydrol. Process. 2006, 20:2011-2026.
48. Partal T., Küçük M. Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey). Phys. Chem. Earth 2006, 31:1189-1200.
49. Percival D.B., Walden A.T. Wavelet Methods for Time Series Analysis 2000, Cambridge University Press, New York, (594 pp).
50. Pingale S., Khare D., Jat M., Adamowski J. Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centres of the arid and semi-arid state of Rajasthan. India. J. Atmos. Res. 2014, 138:73-90.
51. Pišoft P., Kalvová J., Brázdil R. Cycles and trends in the Czech temperature series using wavelet transforms. Int. J. Climatol. 2004, 24:1661-1670.
52. Prokoph A., Adamowski J., Adamowski K. Influence of the 11year solar cycle on annual streamflow maxima in Southern Canada. J. Hydrol. 2012, 442-443:55-62.
53. Rao R.M., Bopardikar A.S. Wavelet Transforms, Introduction to Theory and Applications 1998, Addison-Wesley, (310 pp).
54. Rybski D., Bunde A., Havlin S., von Storch H. Long-term persistence in climate and the detection problem. Geophys. Res. Lett. 2006, 33:L06718.
55. Saboohi R., Soltani S., Khodagholi M. Trend analysis of temperature parameters in Iran. Theor. Appl. Climatol. 2012, 109:529-547.
56. Sang Y.-F. A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resour. Manag. 2012, 26:3345-3365.
57. Sang Y.-F. Improved wavelet modeling framework for hydrologic time series forecasting. Water Resour. Manag. 2013, 27:2807-2821.
58. Shiri J., Kisi O. Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J. Hydrol. 2010, 394:486-493.
59. Sneyers R. On the Statistical Analysis of Series of Observations 1990, Secretariat of the World Meteorological Organization, (192 pp).
60. Sonali P., Nagesh Kumar D. Review of trend detection methods and their application to detect temperature changes in India. J. Hydrol. 2013, 476:212-227.
61. Souza Echer M.P., et al. On the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activity. J. Atmos. Sol. Terr. Phys. 2012, 74:87-93.
62. Su H., Liu Q., Li J. Alleviating border effects in wavelet transforms for nonlinear time-varying signal analysis. Adv. Electr. Comput. Eng. 2011, 11:55-60.
63. Sun X., Lin Z., Cheng X., Jiang C. Regional features of the temperature trend in China based on Empirical Mode Decomposition. J. Geogr. Sci. 2008, 18:166-176.
64. Tabari H., Hosseinzadeh Talaee P. Analysis of trends in temperature data in arid and semi-arid regions of Iran. Glob. Planet. Chang. 2011, 79:1-10.
65. Tiwari M., Adamowski J. Urban water demand forecasting and uncertainty assessment using ensemble wavelet- bootstrap-neural network models. Water Resour. Res. 2013, 49:6486-6507.
66. Tiwari M., Adamowski J. Medium-term urban water demand forecasting with limited data using an ensemble wavelet-bootstrap machine-learning approach. J. Water Resour. Plann. Manage. 2014, 10.1061/(ASCE)WR.1943-5452.0000454.
67. Torrence C., Compo G.P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 1998, 79:61-78.
68. Trenberth K., et al. Observations: surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007, 235-336.
69. Venkata Ramana R., Krishna B., Kumar S.R., Pandey N.G. Monthly rainfall prediction using wavelet neural network analysis. Water Resour. Manag. 2013, 27:3697-3711.
70. Vonesch C., Blu T., Unser M. Generalized Daubechies wavelet families. Sig. Process IEEE Trans. 2007, 55:4415-4429.
71. Wang H., et al. Hydro-climatic trends in the last 50years in the lower reach of the Shiyang River Basin, NW China. Catena 2012, 95:33-41.
72. Wilks D.S. Statistical Methods in the Atmospheric Science 2011, Academic Press, USA, (704 pp). 3rd ed.
73. Yue S., Pilon P., Phinney B., Cavadias G. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process. 2002, 16:1807-1829.