Abstract
This paper utilizes wavelets technique to calculate the Hurst exponent, the fractal dimensions and finally the climate predictability indices of daily average time series of air temperature, surface pressure, precipitation, relative humidity and wind speed for nine meteorological stations (Dhahran, Gizan, Jeddah, Yanbu, Abha, Hail, Guryat, Turaif and Riyadh) spread over different parts of Saudi Arabia. The meteorological data (daily means of temperature, pressure, relative humidity and wind speed and daily totals for precipitation) used in this study covers a period of 16 years starting from 1990 to 2005. The Hurst exponents, calculated using wavelet method, were used to find the fractal dimensions for each of the meteorological parameters. Finally, the predictability indices of temperature, pressure, precipitation and wind speed were used to establish the climate predictability indices. The climate predictability indices of precipitation and wind speed time series were found to be independent of the temperature and pressure. The predictability indices of individual parameters were found to have persistence behavior for entire data set while anti-persistence, in most of the cases, for winter and summer data sets.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
