Itsapplicationtometeorologicaldatarevealsthespectralcomponentsondierent timescales(hours{da.pdf

Wavelet Analysis of Meteorological DataRudolf Rabenstein, Thorsten BartoschLehrstuhl fur Nachrichtentechnik, Cauerstrasse 7, D-91058 Erlangen, GermanyTel. *9131-857101, Fax *9131-303840, Email rabe@nt.e-technik.uni-erlangen.deThe Wavelet transformation is a modern signal analysis tool, which displays theinformation content of an observed signal simultaneously over time and frequency.Its application to meteorological data reveals the spectral components on dierenttime scales (hours { days { years). Wavelet analysis provides an intuitive accessto statistical properties of data series, that is not available with conventionalstochastic analysis methods.IntroductionThe investigation of the properties of meteorological data is important for the analysis andclassication of measured data, generation of synthetic data for simulation, and statisticaldescription of solar systems. Since the temporal variations of meteorological data are non-deterministic, they are usually described by the conventional tools for stochastic functions likeprobability density function, auto- and cross-correlation functions, power density spectrum andcorrelation coecients.An important feature of meteorological data that is not to be overlooked is that theycontain information on dierent time scales. The most obvious examples are the seasonal anddaily cycles with xed and well known periods. Depending on the location, also variations onthe scale of several days to several weeks occur. They do not possess sharply dened periodsand may be present only at certain times of the year. As an example, temperature variationson this scale are important for solar thermal systems with large storage masses. On the otherhand, uctuations in the cloud cover occur on a scale of minutes and hours and may existtoday but not tomorrow. They play a role in the description of photovoltaic systems.The conventional tools of stochastic analysis named above are not very well suited for thedescription of such non-stationar