Separating Different Scales of Motion in Time Series of Meteorological Variables

The removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effe...

Full description

Saved in:
Bibliographic Details
Published in:Bulletin of the American Meteorological Society 1997-07, Vol.78 (7), p.1473-1483
Main Authors: Eskridge, Robert E., Ku, Jia Yeong, Rao, S. Trivikrama, Porter, P. Steven, Zurbenko, Igor G.
Format: Article
Language:English
Subjects:
Autocorrelation
Climate
Climate change
Climate models
Datasets
Earth, ocean, space
Exact sciences and technology
External geophysics
Geophysics. Techniques, methods, instrumentation and models
Meteorology
Pests
Radiosondes
Statistical variance
Time series
Transfer functions
Wavelet analysis
Weather
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effectiveness compared. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov–Zurbenko (KZ) filter. It is shown that PEST and anomalies do not cleanly separate the synoptic and seasonal signals from the data as well as the other two methods. The KZ filter method is shown to have the same level of accuracy as the wavelet transform method. However, the KZ filter method can be applied to datasets with missing observations and is much easier to use than the wavelet transform method.
ISSN:0003-0007
1520-0477
DOI:10.1175/1520-0477(1997)078<1473:sdsomi>2.0.co;2