Separating Different Scales of Motion in Time Series of Meteorological Variables

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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.

*National Climatic Data Center, Asheville, North Carolina.

+New York State Department of Environmental Conservation, Albany, New York.

#University of Idaho, Idaho Falls, Idaho.

&State University of New York at Albany, Albany, New York.

Corresponding author address: Dr. Robert E. Eskridge, National Climatic Data Center, 151 Patton Ave., Asheville, NC 28801-5001. E-mail: beskridg@ncdc.noaa.gov

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.

*National Climatic Data Center, Asheville, North Carolina.

+New York State Department of Environmental Conservation, Albany, New York.

#University of Idaho, Idaho Falls, Idaho.

&State University of New York at Albany, Albany, New York.

Corresponding author address: Dr. Robert E. Eskridge, National Climatic Data Center, 151 Patton Ave., Asheville, NC 28801-5001. E-mail: beskridg@ncdc.noaa.gov
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