Statistical hypothesis testing in wavelet analysis: theoretical developments and applications to Indian rainfall

Schulte, Justin A.

doi:https://doi.org/10.5194/npg-26-91-2019

Articles | Volume 26, issue 2

https://doi.org/10.5194/npg-26-91-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-26-91-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 2

Research article

|

14 Jun 2019

Research article |

| 14 Jun 2019

Statistical hypothesis testing in wavelet analysis: theoretical developments and applications to Indian rainfall

Justin A. Schulte

Supplement

https://doi.org/10.5194/npg-26-91-2019-supplement

Data sets

Meteorological Data Sets for Downloading Indian Institute of Tropical Meteorology https://www.tropmet.res.in/Data Archival-51-Page

Climate Timeseries NOAA/OAR/ESRL PSD https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/

Short summary

Statistical hypothesis tests in wavelet analysis are used to asses the likelihood that time series features are noise. The choice of test will determine which features emerge as a signal. Tests based on area do poorly at distinguishing abrupt fluctuations from periodic behavior, unlike tests based on arclength that do better. The application of the tests suggests that there are features in Indian rainfall time series that emerge from background noise.