Intraseasonal oscillation indices from complex EOFs
- Indices of oscillatory behavior are conveniently obtained by projecting the fields in question into a phase space of a few (mostly just two) dimensions; empirical orthogonal functions (EOFs) or other, more dynamical, modes are typically used for the projection. If sufficiently coherent and in quadrature, the projected variables simply describe a rotating vector in the phase space, which then serves as the basis for predictions. Using the boreal summer intraseasonal oscillation (BSISO) as a test case, an alternative procedure is introduced: it augments the original fields with their Hilbert transform (HT) to form a complex series and projects it onto its (single) dominant EOF. The real and imaginary parts of the corresponding complex pattern and index are compared with those of the original (real) EOF. The new index explains slightly less variance of the physical fields than the original, but it is much more coherent, partly from its use of future information by the HT. Because the latter is in the way of real-time monitoring, theIndices of oscillatory behavior are conveniently obtained by projecting the fields in question into a phase space of a few (mostly just two) dimensions; empirical orthogonal functions (EOFs) or other, more dynamical, modes are typically used for the projection. If sufficiently coherent and in quadrature, the projected variables simply describe a rotating vector in the phase space, which then serves as the basis for predictions. Using the boreal summer intraseasonal oscillation (BSISO) as a test case, an alternative procedure is introduced: it augments the original fields with their Hilbert transform (HT) to form a complex series and projects it onto its (single) dominant EOF. The real and imaginary parts of the corresponding complex pattern and index are compared with those of the original (real) EOF. The new index explains slightly less variance of the physical fields than the original, but it is much more coherent, partly from its use of future information by the HT. Because the latter is in the way of real-time monitoring, the index can only be used in cases with predicted physical fields, for which it promises to be superior. By developing a causal approximation of the HT, a real-time variant of the index is obtained whose coherency is comparable to the noncausal version, but with smaller explained variance of the physical fields. In test cases the new index compares well to other indices of BSISO. The potential for using both indices as an alternative is discussed.…
Author details: | Gerd BürgerORCiDGND |
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DOI: | https://doi.org/10.1175/JCLI-D-20-0427.1 |
ISSN: | 0894-8755 |
ISSN: | 1520-0442 |
Title of parent work (English): | Journal of climate |
Publisher: | American Meteorological Soc. |
Place of publishing: | Boston |
Publication type: | Article |
Language: | English |
Date of first publication: | 2021/01/01 |
Publication year: | 2021 |
Release date: | 2022/11/17 |
Tag: | Empirical orthogonal functions; Filtering techniques; Forecasting techniques; Madden-Julian oscillation; Oscillations; Statistical techniques |
Volume: | 34 |
Issue: | 1 |
Number of pages: | 16 |
First page: | 107 |
Last Page: | 122 |
Funding institution: | German Research Foundation (DFG SHIVA)German Research Foundation (DFG) [265653116]; SaWaM project |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer review: | Referiert |