Abstract
Along with the computation of attractor dimension via the Grassberger-Procaccia method and the nearest neighbour algorithm, a variety of phase space tests are used to search for low-dimensional characterization of daily maximum and minimum atmospheric temperature data (ca 25 000 points each, spanning about a 70-year period). These tests include global and local singular value decompositions, as well as others for uncovering nonlinear correlations among amplitudes of the global singular vectors and for recognizing determinism in a time series. The results show that a low-dimensional characterization of the temperature data is unlikely.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1715-1750 |
| Number of pages | 36 |
| Journal | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |
| Volume | 354 |
| Issue number | 1713 |
| State | Published - 1996 |
Fingerprint
ASJC Scopus subject areas
- Engineering(all)
Cite this
The search for a low-dimensional characterization of a local climate system. / Sahay, A.; Sreenivasan, K. R.
In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 354, No. 1713, 1996, p. 1715-1750.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The search for a low-dimensional characterization of a local climate system
AU - Sahay, A.
AU - Sreenivasan, K. R.
PY - 1996
Y1 - 1996
N2 - Along with the computation of attractor dimension via the Grassberger-Procaccia method and the nearest neighbour algorithm, a variety of phase space tests are used to search for low-dimensional characterization of daily maximum and minimum atmospheric temperature data (ca 25 000 points each, spanning about a 70-year period). These tests include global and local singular value decompositions, as well as others for uncovering nonlinear correlations among amplitudes of the global singular vectors and for recognizing determinism in a time series. The results show that a low-dimensional characterization of the temperature data is unlikely.
AB - Along with the computation of attractor dimension via the Grassberger-Procaccia method and the nearest neighbour algorithm, a variety of phase space tests are used to search for low-dimensional characterization of daily maximum and minimum atmospheric temperature data (ca 25 000 points each, spanning about a 70-year period). These tests include global and local singular value decompositions, as well as others for uncovering nonlinear correlations among amplitudes of the global singular vectors and for recognizing determinism in a time series. The results show that a low-dimensional characterization of the temperature data is unlikely.
UR - http://www.scopus.com/inward/record.url?scp=0029808754&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029808754&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0029808754
VL - 354
SP - 1715
EP - 1750
JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
SN - 0962-8428
IS - 1713
ER -