Bibliographic Citation
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| DOI | http://dx.doi.org/10.1175/1520-0442(1995)008<0401:DOFCSP>2.0.CO;2 |
| Title | Detection of forced climate signals. Part I: Filter theory |
| Creator/Author | North, G.R. [Texas A&M Univ., College Station, TX (United States)] ; Kim, K.Y. [Applied Research Corp., College Station, TX (United States)] ; Shen, S.S.P. [Univ. of Alberta, Edmonton (Canada)] [and others] |
| Publication Date | 1995 Mar 01 |
| OSTI Identifier | OSTI ID: 86324 |
| DOE Contract Number | FG05-91ER61221 |
| Other Number(s) | Journal ID: JLCLEL; ISSN 0894-8755; TRN: TRN: 95:003885-0001 |
| Resource Type | Journal Article |
| Resource Relation | Journal Name: Journal of Climate; Journal Volume: 8; Journal Issue: 3; Other Information: PBD: Mar 1995 |
| Subject | 54 ENVIRONMENTAL SCIENCES ;99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; SIGNAL-TO-NOISE RATIO; FLUCTUATIONS; CALCULATION METHODS; CLIMATE MODELS; FORCING FUNCTIONS; CLIMATIC CHANGE; DATA ANALYSIS; AMPLITUDES; EIGENFUNCTIONS; CLIMATES; AMBIENT TEMPERATURE |
| Description/Abstract | This paper considers the construction of a linear smoothing filter for estimation of the forced part of a change in a climatological field such as the surface temperature. The filter is optimal in the sense that it suppresses the natural variability or {open_quotes}noise{close_quotes} relative to the forced part or {open_quotes}signal{close_quotes} to the maximum extent possible. The technique is adapted from standard signal processing theory. The present treatment takes into account the spatial as well as the temporal variability of both the signal and the noise. In this paper we take the signal`s waveform in space-time to be a given deterministic field in space and time. Formulation of the expression for the minimum mean-squared error for the problem together with a no-bias constraint leads to an integral equation whose solution is the filter. The problem can be solved analytically in terms of the space-time empirical orthogonal function basis set and its eigenvalue spectrum for the natural fluctuations and the projection amplitudes of the signal onto these eigenfunctions. The optimal filter does not depend on the strength of the assumed waveform used in its construction. A lesser mean-square error in estimating the signal occurs when the space-time spectral characteristics of the signal and the noise are highly dissimilar; for example, if the signal is concentrated in a very narrow spectral band and the noise in a very broad band. A few pedagogical exercises suggest that these techniques might be useful in practical situations. 28 refs. |
| Country of Publication | United States |
| Language | English |
| Format | Medium: X; Size: pp. 401-408 |
| System Entry Date | 2008 Feb 04 |
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