Editorial Type:
Article
Article Type:
Research Article

Reduced-Rank Sigma-Point Kalman Filter and Its Application in ENSO Model

K. K. Manoj Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada

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Youmin Tang Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada, and State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China

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Ziwang Deng Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, and Department of Statistics, York University, Toronto, Ontario, Canada

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Dake Chen State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China

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Yanjie Cheng National Climate Center, China Meteorological Administration, Beijing, China

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Print Publication:
01 Oct 2014
DOI:
https://doi.org/10.1175/JTECH-D-13-00172.1
Page(s):
2350–2366
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Abstract

The huge computational expense has been a main challenge while applying the sigma-point unscented Kalman filter (SPUKF) to a high-dimensional system. This study focuses on this issue and presents two methods to construct a reduced-rank sigma-point unscented Kalman filter (RRSPUKF). Both techniques employ the truncated singular value decomposition (TSVD) to factorize the covariance matrix and reduce its rank through truncation. The reduced-rank square root matrix is used to select the most important sigma points that can retain the main statistical features of the original sigma points. In the first technique, TSVD is applied on the covariance matrix constructed in the data space [RRSPUKF(D)], whereas in the second technique TSVD is applied on the covariance matrix constructed in the ensemble space [RRSPUKF(E)]. The two methods are applied to a realistic El Niño–Southern Oscillation (ENSO) prediction model [Lamont-Doherty Earth Observatory model, version 5 (LDEO5)] to assimilate the sea surface temperature (SST) anomalies. The results show that both the methods are more computationally efficient than the full-rank SPUKF, in spite of losing some estimation accuracy. When the truncation reaches a trade-off between cost expense and estimation accuracy, both methods are able to analyze the phase and intensity of all major ENSO events from 1971 to 2001 with comparable estimation accuracy. Furthermore, the RRSPUKF is compared against ensemble square root filter (EnSRF), showing that the overall analysis skill of RRSPUKF and EnSRF are comparable to each other, but the former is more robust than the latter.

Corresponding author address: Dr. Youmin Tang, Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, Prince George BC V2N 4Z9, Canada. E-mail: ytang@unbc.ca

Abstract

The huge computational expense has been a main challenge while applying the sigma-point unscented Kalman filter (SPUKF) to a high-dimensional system. This study focuses on this issue and presents two methods to construct a reduced-rank sigma-point unscented Kalman filter (RRSPUKF). Both techniques employ the truncated singular value decomposition (TSVD) to factorize the covariance matrix and reduce its rank through truncation. The reduced-rank square root matrix is used to select the most important sigma points that can retain the main statistical features of the original sigma points. In the first technique, TSVD is applied on the covariance matrix constructed in the data space [RRSPUKF(D)], whereas in the second technique TSVD is applied on the covariance matrix constructed in the ensemble space [RRSPUKF(E)]. The two methods are applied to a realistic El Niño–Southern Oscillation (ENSO) prediction model [Lamont-Doherty Earth Observatory model, version 5 (LDEO5)] to assimilate the sea surface temperature (SST) anomalies. The results show that both the methods are more computationally efficient than the full-rank SPUKF, in spite of losing some estimation accuracy. When the truncation reaches a trade-off between cost expense and estimation accuracy, both methods are able to analyze the phase and intensity of all major ENSO events from 1971 to 2001 with comparable estimation accuracy. Furthermore, the RRSPUKF is compared against ensemble square root filter (EnSRF), showing that the overall analysis skill of RRSPUKF and EnSRF are comparable to each other, but the former is more robust than the latter.

Corresponding author address: Dr. Youmin Tang, Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, Prince George BC V2N 4Z9, Canada. E-mail: ytang@unbc.ca
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