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- Author or Editor: Joaquim Ballabrera-Poy x
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Abstract
A reduced-order Kalman filter is used to assimilate observed fields of the surface wind stress, sea surface temperature, and sea level into the coupled ocean–atmosphere model of Zebiak and Cane. The method projects the Kalman filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high-dimensional systems.
The Zebiak and Cane model couples a linear, reduced-gravity ocean model with a single, vertical-mode atmospheric model. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the empirical orthogonal functions (EOFs) of the model to represent the simultaneous value of the wind stress, SST, and sea level, when the fields are limited to the latitude band 10°S–10°N, and when the number of EOFs is greater than the number of statistically significant modes.
In this first application of the Kalman filter to a coupled ocean–atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes.
The ability of the method to reconstruct the state of the equatorial Pacific and to predict its time evolution is shown. The method is quite robust for predictions up to 6 months, and able to predict the onset of the 1997 warm event 15 months before its occurrence.
Abstract
A reduced-order Kalman filter is used to assimilate observed fields of the surface wind stress, sea surface temperature, and sea level into the coupled ocean–atmosphere model of Zebiak and Cane. The method projects the Kalman filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high-dimensional systems.
The Zebiak and Cane model couples a linear, reduced-gravity ocean model with a single, vertical-mode atmospheric model. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the empirical orthogonal functions (EOFs) of the model to represent the simultaneous value of the wind stress, SST, and sea level, when the fields are limited to the latitude band 10°S–10°N, and when the number of EOFs is greater than the number of statistically significant modes.
In this first application of the Kalman filter to a coupled ocean–atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes.
The ability of the method to reconstruct the state of the equatorial Pacific and to predict its time evolution is shown. The method is quite robust for predictions up to 6 months, and able to predict the onset of the 1997 warm event 15 months before its occurrence.
Abstract
In this paper, a series of observing system simulation experiments (OSSEs) are used to study the design of a proposed array of instrumented moorings in the Indian Ocean (IO) outlined by the IO panel of the Climate Variability and Predictability (CLIVAR) Project. Fields of the Ocean Topography Experiment (TOPEX)/Poseidon (T/P) and Jason sea surface height (SSH) and sea surface temperature (SST) are subsampled to simulate dynamic height and SST data from the proposed array. Two different reduced-order versions of the Kalman filter are used to reconstruct the original fields from the simulated observations with the objective of determining the optimal deployment of moored platforms and to address the issue of redundancy and array simplification. The experiments indicate that, in terms of the reconstruction of SSH and SST, the location of the subjectively proposed array compareS favorably with the optimally defined one. The only significant difference between the proposed IO array and the optimal array is the lack of justification for increasing the latitudinal resolution near the equator (i.e., moorings 1.5°S and 1.5°N). An analysis of the redundancy also identifies the equatorial region as the one with the largest amount of redundant information. Thus, in the context of these fields, these results may help define the prioritization of its deployment or redefine the array to extend its latitudinal extent while maintaining the same amount of stations.
Abstract
In this paper, a series of observing system simulation experiments (OSSEs) are used to study the design of a proposed array of instrumented moorings in the Indian Ocean (IO) outlined by the IO panel of the Climate Variability and Predictability (CLIVAR) Project. Fields of the Ocean Topography Experiment (TOPEX)/Poseidon (T/P) and Jason sea surface height (SSH) and sea surface temperature (SST) are subsampled to simulate dynamic height and SST data from the proposed array. Two different reduced-order versions of the Kalman filter are used to reconstruct the original fields from the simulated observations with the objective of determining the optimal deployment of moored platforms and to address the issue of redundancy and array simplification. The experiments indicate that, in terms of the reconstruction of SSH and SST, the location of the subjectively proposed array compareS favorably with the optimally defined one. The only significant difference between the proposed IO array and the optimal array is the lack of justification for increasing the latitudinal resolution near the equator (i.e., moorings 1.5°S and 1.5°N). An analysis of the redundancy also identifies the equatorial region as the one with the largest amount of redundant information. Thus, in the context of these fields, these results may help define the prioritization of its deployment or redefine the array to extend its latitudinal extent while maintaining the same amount of stations.
