On the approximation of the inverse error covariances of high‐resolution satellite altimetry data

Yaremchuk, Max; D’Addezio, Joseph M.; Panteleev, Gleb; Jacobs, Gregg A.

doi:10.1002/qj.3336

Cited by 15 publications

(20 citation statements)

References 17 publications

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“…The spatially structured errors will certainly induce strong limitations in the use of SWOT data and must be removed or at least reduced. Past works have addressed the reduction of the small-scale, spatially uncorrelated noise [6,7] and the inclusion of the SWOT error correlations in data assimilation [8,9]. Some techniques to correct the SWOT data's long-range correlated errors have been investigated by Dibarboure and Ubelmann [10].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the method is not expected to reduce the two-dimensional structured errors (e.g., the wet-troposphere error) and is expected to only partly reduce the uncorrelated errors (e.g., the KaRIn error). To reduce the impact of these smaller scale errors, further developments of the method and/or combination with other methods (e.g., [8,9]) will be needed.…”

Section: Introductionmentioning

confidence: 99%

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Reduction of Spatially Structured Errors in Wide-Swath Altimetric Satellite Data Using Data Assimilation

et al. 2019

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The Surface Water and Ocean Topography (SWOT) mission is a next generation satellite mission expected to provide a 2 km-resolution observation of the sea surface height (SSH) on a two-dimensional swath. Processing SWOT data will be challenging because of the large amount of data, the mismatch between a high spatial resolution and a low temporal resolution, and the observation errors. The present paper focuses on the reduction of the spatially structured errors of SWOT SSH data. It investigates a new error reduction method and assesses its performance in an observing system simulation experiment. The proposed error-reduction method first projects the SWOT SSH onto a subspace spanned by the SWOT spatially structured errors. This projection is removed from the SWOT SSH to obtain a detrended SSH. The detrended SSH is then processed within an ensemble data assimilation analysis to retrieve a full SSH field. In the latter step, the detrending is applied to both the SWOT data and an ensemble of model-simulated SSH fields. Numerical experiments are performed with synthetic SWOT observations and an ensemble from a North Atlantic, 1/60° simulation of the ocean circulation (NATL60). The data assimilation analysis is carried out with an ensemble Kalman filter. The results are assessed with root mean square errors, power spectrum density, and spatial coherence. They show that a significant part of the large scale SWOT errors is reduced. The filter analysis also reduces the small scale errors and allows for an accurate recovery of the energy of the signal down to 25 km scales. In addition, using the SWOT nadir data to adjust the SSH detrending further reduces the errors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduction of Spatially Structured Errors in Wide-Swath Altimetric Satellite Data Using Data Assimilation

et al. 2019

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“…This approach for handling correlated observation errors relies on the full R being block diagonal, otherwise it may be necessary to use an approximation method such as Yaremchuk et al . ().…”

Section: The New Approachmentioning

confidence: 97%

A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds

Simonin

Waller

Ballard

et al. 2019

Quart J Royal Meteoro Soc

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Recent research has shown that high-resolution observations, such as Doppler radar radial winds, exhibit spatial correlations. High-resolution observations are routinely assimilated into convection-permitting numerical weather prediction models assuming their errors are uncorrelated. To avoid violating this assumption, observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of full, correlated, error statistics. Some operational centres have introduced satellite inter-channel observation-error correlations and obtained improved analysis accuracy and forecast skill scores. Here we present a strategy for implementing spatially correlated observation errors in an operational system. We then provide the first demonstration of the practical feasibility of incorporating spatially correlated Doppler radial wind error statistics in the Met Office numerical weather prediction system. Inclusion of correlated Doppler radial winds error statistics has little impact on the computation cost of the data assimilation system, even with a fourfold increase in the number of Doppler radial winds observations assimilated. Using the correlated observation-error statistics with denser observations produces increments with shorter length-scales than the control. Initial forecast trials show a neutral to positive impact on forecast skill overall, notably for quantitative precipitation forecasts.There is potential to improve forecast skill by optimizing the use of Doppler radial winds and applying the technique to other observation types. K E Y W O R D SDoppler radial wind, observation error, operational implementation, parallelization, spatial correlations

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“…Moreover, the wet-troposphere error is not expected to be the largest contributing error. However, combining the CER method with existing techniques for locally correlated errors (Brankart et al, 2009;Ruggiero et al, 2016;Yaremchuk et al, 2018) in order to take into account the wettroposphere error should be investigated in future studies and should further improve the results. The four errors concerned by the reduction procedure are the timing error, the roll error, the baseline dilation error and the phase error.…”

Section: Swot Errorsmentioning

confidence: 99%

Wide-Swath Altimetric Satellite Data Assimilation With Correlated-Error Reduction

et al. 2020

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For decades now, satellite altimetric observations have been successfully integrated in numerical oceanographic models using data assimilation (DA). So far, sea surface height (SSH) data were provided by one-dimensional nadir altimeters. The next generation Surface Water and Ocean Topography (SWOT) satellite altimeter will provide two-dimensional wide-swath altimetric information with an unprecedented high resolution. This new type of SSH data is expected to strongly improve altimetric assimilation. However, the SWOT data is also expected to be affected by spatially correlated errors and, hence, can not be assimilated as easily as nadir altimeters. The present paper proposes to embed a state-of-the-art correlated-error reduction (CER) method for the SWOT data into an ensemble-based DA scheme. The DA with the new correlated-error reduced-data (CER-data) is implemented and tested in a simple SSH reconstruction problem using artificial SWOT data and a quasi-geostrophic model. The results show that, in an energetic large scale region, the DA with CER-data-in comparison to the classical DA-reduces the root-mean-square-error (RMSE) of the reconstruction in SSH by approximately 10%, in relative vorticity by 5% and in surface currents by 5-10%, and also slightly improves the noise-to-signal ratio and spectral coherence of the SSH signal at mesoscale (100-200 km) but with a small degradation on the large scales (>300 km). In a less energetic region, the DA with CER-data cuts down the RMSE in SSH by more than 50% on average therefore allowing a significantly more accurate reconstruction of SSH at mesoscale in terms of noise-to-signal ratio, spectral coherence, and power spectral density.

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On the approximation of the inverse error covariances of high‐resolution satellite altimetry data

Cited by 15 publications

References 17 publications

Reduction of Spatially Structured Errors in Wide-Swath Altimetric Satellite Data Using Data Assimilation

Reduction of Spatially Structured Errors in Wide-Swath Altimetric Satellite Data Using Data Assimilation

A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds

Wide-Swath Altimetric Satellite Data Assimilation With Correlated-Error Reduction

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