Implementation of a reduced rank square-root smoother for high resolution ocean data assimilation

Cosme, Emmanuel; Brankart, Jean-Michel; Verron, Jacques; Brasseur, Pierre; Krysta, Monika

doi:10.1016/j.ocemod.2009.12.004

Cited by 48 publications

(80 citation statements)

References 42 publications

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“…(4.10)] to reduce the observational influence with distance. This radius, which roughly corresponds to 250 km, arises from experiences in prior LETKF experiments in this setting and is in agreement with similar experiments and the statistical properties of the system (Cosme et al 2010;Yan et al 2014). In tuning experiments, we found that this radius also allows the NETF to achieve its best performance.…”

Section: Filter Setupsupporting

confidence: 86%

“…It idealizes the ocean circulation that is representative for the midlatitudes (e.g., the Gulf Stream in the North Atlantic). This configuration is an established test bed in oceanography (e.g., Carrier and Robinson 1962;Cosme et al 2010;Lévy et al 2010;Yan et al 2014). The forcing induces a large-scale double-gyre circulation that is complemented by mesoscale eddies.…”

Section: B Model Configuration and Setupmentioning

confidence: 99%

“…The model is entirely forced by a prescribed zonal wind stress t x , which varies with latitude f, but is constant in time t and longitude l (Cosme et al 2010):…”

Section: B Model Configuration and Setupmentioning

confidence: 99%

“…The first 50 years are considered as the spinup phase toward reaching the model climatology. Afterward, the dynamic equilibrium of the model with respect to the applied forcing is reached (Cosme et al 2010). The actual DA experiment is performed in year 75, while the years 51-74 allow us to estimate the model climatology.…”

Section: Model Initialization and Truth Runmentioning

confidence: 99%

“…Cosme et al (2010) and Yan et al (2014) applied the NEMO configuration described above to investigate different DA algorithms. We closely follow their setup and simulate two types of observations with distinct spatial structures and densities.…”

Section: A Observation Scenariomentioning

confidence: 99%

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Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation

Tödter

Kirchgessner

Nerger

et al. 2016

Monthly Weather Review

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This work assesses the large-scale applicability of the recently proposed nonlinear ensemble transform filter (NETF) in data assimilation experiments with the NEMO ocean general circulation model. The new filter constitutes a second-order exact approximation to fully nonlinear particle filtering. Thus, it relaxes the Gaussian assumption contained in ensemble Kalman filters. The NETF applies an update step similar to the local ensemble transform Kalman filter (LETKF), which allows for efficient and simple implementation. Here, simulated observations are assimilated into a simplified ocean configuration that exhibits globally highdimensional dynamics with a chaotic mesoscale flow. The model climatology is used to initialize an ensemble of 120 members. The number of observations in each local filter update is of the same order resulting from the use of a realistic oceanic observation scenario. Here, an importance sampling particle filter (PF) would require at least 10 6 members. Despite the relatively small ensemble size, the NETF remains stable and converges to the truth. In this setup, the NETF achieves at least the performance of the LETKF. However, it requires a longer spinup period because the algorithm only relies on the particle weights at the analysis time. These findings show that the NETF can successfully deal with a large-scale assimilation problem in which the local observation dimension is of the same order as the ensemble size. Thus, the second-order exact NETF does not suffer from the PF's curse of dimensionality, even in a deterministic system.

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Section: Filter Setupsupporting

confidence: 86%

Section: B Model Configuration and Setupmentioning

confidence: 99%

“…The model is entirely forced by a prescribed zonal wind stress t x , which varies with latitude f, but is constant in time t and longitude l (Cosme et al 2010):…”

Section: B Model Configuration and Setupmentioning

confidence: 99%

Section: Model Initialization and Truth Runmentioning

confidence: 99%

Section: A Observation Scenariomentioning

confidence: 99%

See 3 more Smart Citations

Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation

Tödter

Kirchgessner

Nerger

et al. 2016

Monthly Weather Review

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Joint assimilation of Aquarius‐derived sea surface salinity and AVHRR‐derived sea surface temperature in an ocean general circulation model using SEEK filter: Implication for mixed layer depth and barrier layer thickness

et al. 2015

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Sea surface salinity (SSS) from Aquarius mission and sea surface temperature (SST) from Advanced Very High Resolution Radiometer (AVHRR) for the years 2012–2014 are assimilated into the global Massachusetts Institute of Technology General Circulation Model (MITGCM). Investigation of the impact of assimilation of these two data sets on simulated mixed layer depth (MLD) and barrier layer thickness (BLT) forms the core of our study. The method of assimilation is the Singular Evolutive Extended Kalman (SEEK) filter. Several assimilation runs are performed. Single‐parameter assimilation, as well as joint assimilation, is conducted. To begin with, the model simulated SST and SSS are compared with independent Argo observations of these two parameters. Use of latitudinally varying error variances, which is a novel feature of our study, gives rise to the significant improvement in the simulation of SSS and SST. The best result occurs when joint assimilation is performed. Afterward, simulated MLD and BLT are compared with the same parameters derived from Argo observations forming an independent validation data set. Comparisons are performed both in temporal and spatial domains. Significant positive impact of assimilation is found in all the cases studied, and joint assimilation is found to outperform single‐parameter assimilation in each of the cases considered. It is found that simulations of MLD and BLT improve up to 24% and 29%, respectively, when a joint assimilation of SSS and SST is carried out.

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Outlier detection and sequence reconstruction in continuous time series of ocean observation data based on difference analysis and the Dixon criterion

Jingang

Sun

Zhai

et al. 2017

Limnology & Ocean Methods

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In light of the specific characteristics of the long-term record and complex sequence nature of the ocean observation data, a new method was developed based on the original Dixon detection criteria to be specifically detect and remove data outliers. This method combines the two traditional methods of data quality control and Dixon detection theory and assumes that the second-order differential sequence of parameter measurements passes an appropriate stationarity test. Thus, the measurement attributes are considered to be in the same physical state and to occupy a small range of time and space, equivalent to a parallel observation test. Provided that the observations over a small range of time and space correspond to the record of a sequence covering a short period of time, this short time sequence is treated as a sliding window in the proposed new method. Outliers are detected based on lookup-table after an index parameter Q is calculated within the sliding window. A correlation analysis and the test results show that the proposed new method can effectively instantiate a sequence of outliers characterized by different phases. Compared with other existing methods, the new method proved to be computationally efficient and easily programmable for practical implementation. Further, this method preserves the original data because the outliers are replaced by an inverse distance-weighted average of the recorded data within the window, while other data were intact.Because resources in terrestrial ecosystems are limited, the efficient use of ocean resources is extremely important for economic development and carbon-reduction policies (Pan et al. 2012). In recent years, the speed with which marine resources have been exploited is much greater than ever before. The capacity for ocean and coastal monitoring has been significantly improved to more efficiently manage human activities in marine resource development and natural marine disasters. However, the quality of ocean observations must be ensured to provide accurate information for operational management practices. The control and improvement of data quality are the backbone of meaningful analysis and information extraction in business and scientific research (Shi et al. 2000). Similarly, quality control is an important part of ocean data assimilation systems. If erroneous data are assimilated, they can cause immediate spurious disruptive overturning and error propagation because of the sparse distribution of oceanographic data (Ingleby and Huddleston 2007;Cosme et al. 2010).Presently, global ocean science has entered a multidisciplinary and three-dimensional age. An integral marine integrated monitoring system consists of three parts: marine environment integrated observation, digital communication and management, and data processing and applications. The forms of ocean observation can be remote sensing of satellites and aircrafts and in situ observation of scientific research ships, offshore fixed stations and buoy stations (Fig. 1). Offshore fixed-station obs...

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Implementation of a reduced rank square-root smoother for high resolution ocean data assimilation

Cited by 48 publications

References 42 publications

Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation

Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation

Joint assimilation of Aquarius‐derived sea surface salinity and AVHRR‐derived sea surface temperature in an ocean general circulation model using SEEK filter: Implication for mixed layer depth and barrier layer thickness

Outlier detection and sequence reconstruction in continuous time series of ocean observation data based on difference analysis and the Dixon criterion

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