Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields

Alvera‐Azcárate, Aida; Barth, Alexander; Beckers, Jean-Marie; Weisberg, Robert H.

doi:10.1029/2006jc003660

Cited by 115 publications

(92 citation statements)

References 34 publications

(43 reference statements)

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“…The use of cloud filling techniques in ocean colour imagery is much less developed than in SST imagery, perhaps because the satellite data has become easily available only recently or perhaps because CHL retrieval is notoriously more error-prone than SST retrieval. Examples of cloud filling of CHL images are provided in Alvera-Azcárate et al (2007). Use of a Kriging approach for cloud filling of MERIS CHL imagery is described in Müller (2007).…”

Section: Importance Of Cloud Filling In Remote Sensing Studiesmentioning

confidence: 99%

Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology

Sirjacobs

Alvera‐Azcárate

Barth

et al. 2011

Journal of Sea Research

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Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full dataset and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space-time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5% for CHL and 97.2% for TSM. For the MODIS TSM dataset, 97.5% of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98% of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006). Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithm artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, and cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases.

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Section: Importance Of Cloud Filling In Remote Sensing Studiesmentioning

confidence: 99%

Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology

Sirjacobs

Alvera‐Azcárate

Barth

et al. 2011

Journal of Sea Research

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show abstract

“…A multivariate DINEOF (Alvera-Azcárate et al 2007), fed by hydrodynamic fields which strongly affect TSM (i.e. bathymetry, tide cycle, bottom stress), are expected to significantly improve the results.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data

et al. 2011

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In situ measurements of total suspended matter (TSM) over the period [2003][2004][2005][2006], collected with two autonomous platforms from the Centre for Environment, Fisheries and Aquatic Sciences (Cefas) measuring the optical backscatter (OBS) in the southern North Sea, are used to assess the accuracy of TSM time series extracted from satellite data. Since there are gaps in the remote sensing (RS) data, due mainly to cloud cover, the Data Interpolating Empirical Orthogonal Functions (DINEOF) is used to fill in the TSM time series and build a continuous daily "recoloured" dataset. The RS datasets consist of TSM maps derived from MODIS imagery using the bio-optical model of Nechad et al. (Rem Sens Environ 114: 854-866, 2010). In this study, the DINEOF time series are compared to the in situ OBS measured in moderately to very turbid waters respectively in West Gabbard and Warp Anchorage, in the southern North Sea. The discrepancies between instantaneous RS, DINEOFfilled RS data and Cefas data are analysed in terms of TSM algorithm uncertainties, space-time variability and DINEOF reconstruction uncertainty.

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“…DIVA (Data-Interpolating Variational Analysis) and DINEOF (Data Interpolating Empirical Orthogonal Functions; Beckers and Rixen, 2003;Alvera-Azcárate et al, 2007): a small subset of observations are not used during the data assimilation and reserved for the validation of the results. The ensemble run and assimilation steps are repeated for different values of the parameters L and α.…”

Section: Methodsmentioning

confidence: 99%

Dynamically constrained ensemble perturbations – application to tides on the West Florida Shelf

et al. 2009

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Abstract.A method is presented to create an ensemble of perturbations that satisfies linear dynamical constraints. A cost function is formulated defining the probability of each perturbation. It is shown that the perturbations created with this approach take the land-sea mask into account in a similar way as variational analysis techniques. The impact of the land-sea mask is illustrated with an idealized configuration of a barrier island. Perturbations with a spatially variable correlation length can be also created by this approach. The method is applied to a realistic configuration of the West Florida Shelf to create perturbations of the M2 tidal parameters for elevation and depth-averaged currents. The perturbations are weakly constrained to satisfy the linear shallowwater equations. Despite that the constraint is derived from an idealized assumption, it is shown that this approach is applicable to a non-linear and baroclinic model. The amplitude of spurious transient motions created by constrained perturbations of initial and boundary conditions is significantly lower compared to perturbing the variables independently or to using only the momentum equation to compute the velocity perturbations from the elevation.

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Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields

Cited by 115 publications

References 34 publications

Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology

Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology

Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data

Dynamically constrained ensemble perturbations – application to tides on the West Florida Shelf

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