Evaluation of different covariance models for the operational interpolation of high resolution satellite Sea Surface Temperature data over the Mediterranean Sea

Nardelli, Bruno Buongiorno; Pisano, Andrea; Tronconi, C.; Santoleri, Rosalia

doi:10.1016/j.rse.2015.04.025

Cited by 19 publications

(5 citation statements)

References 25 publications

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“…Our previous experience with satellite sea surface temperature interpolation showed that even if OI can reduce the level of noise in L4 fields (depending on the signal covariance characteristic scales, OI actually works also as a smoother), it is much more efficient to work with a slightly smaller amount of data of high quality, rather than including suspicious data into an analysis system (Buongiorno Nardelli et al, 2003;Marullo et al, 2007;Buongiorno Nardelli et al, 2010;Buongiorno Nardelli et al, 2013;Buongiorno Nardelli et al, 2015). Outliers found in proximity of data gaps, in particular, have a dramatic impact on the accuracy of the retrieval and are better removed before carrying out any interpolation.…”

Section: 4 Input Data Pre-processingmentioning

confidence: 99%

Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

Nardelli

Droghei

Santoleri

2016

Remote Sensing of Environment

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Section: 4 Input Data Pre-processingmentioning

confidence: 99%

Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

Nardelli

Droghei

Santoleri

2016

Remote Sensing of Environment

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“…This kind of methods includes the single EOF reconstruction method (Carnes et al, 1990(Carnes et al, , 1994, the gravest empirical mode (Meinen & Watts, 2000) and the coupled pattern reconstruction (Buongiorno Nardelli & Santoleri, 2004), and the multivariate EOF reconstruction (mEOF-R) method (Buongiorno Nardelli & Santoleri, 2005;Buongiorno Nardelli et al, 2006;Wang et al, 2012). The mEOF-R method still presents a good performance in recent works to reconstruct the mixed layer depth (Buongiorno Nardelli et al, 2017) and ageostrophic motion (Buongiorno Nardelli et al, 2018) in the Southern Ocean. Occasionally, statistical modes could correspond to physical processes, for instance, the first mEOF of steric height could reflect the first baroclinic mode in Buongiorno Nardelli et al (2006).…”

Section: Introductionmentioning

confidence: 99%

A Dynamical‐Statistical Approach to Retrieve the Ocean Interior Structure From Surface Data: SQG‐mEOF‐R

et al. 2020

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Combining the dynamical surface‐trapped mode derived from the Surface Quasi‐Geostrophic (SQG) function with the statistical mode calculated from multivariate empirical orthogonal function (EOF) reconstruction (mEOF‐R) method, this paper proposes a new method, SQG‐mEOF‐R, to estimate the interior density from the sea surface density and sea surface height. This method is applied to the eddy‐resolving Ocean General Circulation Model For the Earth simulator simulation and compared with the conventional SQG, isQG (interior plus SQG), and mEOF‐R methods. Two different dynamical regimes were considered: the NorthWest Pacific, dominated by surface‐intensified eddies, and the Southeast Pacific, characterized by the occurrence of subsurface‐intensified eddies. In both regions, the proposed method proves to be robust in reconstructing mesoscale features, with reduced root‐mean square error and relatively high correlation coefficient. The superiority of SQG‐mEOF‐R is highlighted in those subsurface‐intensified eddies that are little or nothing reconstructed by SQG or isQG method.

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“…The NEMO model is one-way nested with the CMEMS Mediterranean Sea operational model (the Mediterranean Forecasting System; Oddo et al 2014) that provides the lateral boundary conditions (LBCs). The satellite level-4 SST product from CMEMS (Buongiorno Nardelli et al 2013) is ingested into the model through the adjustments of surface heat flux by means of a Newtonian relaxation algorithm. The relaxation coefficient is equal to 260 W m 22 K 21 at nighttime, and gently decreases to 210 W m 22 K 21 at noon because of the use of nighttime measurements only in the production of the SST analysis.…”

Section: A Observational Datasetsmentioning

confidence: 99%

“…(MetOp-B, MODIS, Suomi NPP) and processed by CMEMS (Buongiorno Nardelli et al 2015). Only nighttime measurements are considered, in order not to compromise the scores with possible misrepresentation of the diurnal cycle by the ocean model with respect to the satellite skin SST.…”

Section: Impact On Skill Score Verification Metricsmentioning

confidence: 99%

Introducing Along-Track Error Correlations for Altimetry Data in a Regional Ocean Prediction System

Storto

Oddo

Cozzani

et al. 2019

Journal of Atmospheric and Oceanic Technology

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Because of the systematic error in the processing of altimetry data, sea level anomaly (SLA) observation errors are likely affected by nonnegligible spatial correlations. To account for these, we exploit the synergy of altimetry data with in situ profiles from gliders, piloted to follow the altimetry tracks during the Long-Term Glider Mission for Environmental Characterization 2017 (LOGMEC17) observational campaign in the Ligurian Sea. The assimilation of along-track unfiltered sea level anomalies in a regional ocean analysis and forecast system is consequently optimized by means of introducing spatial correlations for the SLA observation errors. In particular, collocated data of glider and altimetry are used to derive an along-track error covariance model for the sea level anomaly assimilation, assuming that most of the covariance behavior versus separation distance stems from altimetry. Spatial scales of the altimetry error are found to have a correlation radius of about 12 km for the dataset utilized in the Ligurian Sea, using a simple Gaussian shape for the error correlation, shorter than the correlation radius found through assimilation output diagnostics. A variational data assimilation system is modified to relax the usual assumption of uncorrelated altimetry observation errors, thus allowing for along-track error correlations. Its implementation provides promising results in the regional ocean prediction system, outperforming in most verification skill scores the use of uncorrelated observational errors without compromising the analysis scheme efficiency.

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Evaluation of different covariance models for the operational interpolation of high resolution satellite Sea Surface Temperature data over the Mediterranean Sea

Cited by 19 publications

References 25 publications

Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

A Dynamical‐Statistical Approach to Retrieve the Ocean Interior Structure From Surface Data: SQG‐mEOF‐R

Introducing Along-Track Error Correlations for Altimetry Data in a Regional Ocean Prediction System

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