Computation of a new Mean Dynamic Topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in-situ data

Abstract. The accurate knowledge of the ocean Mean Dynamic Topography (MDT) is a crucial issue for a number of oceanographic applications and in some areas of the Mediterranean Sea, important limitations have been found pointing to the need of an upgrade. We present a new Mean Dynamic Topography (MDT) that was computed for the Mediterranean Sea. It takes profit of improvements made possible by the use of extended datasets and refined processing. The updated dataset spans the 1993–2012 period and consists of: drifter velocities, altimetry data, hydrological profiles and model data. The methodology is similar to the previous MDT Rio et al. (2007). However, in Rio et al. (2007) no hydrological profiles had been taken into account. This has required the development of dedicated processing. A number of sensitivity studies have been carried out to obtain the most accurate MDT as possible. The main results from these sensitivity studies are the following: moderate impact to the choice of correlation scales but almost negligible sensitivity to the choice of the first guess (model solution). A systematic external validation to independent data has been made to evaluate the performance of the new MDT. Compared to previous version, SMDT-MED-2014 features shorter scales structures, which results in an altimeter velocity variance closer to the observed velocity variance and, at the same time, gives better Taylor skills.

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Computation of a new mean dynamic topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in situ data

Ocean Science ◽

10.5194/os-10-731-2014 ◽

2014 ◽

Vol 10 (4) ◽

pp. 731-744 ◽

Cited By ~ 49

Author(s):

M.-H. Rio ◽

A. Pascual ◽

P.-M. Poulain ◽

M. Menna ◽

B. Barceló ◽

...

Keyword(s):

Mediterranean Sea ◽

External Validation ◽

Data Sets ◽

Dynamic Topography ◽

Data Set ◽

Mean Dynamic Topography ◽

In Situ Data ◽

Velocity Variance ◽

Sensitivity Studies ◽

The Mediterranean

Abstract. The accurate knowledge of the ocean's mean dynamic topography (MDT) is a crucial issue for a number of oceanographic applications and, in some areas of the Mediterranean Sea, important limitations have been found pointing to the need of an upgrade. We present a new MDT that was computed for the Mediterranean Sea. It profits from improvements made possible by the use of extended data sets and refined processing. The updated data set spans the 1993–2012 period and consists of drifter velocities, altimetry data, hydrological profiles and model data. The methodology is similar to the previous MDT by Rio et al. (2007). However, in Rio et al. (2007) no hydrological profiles had been taken into account. This required the development of dedicated processing. A number of sensitivity studies have been carried out to obtain the most accurate MDT as possible. The main results from these sensitivity studies are the following: moderate impact to the choice of correlation scales but almost negligible sensitivity to the choice of the first guess (model solution). A systematic external validation to independent data has been made to evaluate the performance of the new MDT. Compared to previous versions, SMDT-MED-2014 (Synthetic Mean Dynamic Topography of the MEDiterranean sea) features shorter-scale structures, which results in an altimeter velocity variance closer to the observed velocity variance and, at the same time, gives better Taylor skills.

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A Mean Dynamic Topography of the Mediterranean Sea computed from altimetric data, in-situ measurements and a general circulation model

Journal of Marine Systems ◽

10.1016/j.jmarsys.2005.02.006 ◽

2007 ◽

Vol 65 (1-4) ◽

pp. 484-508 ◽

Cited By ~ 118

Author(s):

M.-H. Rio ◽

P.-M. Poulain ◽

A. Pascual ◽

E. Mauri ◽

G. Larnicol ◽

...

Keyword(s):

Mediterranean Sea ◽

General Circulation Model ◽

General Circulation ◽

Circulation Model ◽

In Situ Measurements ◽

Dynamic Topography ◽

Mean Dynamic Topography ◽

Altimetric Data ◽

The Mediterranean

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Satellite and In Situ Monitoring of Chl-a, Turbidity, and Total Suspended Matter in Coastal Waters: Experience of the Year 2017 along the French Coasts

Journal of Marine Science and Engineering ◽

10.3390/jmse8090665 ◽

2020 ◽

Vol 8 (9) ◽

pp. 665

Author(s):

Francis Gohin ◽

Philippe Bryère ◽

Alain Lefebvre ◽

Pierre-Guy Sauriau ◽

Nicolas Savoye ◽

...

Keyword(s):

Mediterranean Sea ◽

Water Clarity ◽

In Situ Monitoring ◽

Free Atmosphere ◽

Chl A ◽

Southern Bight ◽

In Situ Data ◽

Satellite Retrievals ◽

The Mediterranean

The consistency of satellite and in situ time series of Chlorophyll-a (Chl-a), Turbidity and Total Suspended Matters (TSM) was investigated at 17 coastal stations throughout the year 2017. These stations covered different water types, from relatively clear waters in the Mediterranean Sea to moderately turbid regions in the Bay of Biscay and the southern bight of the North-Sea. Satellite retrievals were derived from MODIS/AQUA, VIIRS/NPP and OLCI-A/Sentinel-3 spectral reflectance. In situ data were obtained from the coastal phytoplankton networks SOMLIT (CNRS), REPHY (Ifremer) and associated networks. Satellite and in situ retrievals of the year 2017 were compared to the historical seasonal cycles and percentiles, 10 and 90, observed in situ. Regarding the sampling frequency in the Mediterranean Sea, a weekly in situ sampling allowed all major peaks in Chl-a caught from space to be recorded at sea, and, conversely, all in situ peaks were observed from space in a frequently cloud-free atmosphere. In waters of the Eastern English Channel, lower levels of Chl-a were observed, both in situ and from space, compared to the historical averages. However, despite a good overall agreement for low to moderate biomass, the satellite method, based on blue and green wavelengths, tends to provide elevated and variable Chl-a in a high biomass environment. Satellite-derived TSM and Turbidity were quite consistent with in situ measurements. Moreover, satellite retrievals of the water clarity parameters often showed a lower range of variability than their in situ counterparts did, being less scattered above and under the seasonal curves of percentiles 10 and 90.

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Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data

Open Geosciences ◽

10.1515/geo-2020-0204 ◽

2021 ◽

Vol 13 (1) ◽

pp. 85-97

Author(s):

Ioannis Moutzouris-Sidiris ◽

Konstantinos Topouzelis

Keyword(s):

Mediterranean Sea ◽

Chlorophyll A ◽

Time Window ◽

Pearson Correlation ◽

Neural Net ◽

Data Set ◽

Chl A ◽

In Situ Data ◽

The Mediterranean

Abstract The objective of this study is to evaluate the efficiency of two well-known algorithms (Ocean Colour 4 for MERIS [OC4Me] and neural net [NN]) used in the calculation of chlorophyll-a (Chl-a) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) compared to in situ measurements covering the Mediterranean Sea. In situ data set, obtained from the Copernicus Marine Environmental Monitoring Service (CMEMS) and more specifically from the data set with the title INSITU_MED_NRT_OBSERVATIONS_013_035, and Chl-a values at different depths were extracted. The concentration of Chl-a at a penetration depth was calculated. Then, water was classified into two categories, Case-1 and Case-2. For Case-2 waters, the OC4Me presents a moderate correlation with the in situ data for a time window of 0–2 h. In contrast with the NN algorithm, where very weak correlations were calculated, lower values of the statistical index of Bias for Case-1 waters were calculated for the OC4Me algorithm. Higher values of Pearson correlation were calculated (r > 0.5) for OC4Me algorithm than NN. OC4Me performed better than NN.

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Mean dynamic topography and eddy kinetic energy in the Mediterranean Sea: Comparison between altimetry and a 1/16 degree ocean circulation model

Ocean Modelling ◽

10.1016/j.ocemod.2009.04.001 ◽

2009 ◽

Vol 29 (2) ◽

pp. 137-146 ◽

Cited By ~ 12

Author(s):

Antoni Jordi ◽

Dong-Ping Wang

Keyword(s):

Kinetic Energy ◽

Mediterranean Sea ◽

Ocean Circulation ◽

Circulation Model ◽

Eddy Kinetic Energy ◽

Dynamic Topography ◽

Ocean Circulation Model ◽

Mean Dynamic Topography ◽

The Mediterranean

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An oceanographic survey for oil spill monitoring and model forecasting validation using remote sensing and in situ data in the Mediterranean Sea

Deep Sea Research Part II Topical Studies in Oceanography ◽

10.1016/j.dsr2.2016.02.013 ◽

2016 ◽

Vol 133 ◽

pp. 132-145 ◽

Cited By ~ 22

Author(s):

A. Pisano ◽

M. De Dominicis ◽

W. Biamino ◽

F. Bignami ◽

S. Gherardi ◽

...

Keyword(s):

Remote Sensing ◽

Mediterranean Sea ◽

Oil Spill ◽

In Situ Data ◽

Océanographic Survey ◽

The Mediterranean

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An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations

Remote Sensing ◽

10.3390/rs12244123 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4123

Author(s):

Michela Sammartino ◽

Bruno Buongiorno Nardelli ◽

Salvatore Marullo ◽

Rosalia Santoleri

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Temperature Fields ◽

Spatial And Temporal Variability ◽

Dynamic Topography ◽

In Situ Data ◽

Artificial Neural ◽

The Mediterranean ◽

3D Fields

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models.

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