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The NRT SST product was the result of a collaboration between SOG, that provides the data (link a MFSPP SST data) covering the eastern Mediterranean basin, and CMS that supplies the western Mediterranean images.

Data were received mainly from NOAA-14 and for limited periods (problems of noise on the frequencies dedicated to NOAA-14 transmission in Rome) from NOAA-12 or NOAA-15 satellites. NOAA-12 and NOAA-15 data were adjusted to NOAA-14 to eliminate the existing bias. Only NOAA-14 passes were acquired after June 1999.

The data were preliminary processed at SOG and CMS using apposite software (AVHRR data acquisition and processing). The SST was then computed from the brightness temperature of channel 4 and 5 of the AVHRR through the linear MCSST algorithm proposed by MacClain et al.(1985) at SOG and using a non linear algorithm at CMS, as described for example in Brisson et al. (1998).

A wide overlapping area was kept in both CMS and SOG data, in order to check and eventually eliminate biases between western and eastern Mediterranean sub-products (see also the MFSPP first year report, ).

A cloud detection algorithm, based on the different statistical (variance) and physical characteristics (spatial gradients) of clouds and sea surface temperatures, has been developed and applied to the images. A visual control and further elimination of cloudy pixels from the declouded images has been applied. La figura 1 si riferisce a questa parte

Daily maps were created composing single night pass images, in order to avoid the diurnal cycle and the skin heating effects. La figura 2 si riferisce a questa parte

Daily maps were binned at 1/8º over the grid of MFSPP model.
The binning procedure consists of a median filter with a window of 1/8° centred on the model grid points. Daily SST data were then weekly averaged in NRT mode

Fig.1 a) Example of SST image for the Eastern Mediterranean Sea as derived by the AVHRR. b) the same image after the cloud detection algorithm has been applied.


a b



Fig.2 c) Daily SST Image created from single passes (a) and (b). d) 1/8° binned map on model grid.




SST weekly maps of the Mediterranean basin at the model resolution (1/8°x1/8°) were produced in delayed mode for period October 1998- August 1999.

Starting from September 1999 the SST product was made available every Thursday in Near Real Time. In addition, during the TOP phase of the project (3 months), full resolution (.hdf and .gif) SST maps have been distributed.

All products have been checked and analyzed before delivery and are available on MFSPP server (Weekly Med maps and full resolution for TOP) and SOG data access page (eastern Med daily maps).



SIOMED is part of the "Ambiente Mediterraneo" Project, a convention between the Ente Nazionale Energia e Ambiente (ENEA) research institution and the Italian Ministry for University and Scientific and Technological Research (MURST) initiated in 1996. Its main objective is to promote technological and scientific knowledge for the observation, monitoring and better unerstanding of the Mediterranean environment. This task is achieved by funding various reserach institutions participating in the effort, among which is CNR, and in particular the GOS group at ISAC-CNR Sezione di Roma.


GOS Tasks

GOS takes part in sub-project (Development of methods for data set assimilation in numerical models). GOS's activity is to develop time series of sea level and surface energy and water flux maps from satellite imagery, to be assimilated in Mediterranean circulation models. In particular GOS is taking are of the following.

1) Acquisition of SSM/I (Special Sensor Microwave Imager) data for retrieval of surface atmospheric parameters (humidity, wind, precipitation).

2) Acquisition of satellite altimetry data (ERS and TOPEX/POSEIDON) for the production of sea level anomaly maps.

3) Acquisition of visible and infrared satellite data (AVHRR) for the production of Sea Surface Temperature maps.

4) Oceanographic cruises for hydrographic and air-sea interaction quality data collection.

5) Definition of algorithms for the retrieval of the above surface parameters from satellite data.

6) Validation/correction of these algorithms via the collected in situ measurements.

7) Production of sea level anomaly, surface meteorological parameter and turbulent heat flux maps to be assimilated in Mediterranean circulation models.


Current results

Air-Sea turbulent fluxes from passive microwave (SSM/I) and thermal (AVHRR) satellite data


The detailed results are in press on Annales Geophysicae

Near Real Time Sea Level Anomaly (SLA) and Sea Surface Temperature (SST) products during 2-years of MFS pilot project: processing, analysis of the variability and of the coupled patterns.

B. Buongiorno Nardelli1, G. Larnicol2, E. D’Acunzo1, R. Santoleri1, S. Marullo3, P.Y. Le Traon2

Comparison with Delayed Time data/Accuracy assessment

The Near Real Time (NRT) operational products developed from satellite data (AVHRR, Topex/Poseidon, ERS-2) in the framework of the Mediterranean Forecasting System Pilot Project (MFSPP, autumn 1998-autumn 2000) have been compared to Delayed Time (DT) products over the Mediterranean sea in order to evaluate their accuracy.
Altimeter DT data used were distributed by AVISO (MGC-B, version 2, AVISO, 1996) for the M-GDRS and by the French Processing and Archiving Facility (PAF) CERSAT for the OPRs.
SST DT data were obtained from NASA Pathfinder global dataset (version 4.1, until December 31st 1999) at 9 km resolution, available at the Physical Oceanography Distributed Active Archive Center (PODAAC)
The NRT data were assimilated in MFSPP general circulation model, and consequently it was of great interest first of all to assess the accuracy of the NRT respect to delayed time data, and secondly to characterize the MFSPP years respect to the typical conditions observed in the Mediterranean sea during the previous periods. Actually, NRT data were affected by errors of different nature. In facts, both SLA and SST algorithms for NRT processing are obviously less accurate than delayed time ones. SST operational algorithms process data using constant calibration coefficients, while delayed time products like Pathfinder datasets are obtained from monthly calibrated coefficients. On the other hand, the orbit error contributes significantly to SLA error when estimated in NRT. Moreover, the lower spatial and temporal coverage in the NRT data is also an important source of error (more data are rejected because of the lower quality) and the space-time interpolation scheme (with a shorter time window respect to delayed algorithms) also produces additional errors in the weekly SLA maps. These errors are quantified in tables 1 and 2.


RMS T0-7
RMS T0-14
annual TPERS
winter TPERS
summer TPERS

Table 1 : RMS of the differences between NRT and delayed time altimeter maps. The column with RMS at T0-7 days corresponds to the actual NRT MFSPP system. The column at T0-14 days stresses the problem of the shift in the observation period.

annual AVHRR
winter AVHRR
summer AVHRR

Table 2 : Mean bias error (MBE) and RMS of the differences between NRT and delayed time SST maps.

Analysis of the variability

Despite these differences observed between NRT and DT data, the MFSPP dataset described adequately the fundamental aspects of the Mediterranean circulation and allowed to characterize the MFSPP years in a wider temporal context. In fact, both surface height and temperature data coherently describe a surface circulation that is gradually returning to what was known in literature as the ‘classical’ picture for the Mediterranean, after some years characterized by a modified circulation involving the central part of the basin, with an intense anticyclonic circulation in the Ionian sea (1991-1997). Moreover, MFSPP years are clearly characterized by the presence of Ierapetra eddy.

SLA seasonal means deduced from combined maps of TP and ERS. Units are in cm. (a) Winter 1999, (b) Spring 1999, (c) Summer 1999, (d) Fall 1999

SLA seasonal means deduced from combined maps of TP and ERS. Units are in cm. (a) Winter 2000, (b) Spring 2000, (c) Summer 2000, (d) Fall 2000 Coupled Pattern Analysis

Finally, a methodology proposed by Leuliette and Wahr (1999) to investigate the coupling of SSH and SST has been tested on MFSPP data. This multi-variate method consists in the SVD of the covariance between SST and SSH. When this method was applied to 2 years of SST anomalies and SLA data without any additional processing of the maps, the first mode explained almost all the co-variability of the two fields (99% of the covariance), with a low spatial correlation (.20) and a temporal correlation of .57. Despite the temporal coefficients displayed a very clear seasonal signal for both data, it was thus not possible to give a coherent interpretation for these low-correlated spatial patterns. However, once a more refined handling of the SST and SLA maps has been decided, removing the spatial average from each map (‘gradient CPA’), the co-variability of the temperature and sea surface heights fields was distributed in more modes (74% of the covariance for the first mode, 20% for the second, 6% for the third...) containing both interannual (first mode) and seasonal (second mode) signals and characterized by clear and highly-correlated temporal and spatial patterns.
Actually, a longer time period would allow a better identification of varying signals. In this sense the two years of MFSPP satellite data are not the best dataset to perform an advanced analysis of the coupled patterns. However, applying this method to a limited area as the Mediterranean sea represented, on one hand, a test of the capabilities of this methodology on scales smaller than global scale. On the other hand, it represented the first step to the coupled analysis of longer time series of several datasets, which will be performed as soon as coherent datasets will become available (e.g. MFSPP follow on), and that should lead to a more deep analysis of ocean surface dynamics.






Spatial corr. =.55
Temporal corr.=.78
Explained cov.=.74


First coupled mode of SLA and SSTA data collected during MFSPP after removing the spatial averages over the basin (‘gradient CPA’). Patterns and associated temporal coefficients. (a) SLA pattern, (b) SLA temporal coefficient, (c) SST pattern, (d) SST temporal coefficient.



SLA (a) and SSTA (b) homogeneous correlation maps relative to the first ‘gradient CPA’ mode.






Spatial corr. =.40
Temporal corr.=.77
Explained cov.=.20


Second ‘gradient CPA’ mode of SLA and SSTA data collected during. Patterns and associated temporal coefficients. (a) SLA pattern, (b) SLA temporal coefficient, (c) SST pattern, (d) SST temporal coefficient.



SLA (a) and SSTA (b) homogeneous correlation maps relative to the second ‘gradient CPA’ mode.