Available online at www.sciencedirect.com

s 70 (2008) 33–48www.elsevier.com/locate/jmarsys

Journal of Marine System

Comparison of analyzed and measured wind speeds in theperspective of oceanic simulations over the Mediterranean

basin: Analyses, QuikSCAT and buoy data

Paolo M. Ruti a,⁎, Salvatore Marullo a, Fabrizio D'Ortenzio b, Michel Tremant c

a Centro Ricerche Casaccia, ENEA, S. Maria di Galeria (Rome), 00060, Italyb Laboratoire d'Oceanographie de Villefranche, CNRS and Universitè Pierre et MarieCurie, Villefranche-sur-Mer, France

c METEO-France Centre de Météorologie Marine Brest, France

Received 1 August 2005; received in revised form 2 February 2007; accepted 17 February 2007Available online 12 March 2007

Abstract

Surface vector wind datasets from different assimilation systems and from scatterometers have been recently made availableover the entire Mediterranean basin and for a large spectrum of spatial and temporal resolution. In this work, we compare windvector analyses, derived from different routine assimilation systems and from blended products, to wind vectors obtained fromQuikSCAT satellite sensor and to those directly measured by buoy-mounted anemometers. The analysis has been performed toverify the accuracy of the analyzed data, when the specific objective is the generation of surface winds field to force MediterraneanSea simulations. The inter-comparison covers the period 2000–2005. Our analysis demonstrated that the spatial resolution of thedata sets represents one of the main relevant sources of error in the analyzed wind fields, explaining the worst results of thereanalysis data and the relative accuracy of the ECMWF. This work also confirms the usefulness of blending QuikSCAT andreanalysis products, which could be used to force oceanic simulations. The blended data cover the period from July 1999 to presentwhen QuikSCAT wind data are available. Before this period, blended products are not produced and different solutions to correctwind speed from routine assimilation systems have to be investigated. A simple empirical method to adjust the ERA40 wind speedproduct is then proposed. The analysis of the difference between the annual Mediterranean heat budget computed using theadjusted and the original ERA40 winds suggests that the impact of the correction is not negligible. Considering the year 2000, theannual average heat budget for the whole basin is modified from ∼34 W/m2 to ∼−6 W/m2.© 2007 Elsevier B.V. All rights reserved.

Keywords: Intercomparison; Wind speed; Data buoys; Scatterometers; Meteorological data; Mediterranean

1. Introduction and motivation

One of the main parameters used to assess the forcingfields for oceanic model runs is the surface atmosphericwind (operationally defined at 10 meters from the sur-

⁎ Corresponding author. Tel.: +39 06 30484886; fax: +39 0630484264.

E-mail address: paolo.ruti@casaccia.enea.it (P.M. Ruti).

0924-7963/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jmarsys.2007.02.026

face), which is directly used to derive surface stress andturbulent flux fields. This implies that errors in the deter-mination of the wind term can alter the model forcing andhave an impact in the output of the ocean circulationmodels (Myers et al., 1998). Oceanicmodels are generallyforced at surface by a combination of radiative and mo-mentum fluxes, which drive the transfer of energybetween the atmosphere and the sea. The wind acts inboth the mechanisms (i.e. the mechanic and the radiative),

34 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

driving the dynamic of the surface marine mixed layer. Itrepresents then a crucial factor to realize realistic oceanicsimulations (see for example the review of Large et al.,1994). Moreover, the consistency of the wind parameter

Fig. 1. Central panel: Mediterranean basin with orography and buoy locationQuikSCAT, ECMWF, NCEPB and ERA40 grid meshes for Côte d'Azur (upsquare represents the grid point of the models, while the small box around th[The Mediterranean map is from www.cls.fr/mater/mater_results_v1.htm]

as oceanic forcing depends also by the spatial and tem-poral resolutions, which needs to be adequately refined toavoid model's divergences or unrealistic outputs (Ji andSmith, 1995; Chen et al., 1999; Kelly et al., 1999).

s (A1=Lion, B1=ODAS Côte d'Azur, A2=Mykonos, B2=Santorini).per panels) and Santorini (lower panels) buoy sites. The center of eache buoy location represents a 0.15 degree box centered at the buoy site.

35P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

In the Mediterranean, the importance of the accuracyand of the spatial and temporal resolution of the windforcing has been already highlighted in the context ofboth marine operational forecasting (Bargagli et al.,2002; Pinardi et al., 2003) and climate simulations(Castellari et al., 2000; Artale et al., 2002). High spatialresolution wind is needed mainly because the Mediter-ranean basin is surrounded by a complex orography(Fig. 1), which strongly influences the atmosphericflows (ranging from local to synoptic scales) and, inturn, the surface forcing fields for oceanic simulations.

Presently, the available wind datasets, covering theMediterranean basin for the last few decades, derivefrom two main sources:

i) the winds measured by satellite-mounted scatte-rometer instruments (e.g. QuikSCAT or ERS);

ii) the products of global assimilation systems, whichinclude, for example, the operational analysis ofthe European Center for Mid-Range WeatherForecast (ECMWF).

Moreover, the National Center for EnvironmentalPrediction (NCEP), in collaboration with the NationalCenter for Atmospheric Research (NCAR) (Kalnayet al., 1996), and the ECMWF (Simmons and Gibson,2000) have released re-analysed datasets for the timeframes 1948–today and 1957–2002, respectively, an-swering the need for homogeneous long time seriesproduced at the same resolution (vertical and horizontal)and with the same procedure.

To increase their accuracy, reanalysis products havebeen also “corrected” by the use of some statistical andspectral properties of the wind fields, derived analyzingexperimental data (i.e. scatterometer). Among the others,Chin et al. (1998) corrected NCEP re-analysis data usingQuikSCATobservations, improving the performances ofglobal ocean simulation models when corrected windsare used (Milliff et al., 1999).

The assessment of the errors of the wind products inthe Mediterranean sea is then a pre-requisite to obtainrealistic and truthful simulations of the basin circulation.So, an accurate exercise of comparison with experi-mental data collected in the region is then required.

In the past, comparisons between re-analysis or an-alysis products and surface measurements have beenperformed at some ocean sites (in and out the Mediter-ranean area), often giving contradictory conclusions.Weller and Anderson (1996), comparing buoy and ana-lysis winds during the COARE-IFA experiment, verifiedthat ECMWF data underestimate wind speed in thetropical Pacific. Conversely, Weller et al. (1998) demon-

strated that the ECMWF provides realistic winds whencompared to in-situ time series measured off the Omancoast. These differences could be explained consideringthe regional aspects of the atmospheric flow and of theair–sea interaction, and confirm that the reliability of thewind products could be strongly dependent on the in-vestigated area. In the Mediterranean area, Bozzano et al.(2004) compared sea winds data from a single buoy in theLigurian Sea (Northwestern Mediterranean Sea) withECMWF products. They concluded that the ECMWFoverestimates the measured wind for calm conditions andunderestimates the experimental data for near gale andgale conditions.

However, the results of the cited works are restrictedto the few oceanic sites where in-situ data are available,de facto limiting their applicability to larger oceanregions.

The use of satellite products minimizes the problem,offeringwind fields data with a world-wide coverage andwith an high spatial and temporal resolution. However,also satellite data needs a validation effort, to character-ize the overall accuracy and precision of the satellitederived datasets (Freilich andDunbar, 1999;Mears et al.,2001). In particular, winds obtained from the QuikSCATscatterometer, which will be used in the follow, havebeen validated with in-situ buoy or ship data overseveral ocean locations (Draper and Long, 2002; Ebuchiet al., 2002; Bourassa et al., 2003; Freilich and Vanhoff,2003; Chelton and Freilich, 2005). In the Mediterraneanarea, a comparison between QuikSCAT scatterometer,buoy and ECMWF analysis winds has been performed inthe framework of the Mediterranean ForecastingSystem Toward Environmental Prediction (MFSTEP)project (Pinardi et al., 2003), highlighting an underes-timation for strong winds of the ECMWF analysis andan overestimation for lower winds, less than 4 m s−1

(http://www.bo.ingv.it/mfstep/PandDR/deliverables/IYSc_Rep/WP3.pdf).

In the present paper, a comprehensive evaluation ofthe different wind data sets in the Mediterranean Sea isdescribed. The QuikSCAT and analyzed wind vectorserrors will be assessed by comparison with high qualityin-situ surface buoy observations. The evaluation willfocus on four selected Mediterranean sites: one in theLigurian Sea, one in the Gulf of Lion, and two in theAegean Sea. The choice of the sites was determined bythe following considerations:

1. In the selected sites, four meteorological buoys aredeployed, and the collected data are available.

2. Most of the buoys cover an entire year, withoutrelevant gaps in the time series.

36 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

3. The four buoys are located in areas where importantMediterranean winds are observed, and where rele-vant air–sea interactions occur. This will allow us forevaluating the performance of the selected data setsover a wide range of wind conditions.

The paper is organized as follows: the surface winddatasets are introduced in the next section. In this work,several datasets have been considered: near-surfacewinds computed by both the operational ECMWF andERA40 assimilation systems, NCEP reanalysis, near-surface winds derived from satellite microwave mea-surements (QuikSCAT) and winds measured by fourbuoys moored in the western and eastern Mediterraneanbasins. A blended data set, merging NCEP reanalysisand QuikSCAT data, has been also evaluated. The col-location procedures and the impact of the wind speedcorrection for the buoy data are also described. InSection 3, gridded analysis and remotely measured windare compared with buoy measurements. In Section 4, aselected gridded data set has been compared with theQuikSCAT winds, over all the Mediterranean basin.Section 5 presents a simple empirical method to correctlong wind speed time series and a discussion on theadvantages of a new blended product are examined inthe context of Mediterranean heat budget (Section 5). Asummary is finally provided in Section 6.

2. Surface wind datasets

The comparison of the selected wind datasets is basedon the period covering the years 2000 to 2005, being thebuoys data available for the year 2000 (Mykonos,Santorini, Azur) and for the years 2002 to 2005 (Lion).The modeled data sets (ECMWF, ERA40, NCEP andNCEP-QuikSCAT) have bee compared against the buoysdata, when all the models are available, i.e. the year 2000for the Mykonos, Santorini and Azur buoys, and the year2002 for the Lion buoy, since ERA40 is not available after2002. Regarding the QuikSCAT data, the comparisonagainst buoy data has been performed when both the dataare available: 2000 for the Mykonos, Santorini and Azurcases, and since 2002 to 2005 for the Lion case.

2.1. Satellite data

QuikSCAT measures the sea surface radar cross-section σ0 for several different azimuth angles for bothhorizontally and vertically polarized radiation. The dataare fitted to a geophysical model function that describesthe expected σ0 as a function of wind speed and direc-tion relative to the look angle, to obtain the equivalent

neutral wind speed at a height of 10 m above sea level.Equivalent neutral wind speeds can differ from theactual 10 m wind speed, but these differences are usuallyless then 0.5 m s−1 (Bourassa et al., 2003). The presenceof rain in the atmosphere can affect σ0. At low windspeeds the scattering from rain drops dominates withrespect to the scattering due to the wind action over thesea surface, increasing the wind estimate so that a rainflag is necessary to reliably use the QuikSCAT data.

In this work, QuikSCAT Level 3 scatterometer seawinds are used, which consist of gridded values of scalarwind speed, meridional and zonal components on anapproximately 0.25×0.25 degree resolution. One of theobjectives of the present paper is the evaluation of winddata as forcing of oceanic simulations. For this reason,QuikSCAT gridded level-3 products rather than thelevel-2 swath winds are deliberately selected for thecomparison, being the firsts the most used by oceanmodelers. The data are provided by the JPL PO-DAACand include rain flags as an indicator of wind valuedegradation (Physical Oceanography DAAC, GuideDocument, 2001). Only observations for which the rainflag algorithm does not detect rain are retained and areconsidered in the following comparison exercise.

Since 22 January 2002, near-surface wind informa-tion observed by QuikSCAT has been assimilated in theoperational 4D-Var system at ECMWF (Hersbach et al.,2004). Therefore, the analysis for the years before 2002is not affected by QuikSCAT assimilation.

2.2. Gridded model data

2.2.1. ECMWF analysisThe ECMWF data for the year 2000 consist of 6

hourly analyzed winds produced by the operationalcycle CY21r4 of the Integrated Forecast System at theECMWF, operational since October 1999 (Jakob et al.,2000). For the year 2000, the assimilation system usesthe ECMWF model at the triangular truncation T319(about 60 km) and from November 2000 at the tri-angular truncation T511 (40 km). The system includes60 vertical levels.

Since 22 January 2002, an upgraded version of themodel, CY24r3, was implemented. This version in-cludes several important changes that affect all compo-nents of the system (data assimilation, atmospheric andoceanic wave forecasts, EPS; for details see ECMWFNewsletter 93). Regarding the assimilated data, severalimprovements have been activated (assimilation ofQuikSCAT data, less thinning of aircraft observations,more intelligent thinning and better scan correction ofATOVS radiances).

37P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

2.2.2. ECMWF re-analysisERA40 winds have been produced using the In-

tegrated Forecasting System developed jointly byECMWF and Météo-France (Simmons and Gibson,2000). The three-dimensional variational assimilation ofobservations is used, and the assimilating model hasT159 spectral resolution in the horizontal and 60 levels inthe vertical. The analysis has been produced every 6 h(http://www.ecmwf.int/research/era/) and data have beenreleased from the period 1962–2002. The ERA40 as-similation system does not use the QuikSCAT data.

2.2.3. NCEP re-analysisThe NCEP re-analysis has been produced at the

National Center for Environmental Prediction, in col-laboration with the National Center for AtmosphericResearch (NCAR; Kalnay et al., 1996). The surfacewinds are available four times per day on a Gaussiangrid consistent with T62 resolution (i.e., triangular trun-cation, admitting 62 zonal wavenumbers). The grid hasa resolution of almost 1.875° lon per 1.9° lat.

NCEP Re-analysis data have been provided by theNOAA-CIRES Climate Diagnostics Center, Boulder,Colorado, USA, from their Web site at http://www.cdc.noaa.gov/. The NCEP-reanalysis assimilation systemdoes not use the QuikSCAT data.

2.2.4. NCEP-blendedGlobal 6-hourly maps of ocean surface winds are

derived from a space and time blend of QuikSCATscatterometer observations and NCEP re-analyses(hereafter referred as NCEPB). The blending methodcreates global fields by using QuikSCAT wind in swathregions, and modifying the NCEP fields in the regionsnot covered by satellite. The method adds to the low-wavenumber NCEP fields a high-wavenumber compo-nent, which is derived from monthly regional QuikS-CAT statistics. The final blended product has a spatialresolution of 0.5°×0.5°, and a global coverage from 88°S to 88° N.

A detailed description of the blending procedure canbe found in Chin et al. (1998), while the rain effects onQuikSCAT surface wind retrievals and on the NCEPBare explained in Milliff et al. (2004). The NCEPB oceanwinds product has provided by Colorado ResearchAssociates, Boulder, Colorado, USA, and are availablefrom the Web (http://dss.ucar.edu/datasets/ds744.4/).

2.3. Buoy data

The in-situ data used for the comparison are obtainedfrom four buoys located in two different regions of the

Mediterranean sea (Fig. 1). Two buoys are managed bythe Greek National Center for Marine Research as partof the POSEIDON system and are positioned in theAegean Sea, the first near to the Island of Santorini(36.°16′ N, 25.°29′ E), the second near the Island ofMykonos (37.51°N, 25.46°E). The data acquisition sce-nario of the POSEIDON system provides observationsevery 3 h. The other two selected buoys are managed byMétéo-France and are deployed in the northwesternMediterranean Sea. More precisely, the ODAS-03FRbuoy (Azur buoy in the following) is moored in theLigurian Sea, (43°22′ N, 7° 51′ E), and the Gulf of Lionbuoy (Lion in the following) is positioned in the Gulf ofLion (42.1° N and 4.7°E).

The height of the POSEIDON and ODAS buoy-mounted anemometers is 3.2 m. The wind measure-ments are averaged over 10 min every hour for Côted'Azur and Lion buoys and every 3 h for Santorini andMykonos buoys.

Details on the instrumental characteristics and on thesampling protocols of the two buoys are given by Nittiset al. (2002) for the Santorini and Mykonos buoys, whilefor the Azur and Lion buoys are summarized in thefollowing.

The Meteo-France buoys use a three cup anemometer(Vector Instruments A100 L2) and a self referencingwind vane (Vector Instruments SRW1GM) for windmeasurements. The wind measurements are averagedover 10 min every hour. The average wind speed is givenby the simple scalar average of the number of impulsionsissued from the anemometer (10Hz/kt). Avector averageis used to calculate the mean wind direction. During10 min, every 28 turns of the anemometer (the wind vanedata are collected. The “u” and “v” components are thencalculated for each observation. Next, the average of the“u” and “v” components are computed and the averagewind direction is obtained from “arctan(u /v)”.

2.4. Collocation procedure

To compare satellite and model wind data with buoyobservations, matchup datasets of collocated (in spaceand time) wind pairs were produced. For each data set, a0.15 degree square box centered on the buoy locationhas been considered. Following Mears et al. (2001), thedimension of the box is chosen so that if the buoy is nearto the center of the QuikSCAT pixel, only that pixel isconsidered. If a pixel of the data set embeds entirely thesquare box of the buoy location, that particular pixelwill be selected for the comparison. On the other hand, ifthe buoy box overlaps two or more data pixels, aweighted average will be performed, with the weights

38 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

proportional to the distance of the pixel-center from thebuoy location.

Sample cases relative to the selected data sets areshown in Fig. 1, where the box positions and theselected pixels are indicated, for each box location andfor each spatial resolution of the data. In the case of theCôte d'Azur and Lion buoys, the 0.15 degree square boxof the buoy is totally within one QuikSCAT pixel, whilefor the case of Santorini and Mykonos systems the boxoverlaps 4 QuikSCAT pixels (Fig. 1).

Regarding the temporal collocation, the griddedmodel-derived data sets match exactly with the buoymeasurements (hourly for the Côte d'Azur and Lion,and every 3 h for Santorini and Mykonos), resulting in 4matchups per day.

The variable timing of the QuikSCAT observationsrequires a specific criterion to collocate in time satelliteand buoy measurements. As the Côte d'Azur and Lionbuoys wind are available hourly, and the Santorini andMykonos buoys wind 3-hourly, the QuikSCAT overflight time is no more than 30 min and 90 min distantfrom the closest buoy observation, respectively. Thus,the time lag between the QuikSCATover flight time andthe closest buoy measurement varies in the range 0–30 min (0–90 min) for Côte d'Azur and Lion (Santoriniand Mykonos) buoys. A scatterometer data is then re-tained as a matchup when his temporal distance with acollocated in-situ observation is comprised in the time-range 30 to 90 min. In fact, the difference betweensatellite and buoy wind measurements is uniformly dis-tributed as function of the collocation time step, sup-porting our temporal matching procedure.

Additional consideration concerns the optimal pro-cessing of the buoys data in the context of the satellite/in-situ comparison. If the Taylor hypothesis applies(Taylor, 1938), the optimum averaging time for the buoydata should depend on the spatial resolution of thecomparison data sets. Considering a typical phase ve-locity of 5–10 m s−1 for Mediterranean cyclones, buoydata should be averaged over 30–60 min when com-pared with scatterometer winds and over even longertimes for ECMWF and NCEP analysis. Unfortunately,we only have buoy wind data averaged over 10 min.Thus, unresolved variability of the wind speed in theQuikSCAT footprint or in the grid box spatial average ofanalyzed winds can result in wind underestimationrespect to the buoy data. The order of magnitude of thisunderestimation, as function of the spatial scale has beenestimated by Levy (2000). He found that, for grid scalesbetween 25 km and 250 km the sub-grid unresolvedvelocity scale should be in the range between few tenthsof m s−1 and about 1.5 m s−1. Thus, in the comparison

exercise only differences over this threshold should beconsidered significant.

The described matchup procedure has been appliedover the entire measurement period, resulting in 580matchup points for Santorini, 365 matchup points forMykonos, 370 matchup points for Côte d'Azur and1890 matchup points for Lion.

2.5. Impact of wind speed correction on the in-situ data

Surface observations measure the actual wind speedat the height of the anemometer instrument, which is, forbuoy systems, typically located between 3 and 10 m (inour case 3.2 m). To achieve the comparison with thesatellite or model estimates, the buoy derived windshave been converted to the equivalent neutral windspeed at a height of 10 m above sea level for com-parisons with QuikSCAT observations or to the actual10 m wind speed for comparisons with model estimate.

The relationship that yields the measured wind speedat different heights is a function of air turbulence, whichis, in turn, determined by the wind shear and by thebuoyancy of the atmosphere (Garratt, 1992), that isstrictly dependent on the vertical density stratification.Two methods have been developed to account for thedescribed processes (Mears et al., 2001). In the first, asimple approach assumes a logarithmically varying windvertical profile, so that the corrected wind speed at aheight z is given by

ULOGðzÞ ¼ lnðz=z0Þ=lnðzm=z0Þ4UðzmÞ

In the expression, derived using a mixing-length ap-proach and assuming neutral stability conditions, U(z) isthe wind speed at a height z, zm is the measurementheight and z0 is the roughness length (a typical oceanicvalue for z0 is 1.52×10

−4 m, Peixoto and Oort, 1992).This method does not take account of the atmosphericstratification and then can lead to significant errors in theextrapolation. A second method, named “neutral stabil-ity correction” (Liu and Tang, 1996), permits to ver-tically extrapolate the wind data with a minor uncertaintyand it was then adopted here. The procedure requires airand sea surface temperatures, surface pressure and near-surface relative humidity. The whole set of these param-eters is, however, not always available, as the case forexample of our buoy Santorini. It is then important todefine the range of applicability of the two correctionmethods and to investigate the possible sources of theerror when the less accurate log method is used.

As a preliminary analysis, we consider a buoy for thewestern basin (Côte d'Azur) and a buoy for the eastern

Fig. 2. Scatterplot of the difference between Liu- and Log-corrected wind speed as a function of measured wind speed. a) Côte d'Azur, using buoydata only; b) Côte d'Azur, using ECMWF analysis; c) Santorini, using ECMWF analysis.

39P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

basin (Santorini). Fig. 2a shows a scatter plot of thedifference between Liu- and Log-corrected wind speedsas a function of measured wind intensity, for the Côted'Azur buoy. The main difference is observed for strongwinds (roughly above 15 m s−1), where the log cor-rection differs from the neutral stability correction byless than 0.5 m s−1. In the North Western MediterraneanArea then it is possible to argue that, in the range ofvariability of the measured winds, the application of thelogarithmic method does not introduce large errors. Theunavailability of some of the parameters required toapply the neutral stability correction method at Santoriniposes some problems in order to evaluate the differencebetween the two methods at this location. A possiblesolution could be to use the ECMWF analysis data toperform the analysis for Santorini. Thus, we first eval-uate the skill of the ECMWF analysis at Côte d'Azursite (Fig. 2b), and then we compare the methods atSantorini (Fig. 2c). Fig. 2b shows the good performanceof the ECMWF analysis compared to the in-situ data(Fig. 2a). The comparison of Fig. 2c with the otherfigures suggests that the log correction method does notproduce large errors for the two sites, and that the biasattains the value of about 0.5 m s−1 only for strongwinds.

3. In-situ comparison

In this section, we compare wind speeds and winddirections measured at the buoy sites against the cor-responding gridded models (ECMWF, ERA40, NCEP,NCEPB) and satellite data (QuikSCAT). A statisticalcomparison has been performed using scatter diagrams,histograms and standard parameters (Mean BiasError — MBE, Root Mean Square Error — RMSE,

correlation coefficient R, slope and intercept of theregressed line).

The comparison for the wind speed at the Côte d'Azursite is shown in Fig. 3, first column. The wind speed at thebuoy site versus the wind speed measured by QuikSCATis shown in Fig. 3-a1. The QuikSCAT observations,except from calm and light winds (b5 m s−1), are in goodagreement with the buoy data. Taking into account onlywinds less than 5 m s−1, the QuikSCAT data overestimatethe measured wind. In fact, low wind speeds are unable toovercome the viscous damping and the Bragg wavescannot grow, so nomicrowave backscatter can be detectedover the noise level (Plant, 2000). More specifically, thebias between the twowindmeasurements tends to zero forhigh winds, implying that the deviation of the slope fromunity is essentially due to the overestimation at low windspeed. Considering the statistical parameters (Table 1), thewind speed correlation is 0.93, while theMBE and RMSEare respectively 0.59 m s−1 and 1.5 m s−1. Buoys andscatterometer correlate closely, as expressed by a slope ofabout 0.89 and intercept close to 1 m s−1.

The scatter diagrams for the other gridded modeldatasets are shown in Fig. 3b1–e1. The ECMWF datahave the best agreement, with the closest to one slope(0.65), the higher correlation coefficient (0.81) and thelower RMSE (2.59 m s−1). Nevertheless, the ECMWFdata underestimate strong winds (N10 m s−1). TheERA40 data (Fig. 3-c1) show a strong underestimationfor winds higher than about 5 m s−1. Although thespread of the data points around the regression line issimilar to that observed for ECMWF, the RMSE ishigher (3.87 m s−1) and the correlation coefficient islower (0.64). Fig. 3-d1 shows the scatter diagram for theNCEP data. In this case the skill is quite low, with thelowest correlation coefficient (0.43) and the highest

40 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

RMSE (3.97 m s−1). The data overestimate the buoymeasurements for light winds and underestimate windshigher than 10 m s−1. On the contrary, the NCEPB data(Fig. 3-e1) spread around the diagonal of the scatter plot,without showing any underestimation or overestima-tion. The RMSE is 3.49 m s−1, lower than ERA40 andNCEP, while the correlation coefficient (0.61) issignificantly higher than for NCEP. The Lion buoyshows a general improvement for all the datasets(Fig. 3b5–e5, and Table 2). The analysis of the corre-lation coefficient, MBE and RMSE depicts an overallimprovement of the different datasets respect to theresults of the Côte d'Azur, or in one case no relevantvariations (NCEPB). A possible reason could be at-tributed to the off coast position of the Lion buoy. TheCôte d'Azur could be affected by coastal topographicforcing more than the companion buoy, and these localeffects could be misrepresented by the General Circu-lation Models used by the assimilation systems. TheQuikSCAT performance is practically unchanged for thetwo buoys. Notice that, according to Chelton andFreilich (2005), the biases in the scatterometer versusbuoys and in the models versus buoys are of oppositesigns. This will have an impact on the computation ofthe heat budget.

The analysis performed for the Santorini buoy isshown in Fig. 3, third column. The QuikSCAT databehave as in the Côte d'Azur case, i.e. in very goodagreement, except for winds lower than 5 m s−1. Thegood performance is confirmed by the value of thecorrelation coefficient (0.87) and of the RMSE (1.90 ms−1) (Table 3). The ECMWF scatter (Fig. 3-b3) shows abehavior quite similar to that shown in Fig. 3-b1 for thewestern buoy, as confirmed by the statistical parameters(Table 3). In fact, the correlation coefficient is 0.77,while the RMSE is 2.39 m s−1. With respect to thewestern basin, a slightly different behavior is observedfor the ERA40 (Fig. 3-c3), NCEP (Fig. 3-d3) andNCEPB (Fig. 3-e3). ERA40 shows a higher slope (0.54)and, reduced MBE (−1.33 m s−1) and RMSE (2.89 ms−1) respect to the Côte d'Azur, demonstrating thetendency to improve the wind simulation over theAegean Sea. At the Santorini site, both NCEP and

Fig. 3. Comparison between buoy and QuikSCATwind data (first row), and beof wind intensity in the Ligurian Sea (first column) and in the Aegean Sea, San(second column) and in the Aegean Sea (forth column). a1–a4) QuikSCAT vec1–c4) ERA40 versus buoy measurements. d1–d4) NCEP versus buoy meComparison between buoy and QuikSCAT wind data (first row), and betweewind intensity in the Gulf of Lion (first column) and in the Aegean sea, Myk(second column) and in the Aegean Sea (forth column). a5–a8) QuikSCAT vec5–c8) ERA40 versus buoy measurements. d5–d8) NCEP versus buoy mea

NCEPB wind speeds have a reduced spread around theregression line and an improvement of all the statisticalparameters (see Table 3). The analysis of the AegeanSea buoys is completed by using the data from Myko-nos. The analysis of the scatter plots (Fig. 3a7–e7) andof the statistical parameters (Table 4) shows similarresults to what observed in Santorini, but with a sensibleincrease of the MBE and RMSE. In this case, thelocation of the buoy and the shorter period of the timeseries could affect the Mykonos result.

In order to complete the analysis and to identifypossible reasons for the differences between western andeastern basins, a comparison on the wind directions havebeen performed (Fig. 3 pair columns). The measure-ments are divided into bins of 5° wide and a winddirection histogram has been plotted for each site and foreach matchup data set. Each histogram is then divided intwo parts for westerly (−180 to 0 degrees) and easterly(0 to 180°) winds.

The Côte d'Azur buoy histograms always show(Fig. 3, second column) that two main directions, i.e.from the northeast and southwest, are present. The firstprevailing direction corresponds to the Mistral, a strongnorthwesterly wind, which blows through the Garonneand Rhone valleys and then into the Gulf of Lion (fora detailed description of the main Mediterranean windssee also Zecchetto and Cappa, 2001). The regionalpressure pattern, which drives the Mistral, is character-ized by a pressure low over the Aegean Sea and apressure high covering the Spain and the Atlas region.The low level flow blows into the Gulf of Lion fromnorthwest, but a northern flow is present over the Italianpeninsula and its deformation due to the Island forcing(Sardinia and Corsica) produces northeasterly windsover the Ligurian Sea.

The second relevant direction represents a windblowing from southwest, which could be associatedwith atmospheric highs entering in the Gulf of Lion fromthe west or southwest, and stationing over the Gulf ofGenova. The flow is channeled along the coast andbetween the Ligurian topography and the Corsica Island.This atmospheric pattern explains the strong coastaleffects observed in the Côte d'Azur buoy. QuikSCAT

tween buoy and modeled wind data (second to fifth rows). Scatter plotstorini, (third column). Histograms of wind direction in the Ligurian searsus buoy measurements. b1–b4) ECMWF versus buoy measurements.asurements. e1–e4) Blended NCEP versus buoy measurements. 3bis.n buoy and modeled wind data (second to fifth rows). Scatter plots ofonos, (third column). Histograms of wind direction in the Ligurian searsus buoy measurements. b5–b8) ECMWF versus buoy measurements.surements. e5–e8) Blended NCEP versus buoy measurements.

41P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

(Fig. 3-a2) has a good skill in reproducing the prevailingdirections, with a slight underestimation of the windsblowing from northeast. For the southwesterly winds, a

small shift is also evident in the histogram: buoy peak is inthe interval −140° to −135°, while QuikSCAT peak is inthe interval −120° to −115°.

Fig. 3 (continued ).

42 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

Table 3Statistical parameters for the wind speed: Mean Bias Error — MBE,Root Mean Square Error — RMSE, correlation coefficient R, slopeand intercept of the regressed line

Santorini Interceptm s−1

Slope R MBE(m s−1)

RMSE(m s−1)

No. ofpairs

QuikSCAT 1.88 0.75 0.87 0.24 1.90 580ECMWF 2.38 0.61 0.77 −0.14 2.39 1454ERA40 1.60 0.54 0.73 −1.33 2.89 1454NCEP 2.33 0.48 0.63 −0.99 3.13 1454NCEPB 2.78 0.67 0.72 0.64 2.82 1454

Santorini buoy.

Table 1Statistical parameters for the wind speed: Mean Bias Error — MBE,Root Mean Square Error — RMSE, correlation coefficient R, slopeand intercept of the regressed line

Côted'Azur

Interceptm s−1

Slope CorrC MBE(m s−1)

RMSE(m s−1)

No. ofpairs

QuikSCAT 1.17 0.89 0.93 0.59 1.50 369ECMWF 1.18 0.65 0.81 −0.97 2.59 1249ERA40 1.55 0.37 0.64 −2.23 3.87 1249NCEP 3.65 0.33 0.43 −0.41 3.97 1249NCEPB 2.78 0.57 0.61 0.16 3.49 1249

Côte d'Azur buoy.

43P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

The histograms for the other gridded model datasetsare shown in Fig. 3, b2–e2. ECMWF displays a rea-sonably good performance, with only a slight northwardshift of the direction for the easterly part. The ERA40data capture the two main peaks observed in the buoymeasurements, though relevant errors are still evident.In fact, ERA40 generates easterly more than northeast-erly winds and westerly more than southwesterly winds.The error could be caused by the coarser resolution ofthe ERA40 model. This effect is even more evident inthe histogram of the NCEP data, which does not showany preferential direction. The error in the directionappears to increase as the resolution degrades. On theother hand, the same data “corrected” by the use ofQuikSCAT winds (i.e. the NCEPB) significantly in-crease their skill (Fig. 3-e2). We can argue that theimprovement in the wind direction of the NCEPB data,is mainly due to the use of QuikSCAT data instead of theNCEP reanalysis, when the former are available. TheLion buoy is on the track of the Mistral main flow.Evidence of this comes from the unimodal peak in thedirection histogram, a northwest predominant direction.All the models are able to capture the main Mistral flow.The large scale pressure pattern generating the Mistral iswell reproduced by all the assimilation systems while,coastal features are strongly dependent on the resolution

Table 2Statistical parameters for the wind speed: Mean Bias Error — MBE,Root Mean Square Error — RMSE, correlation coefficient R, slopeand intercept of the regressed line

Gulf duLion

Interceptm s−1

Slope CorrC MBE(m s−1)

RMSE(m s−1)

No. ofpairs

QuikSCAT 0.02 1.06 0.97 0.52 1.44 1890ECMWF 0.66 0.85 0.92 −0.52 1.77 1428ERA40 0.77 0.60 0.84 −2.28 3.33 961NCEP 1.60 0.70 0.78 −0.81 2.90 1428NCEPB 3.38 0.54 0.52 −0.19 4.36 1428

Gulf of Lion buoy.

of the atmospheric model. So, considering both thewestern buoys, the ECMWF analysis has the best skill.

The analysis for Santorini is shown in Fig. 3, fourthcolumn. The comparison between QuikSCAT and thebuoy data (Fig. 3-a4) shows a predominance of windsfrom north-northwest and west. In the summer months,the winds in the Aegean region are predominantly fromthe north (Aetesians). These winds begin to blow in Mayand June, reach full strength in July and August, and dieoff in September and October. The Aetesians are aconsequence of a pressure gradient between a low pres-sure area over Pakistan (the Asian monsoon low), whichextends its influence as far as the eastern Mediterranean,and the high pressure area over the Azores, whichaffects the western Mediterranean. The pressure gradi-ent between these two stable pressure areas produces theconstant northerlies observed in summer.

Also for this site, the QuikSCAT displays a good skillin reproducing the wind direction. Both ECMWF andERA40 (Fig. 3, b4–c4) reproduce well the main direc-tions, although they overestimate the northwesterly com-ponent and they underestimate the westerly one. In thecase of the NCEP wind direction distribution (Fig. 3-d4),thewesterlywinds aremissed and theAetesians are slightlymore northeasterly than northwesterly. Once again, theblended product (NCEPB, Fig. 3-e4) improves the estimate

Table 4Statistical parameters for the wind speed: Mean Bias Error — MBE,Root Mean Square Error — RMSE, correlation coefficient R, slopeand intercept of the regressed line

Mykonos Interceptm s−1

Slope CorrC MBE(m s−1)

RMSE(m s−1)

No. ofpairs

QuikSCAT 1.92 0.71 0.86 −0.59 2.33 356ECMWF 1.49 0.64 0.84 −1.57 2.83 810ERA40 1.07 0.56 0.79 −2.71 3.78 810NCEP 2.64 0.38 0.56 −2.68 4.56 810NCEPB 3.62 0.51 0.62 −0.58 3.48 810

Mykonos buoy.

Fig. 4. Vector correlations of the scatterometer and ERA40 Modelwind fluctuations were computed at each point in the domain andnormalized to 2 to yield values in the range 0 to 1. a) year 2000; b) year2001.

44 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

of the wind direction with respect to the NCEP data, bothfor northerly and westerly winds. The Mykonos buoyshows prevailing north-northwest Aetesians. The modelshave quite a similar behavior, they simulate prevailingnortherly winds. In this case, QuikSCAT fails forcapturing the main peaks, it shows a broad maximumaround northerly direction. This fact could be explainedby the presence of many Islands, with problem for thescatterometer. The local topographic forcing couldexplain the systematic difference between buoy andmodels.

The analysis of the wind direction poses a series ofquestions: why do the different datasets at the westernsite behave worse as the spatial resolution degrades, atleast for coastal buoy? And why doesn't this happen atthe eastern site?

As already mentioned, the Côte d'Azur winds arecharacterized by the Mistral, and by southwesterlywinds. The Mistral is the main signal in the Lion buoy.While, the different models behave worse as the spatialresolution degrades for the Côte d'Azur buoy, the samemodels have good skill for the Lion site. The Lion buoyis along the main track of the Mistral flow, governed bylarge scale pressure pattern, and all the models representquite well the spatial structure originating the Mistral, asdemonstrated by the histogram of the wind direction. Onthe contrary, the Côte d'Azur buoy is affected by thetopography of the south of France and of the north ofItaly, and, in turn, by the interaction between the atmo-spheric flow and the topography. In this case, the wind,i.e. the Mistral is the product of the interaction of theatmospheric flow with a complex orography. The re-presentation of this interaction depends on the resolutionof the model which produces the analysis. At T63, theresolution of the NCEP reanalysis, the Rhone valley ispoorly represented. Then a lowering of the resolution ofthe model could explain the worsening of the Mistralrepresentation.

Considering the eastern buoy, all the data sets re-produce the peak in the northerly wind. The Aetesiansare caused by a large scale pattern, which is well capturedby all the analyses and reanalyses, i.e. the Aetesians arenot affected by the model resolution since they dependon a large scale forcing. On the other hand, the easterlywinds, apart from QuikSCAT and NCEPB, are notrepresented by either model.

4. Large domain comparison

Since only QuikSCAT and the models providegridded information to be used as a driver for oceanmodeling, we want to test the model's skill over a wider

area covering the entire Mediterranean sea. The pre-vious paragraph has shown the goodness of the Quik-SCAT data respect to in-situ measurements. So, thesatellite data become our “truth” to be compared withthe models' results.

One possible approach for this type of comparison ispresented by Perlin et al. (2004). Here, we consider thevector correlation as our measure for the comparisonbetween QuikSCAT and the numerical datasets. Welimit the analysis to the ERA40 dataset which, even if ithas shown some weakness in the coastal areas, has oftenbeen used as atmospheric forcing in many numericalsimulation of the Mediterranean circulation.

Vector correlations (Crosby et al., 1993) of thescatterometer and ERA40Model wind fluctuations werecomputed at each point in the domain and normalized to2 to yield values in the range 0 to 1 (Fig. 4). For thiscomparison, QuikSCAT winds were binned into theERA40 1 degree grid to match with the ERA40 spatialresolution. The number of model-observation pairs usedto compute the correlations exceeded 500 in the 88% ofsea grid points in both years while only in the 4.6% ofgrid points the number of pairs drops to less than 100both in 2000 (Fig. 4a) and 2001 (Fig. 4b). In general thespatial patterns of the statistical parameter in the 2 yearsare very similar.

Fig. 5. Linear fit (slope) between ERA40 and QuikSCAT over theMediterranean basin for year 2000.

45P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

As already observed by previous authors (Perlinet al., 2004) for different regions in the world the vectorcorrelation tends to decrease from more than 0.85 in theoffshore region to less than 0.5–0.6 near many coastalareas. This decrease is less evident in coastal regionsdominated by the Mistral in the gulf of Lion or Aetesianwinds in the Levantine basin. While, it is confirmed overthe Ligurian Sea (Côte d'Azur).

The vector correlation analysis for the ERA40 datasetconfirms what already observed for the in-situ compar-ison. The coastal area, where the interaction between theatmospheric flow and the topography dominates, showslow correlation values which may arise from the coarse-ness of the model respect to the process we want tosimulate.

5. Towards a new dataset for Mediterranean oceansimulations

The previous two sections analyzed the reliability ofseveral gridded dataset in reproducing the surface windover the Mediterranean basin. On the average, theQuikSCAT and the ECMWF analysis have shown thebest performance. These wind fields can be applied toforce ocean models over the Mediterranean region.Nevertheless, long climatic simulations need long timeseries of forcing which can be supplied only by the re-analysis products. Considering the surface wind of thetwo re-analyses, ERA40 shows slight improvementsrespect to NCEP, which has a poor performance. Inorder to produce a better forcing for long climate simu-lations, a possible solution could be to produce ablended dataset based on ERA40, correcting the mainbias. Basing on the good performance of the QuikSCATsurface winds at the buoy's locations (Fig. 3), an em-pirical method to correct the surface wind speed can beapplied: the slope of the ERA40 can be adjusted overeach grid point by the mean of the QuikSCAT data.

For each grid point of the Mediterranean Sea, aregression line between ERA40 and QuikSCAT has beenestimated for all the overlapping grid points and for theyear 2000. The slopes have been computed imposing thatintercept equals zero (to avoid negative wind speeds), andonly considering QuikSCAT wind values more intensethan 5 m s−1. The last assumption derives from theresults presented in Section 3 i.e. the overestimation ofQuikSCAT for wind speed less than 5 m s−1. In Fig. 5,we show the slope computed using ERA40 andQuikSCAT over the Mediterranean basin. The ERA40shows a good behavior over the southern Ionianbasin and over the Sicily channel, where slopes areclose to 1. Underestimation of the ERA40 wind speed

is observed in the western basin, in the Adriatic Sea, inthe northern Ionian Sea, and in the Levantine basin.On the average, the underestimation is of about 30%(mean slope, 1.34). We should note a strongerunderestimation over the Alboran sea, where ERA40never exceeds 5 m s−1 during all the year (2000). TheERA40 analysis, probably, does not represent the oro-graphic channeling of the wind between the Sra Nevadaand the Atlas mountain chain.

The wind speed error does not directly affect theocean circulation, but it is the wind stress and in turn thewind curl which forces the oceanic circulation. So, weshould take into account how the differences in the windspeed actually induce the differences in the wind stressand wind stress curl. If we consider a relative windspeed error between ERA40 and QuikSCAT (see Fig. 5)of about 30%, this produces differences in the wind-stress ranging from 0.02 N m−2 to 0.34 N m−2 for windspeed ranging from 5 to 20 m s−1. The relative errorgoes from 30% for the wind speed to 40% for the windstress. A computation of the Ekman pumping verticalvelocity using the ERA40 and QuikSCAT data, pro-duces a reduction of the vertical velocity of a factor 2.

Then, we empirically corrected the ERA40 windfield by applying the local value of the slope to theintensity of each vector (adjusted ERA40). The com-puted wind data sets have been then used to calculate theheat budget over the basin, as a preliminary test. Theknowledge of the heat fluxes between ocean and atmo-sphere is a requirement for understanding and modelingthe Mediterranean climate system. The air–sea heat fluxis characterized by four components: shortwave andlongwave radiation, latent and sensible heat fluxes.Different parametrization have been used to compute thesurface fluxes, using meteorological and oceanograph-ical data (bulk formulae, for a review see Krahmannet al., 2000, and references therein). Since the windspeed is a key meteorological parameter in the bulkformulae that compute latent and sensible heat fluxes,

46 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

these can be used to evaluate how the choice of aparticular wind dataset can affect the estimation of theannual Mediterranean heat budget.

As a first step, the net surface heat flux at the air–seainterface has been estimated in the Mediterranean basinusing a combination of ERA40 analysis (sea levelatmospheric pressure, 2 m air and dew point tempera-tures, cloud cover, zonal and meridional wind compo-nents) and Pathfinder AVHRR sea surface temperatures(for a detailed description of the parameterizations usedsee Appendix A in Marullo et al., 2003). The samecomputation has been performed using the adjustedERA40 winds, in order to evaluate the impact on theheat fluxes of the proposed empirical correction. Theresults are shown in Fig. 6.

The spatial patterns between the two datasets are quitesimilar. The most evident difference between the twoplots is a predominance of positive fluxes (i.e. the oceanabsorbs heat from the atmosphere) in the ERA40 case,with respect to the blended ERA40. In fact, whereas inthe ERA40 map the regions with negative budget areconfined to the Gulf of Lion and to the Aegean regionsonly, in the blended ERA40 case the ocean releases heatto the atmosphere over most of the basin, except for theSicily channel and the extreme western Mediterranean.Moreover, when the averaged total heat budget over theentireMediterranean basin is calculated, the results differ

Fig. 6. Total heat budget for the year 2000 over the Mediterranean sea.(a) Using ERA40 analysis, (b) using ERA40 modified using slopes ofFig. 4a. Units: W m−2. Positive fluxes mean a warming of the ocean,the opposite for negative fluxes.

noticeably, giving 23.8 W/m2 using ERA40 winds, and−5.77 W/m2 using blended ERA40 winds. The analysispresented here cannot be considered exhaustive, mainlybecause the study has been conducted only on a singleyear, and hypothesizing the wind to be the main errorsource in the heat budget computation. Nevertheless, theblended ERA40 derived Mediterranean heat budget iscloser to the climatology (Bethoux, 1979; Garrett andOuterbridge, 1993; Macdonald et al., 1994), whichsuggests a basin total budget ranging between −7and −5 W/m2. The difference between the two estima-tions is fairly relevant and it is clearly explained by theanalysis presented before i.e. underestimation of thewind by ERA40. The observed discrepancies on themean total budget and on the spatial distribution ofthe heat fluxes certainly will affect the climatic simu-lations, which are strongly dependent on such terms.

6. Summary and conclusions

Wind speed analysis from ECMWF routine assimi-lation system and from ERA40 and NCEP reanalysishave been compared to wind speeds derived fromQuikSCAT and to in-situ measurements (buoy-mountedanemometers). The comparison has been extended to ablended product, which corrects the NCEP reanalysisusing the QuikSCAT data. The analysis has been carriedout to verify the reliability of the analyzed wind speedsto be used as forcing for Mediterranean Sea simulations.As a final test, the effect of the different wind parameter-izations on the heat fluxes budget has been evaluated.

The comparison between the satellite winds and thesea truth data demonstrated the ability of the QuikSCATinstrument in the retrieving the dynamics of the windfields at the two buoy sites. The results of the matchupanalysis demonstrated that the winds obtained fromsatellite data reproduced the in-situ variability for boththe direction and the intensity. The only significantdiscrepancy is obtained for winds with magnitude lessthan 5 m s−1, for which it is well known that thescatterometers have minor problems.

The gridded data sets, when compared with the seameasurements, have shown a poorer accuracy respect toQuikSCAT, even if relevant differences are retrievedamong the models. The ECMWF wind field demon-strated the best performances in both the regions ex-amined, while the results are becoming increasinglyweaker considering the ERA40 and NCEP data. Notice-ably, for the NCEP and the ERA40, the weak per-formances concerned both wind direction and intensity,resulting in a general degradation of the truthfulness ofthe wind speed field for these two data sets, particularly

47P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48

for the NCEP. On the other hand, the NCEPB data sets,derived blending NCEP and QuikSCAT data, produced arelevant amelioration of the NCEP wind field, demon-strating that the method could significantly improve thewind retrieval.

The results of the matchup analysis highlight theproblems of the available wind data sets in the Medi-terranean. Even though the study has been mainly con-ducted on three sites only (two buoys in the Aegean Sea,one in the Ligurian Sea and one in the Guf of Lion), theresults obtained could be considered of general ap-plicability in the basin. Indeed, the buoys are located inregions where the wind field exhibits strong variability,being exposed to the regimes of the Mistral and of theAetesians, two among the most important winds of theMediterranean area.

The spatial resolution of the data sets is supposed tobe one of the main relevant sources of error in theanalyzed wind fields, explaining the worst results of thereanalysis data and the relative accuracy of the ECMWF.

The NCEPB datasets deserve particular consideration.In fact, our study demonstrated that the blending methodsignificantly increases the truthfulness of the starting dataset: NCEP alone was not able to capture some of theimportant features of the wind regime at the buoy sites,while NCEPB did, mainly exploiting the enhancedresolution and accuracy of the QuikSCAT data.NCEPB, however, remains affected by relevant errorswhen compared with in-situ data. Our results suggest thatthis could be ascribed to NCEP's poor spatial resolution,which is demonstrated to have an important role indetermining the performances of the gridded data sets.

In our opinion, it is important to explore the degree ofamelioration of a model-derived wind field, when ablending method is applied to a relatively high spatialresolution data set (i.e. ERA40), but with long temporalextension to be used for forcing ocean climatic simu-lations. Without reproducing the sophisticated and com-plexmethodologies applied to build up the NCEPB fields,we retrieved for each ERA40 grid point a linearrelationship relating ERA40 and QuikSCAT data. As afinal analysis, we verified the impact of the choice of thewind data set on the computation of the Mediterraneanheat budget. The point is quite crucial for the simulation ofthe basin physical dynamics via OGCMs. In fact, it wasdemonstrated (Artale et al., 2002) that the outputs of thesame model, forced with even slightly different windfields, can differentiate substantially. This is not sur-prising, because the wind field plays an important role indetermining the momentum and heat fluxes, which pri-marily force the oceanmodels. For the ERA40wind fieldsand its blended product, the total heat budget of the

Mediterranean has been calculated via bulk formulas, andthe resulting fields have been compared with previousestimates. The analysis cannot be considered exhaustiveand complete mainly because it has been conducted forthe year 2000 only. However, the blended product resultsrealistic in the heat budget estimations, and, comparedwith the basic products, produced an evident amelioration.

Acknowledgements

We thank the JPL PO.DAAC that makes theQuikSCAT data freely available, Dr. K. Nittis for theSantorini and Mykonos buoys data and METEO-FRANCE for the ODAS-03FR Côte d'Azur buoy data.We thank an anonymous reviewer. We would also like tothank Dr. R. Evans, Dr. F. Bignami and Dr. R. Santolerifor the helpful comments. The NCEP reanalysis data wasprovided to us by NOAA's Climate Diagnostic Center.QSCAT/NCEP Blended Ocean Winds have beenobtained from Colorado Research Associates http://dss.ucar.edu/. ECMWF ERA-40 data used in this study havebeen obtained from the ECMWF data server. The workwas carried out with the support of the Agenzia SpazialeItaliana (ASI I/R/206/02).

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