Available online at www.sciencedirect.com Journal of Marine Systems 70 (2008) 33 – 48 www.elsevier.com/locate/jmarsys Comparison of analyzed and measured wind speeds in the perspective 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 b a Centro Ricerche Casaccia, ENEA, S. Maria di Galeria (Rome), 00060, Italy 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 2007 Available online 12 March 2007 Abstract Surface vector wind datasets from different assimilation systems and from scatterometers have been recently made available over the entire Mediterranean basin and for a large spectrum of spatial and temporal resolution. In this work, we compare wind vector analyses, derived from different routine assimilation systems and from blended products, to wind vectors obtained from QuikSCAT satellite sensor and to those directly measured by buoy-mounted anemometers. The analysis has been performed to verify the accuracy of the analyzed data, when the specific objective is the generation of surface winds field to force Mediterranean Sea simulations. The inter-comparison covers the period 2000–2005. Our analysis demonstrated that the spatial resolution of the data sets represents one of the main relevant sources of error in the analyzed wind fields, explaining the worst results of the reanalysis data and the relative accuracy of the ECMWF. This work also confirms the usefulness of blending QuikSCAT and reanalysis products, which could be used to force oceanic simulations. The blended data cover the period from July 1999 to present when QuikSCAT wind data are available. Before this period, blended products are not produced and different solutions to correct wind speed from routine assimilation systems have to be investigated. A simple empirical method to adjust the ERA40 wind speed product is then proposed. The analysis of the difference between the annual Mediterranean heat budget computed using the adjusted and the original ERA40 winds suggests that the impact of the correction is not negligible. Considering the year 2000, the annual 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 forcing fields for oceanic model runs is the surface atmospheric wind (operationally defined at 10 meters from the sur⁎ Corresponding author. Tel.: +39 06 30484886; fax: +39 06 30484264. E-mail address: email@example.com (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 and turbulent flux fields. This implies that errors in the determination of the wind term can alter the model forcing and have an impact in the output of the ocean circulation models (Myers et al., 1998). Oceanic models are generally forced at surface by a combination of radiative and momentum fluxes, which drive the transfer of energy between the atmosphere and the sea. The wind acts in both 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. It represents then a crucial factor to realize realistic oceanic simulations (see for example the review of Large et al., 1994). Moreover, the consistency of the wind parameter as oceanic forcing depends also by the spatial and temporal resolutions, which needs to be adequately refined to avoid model's divergences or unrealistic outputs (Ji and Smith, 1995; Chen et al., 1999; Kelly et al., 1999). Fig. 1. Central panel: Mediterranean basin with orography and buoy locations (A1 = Lion, B1 = ODAS Côte d'Azur, A2 = Mykonos, B2 = Santorini). QuikSCAT, ECMWF, NCEPB and ERA40 grid meshes for Côte d'Azur (upper panels) and Santorini (lower panels) buoy sites. The center of each square represents the grid point of the models, while the small box around the buoy location represents a 0.15 degree box centered at the buoy site. [The Mediterranean map is from www.cls.fr/mater/mater_results_v1.htm] P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 In the Mediterranean, the importance of the accuracy and of the spatial and temporal resolution of the wind forcing has been already highlighted in the context of both marine operational forecasting (Bargagli et al., 2002; Pinardi et al., 2003) and climate simulations (Castellari et al., 2000; Artale et al., 2002). High spatial resolution wind is needed mainly because the Mediterranean basin is surrounded by a complex orography (Fig. 1), which strongly influences the atmospheric flows (ranging from local to synoptic scales) and, in turn, the surface forcing fields for oceanic simulations. Presently, the available wind datasets, covering the Mediterranean basin for the last few decades, derive from two main sources: i) the winds measured by satellite-mounted scatterometer instruments (e.g. QuikSCAT or ERS); ii) the products of global assimilation systems, which include, for example, the operational analysis of the European Center for Mid-Range Weather Forecast (ECMWF). Moreover, the National Center for Environmental Prediction (NCEP), in collaboration with the National Center for Atmospheric Research (NCAR) (Kalnay et al., 1996), and the ECMWF (Simmons and Gibson, 2000) have released re-analysed datasets for the time frames 1948–today and 1957–2002, respectively, answering the need for homogeneous long time series produced at the same resolution (vertical and horizontal) and with the same procedure. To increase their accuracy, reanalysis products have been also “corrected” by the use of some statistical and spectral properties of the wind fields, derived analyzing experimental data (i.e. scatterometer). Among the others, Chin et al. (1998) corrected NCEP re-analysis data using QuikSCAT observations, improving the performances of global ocean simulation models when corrected winds are used (Milliff et al., 1999). The assessment of the errors of the wind products in the Mediterranean sea is then a pre-requisite to obtain realistic and truthful simulations of the basin circulation. So, an accurate exercise of comparison with experimental data collected in the region is then required. In the past, comparisons between re-analysis or analysis products and surface measurements have been performed at some ocean sites (in and out the Mediterranean area), often giving contradictory conclusions. Weller and Anderson (1996), comparing buoy and analysis winds during the COARE-IFA experiment, verified that ECMWF data underestimate wind speed in the tropical Pacific. Conversely, Weller et al. (1998) demon- 35 strated that the ECMWF provides realistic winds when compared to in-situ time series measured off the Oman coast. These differences could be explained considering the regional aspects of the atmospheric flow and of the air–sea interaction, and confirm that the reliability of the wind products could be strongly dependent on the investigated area. In the Mediterranean area, Bozzano et al. (2004) compared sea winds data from a single buoy in the Ligurian Sea (Northwestern Mediterranean Sea) with ECMWF products. They concluded that the ECMWF overestimates the measured wind for calm conditions and underestimates the experimental data for near gale and gale conditions. However, the results of the cited works are restricted to the few oceanic sites where in-situ data are available, de facto limiting their applicability to larger ocean regions. The use of satellite products minimizes the problem, offering wind fields data with a world-wide coverage and with an high spatial and temporal resolution. However, also satellite data needs a validation effort, to characterize the overall accuracy and precision of the satellite derived datasets (Freilich and Dunbar, 1999; Mears et al., 2001). In particular, winds obtained from the QuikSCAT scatterometer, which will be used in the follow, have been validated with in-situ buoy or ship data over several ocean locations (Draper and Long, 2002; Ebuchi et al., 2002; Bourassa et al., 2003; Freilich and Vanhoff, 2003; Chelton and Freilich, 2005). In the Mediterranean area, a comparison between QuikSCAT scatterometer, buoy and ECMWF analysis winds has been performed in the framework of the Mediterranean Forecasting System Toward Environmental Prediction (MFSTEP) project (Pinardi et al., 2003), highlighting an underestimation for strong winds of the ECMWF analysis and an 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 of the different wind data sets in the Mediterranean Sea is described. The QuikSCAT and analyzed wind vectors errors will be assessed by comparison with high quality in-situ surface buoy observations. The evaluation will focus on four selected Mediterranean sites: one in the Ligurian Sea, one in the Gulf of Lion, and two in the Aegean Sea. The choice of the sites was determined by the following considerations: 1. In the selected sites, four meteorological buoys are deployed, and the collected data are available. 2. Most of the buoys cover an entire year, without relevant 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 important Mediterranean winds are observed, and where relevant air–sea interactions occur. This will allow us for evaluating the performance of the selected data sets over a wide range of wind conditions. The paper is organized as follows: the surface wind datasets are introduced in the next section. In this work, several datasets have been considered: near-surface winds computed by both the operational ECMWF and ERA40 assimilation systems, NCEP reanalysis, nearsurface winds derived from satellite microwave measurements (QuikSCAT) and winds measured by four buoys moored in the western and eastern Mediterranean basins. A blended data set, merging NCEP reanalysis and QuikSCAT data, has been also evaluated. The collocation procedures and the impact of the wind speed correction for the buoy data are also described. In Section 3, gridded analysis and remotely measured wind are compared with buoy measurements. In Section 4, a selected gridded data set has been compared with the QuikSCAT winds, over all the Mediterranean basin. Section 5 presents a simple empirical method to correct long wind speed time series and a discussion on the advantages of a new blended product are examined in the context of Mediterranean heat budget (Section 5). A summary is finally provided in Section 6. 2. Surface wind datasets The comparison of the selected wind datasets is based on the period covering the years 2000 to 2005, being the buoys data available for the year 2000 (Mykonos, Santorini, Azur) and for the years 2002 to 2005 (Lion). The modeled data sets (ECMWF, ERA40, NCEP and NCEP-QuikSCAT) have bee compared against the buoys data, when all the models are available, i.e. the year 2000 for the Mykonos, Santorini and Azur buoys, and the year 2002 for the Lion buoy, since ERA40 is not available after 2002. Regarding the QuikSCAT data, the comparison against buoy data has been performed when both the data are available: 2000 for the Mykonos, Santorini and Azur cases, and since 2002 to 2005 for the Lion case. 2.1. Satellite data QuikSCAT measures the sea surface radar crosssection σ0 for several different azimuth angles for both horizontally and vertically polarized radiation. The data are fitted to a geophysical model function that describes the expected σ0 as a function of wind speed and direction 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 the actual 10 m wind speed, but these differences are usually less then 0.5 m s− 1 (Bourassa et al., 2003). The presence of rain in the atmosphere can affect σ0. At low wind speeds the scattering from rain drops dominates with respect to the scattering due to the wind action over the sea surface, increasing the wind estimate so that a rain flag is necessary to reliably use the QuikSCAT data. In this work, QuikSCAT Level 3 scatterometer sea winds are used, which consist of gridded values of scalar wind speed, meridional and zonal components on an approximately 0.25 × 0.25 degree resolution. One of the objectives of the present paper is the evaluation of wind data as forcing of oceanic simulations. For this reason, QuikSCAT gridded level-3 products rather than the level-2 swath winds are deliberately selected for the comparison, being the firsts the most used by ocean modelers. The data are provided by the JPL PO-DAAC and include rain flags as an indicator of wind value degradation (Physical Oceanography DAAC, Guide Document, 2001). Only observations for which the rain flag algorithm does not detect rain are retained and are considered in the following comparison exercise. Since 22 January 2002, near-surface wind information observed by QuikSCAT has been assimilated in the operational 4D-Var system at ECMWF (Hersbach et al., 2004). Therefore, the analysis for the years before 2002 is not affected by QuikSCAT assimilation. 2.2. Gridded model data 2.2.1. ECMWF analysis The ECMWF data for the year 2000 consist of 6 hourly analyzed winds produced by the operational cycle CY21r4 of the Integrated Forecast System at the ECMWF, operational since October 1999 (Jakob et al., 2000). For the year 2000, the assimilation system uses the ECMWF model at the triangular truncation T319 (about 60 km) and from November 2000 at the triangular truncation T511 (40 km). The system includes 60 vertical levels. Since 22 January 2002, an upgraded version of the model, CY24r3, was implemented. This version includes several important changes that affect all components of the system (data assimilation, atmospheric and oceanic wave forecasts, EPS; for details see ECMWF Newsletter 93). Regarding the assimilated data, several improvements have been activated (assimilation of QuikSCAT data, less thinning of aircraft observations, more intelligent thinning and better scan correction of ATOVS radiances). P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 2.2.2. ECMWF re-analysis ERA40 winds have been produced using the Integrated Forecasting System developed jointly by ECMWF and Météo-France (Simmons and Gibson, 2000). The three-dimensional variational assimilation of observations is used, and the assimilating model has T159 spectral resolution in the horizontal and 60 levels in the vertical. The analysis has been produced every 6 h (http://www.ecmwf.int/research/era/) and data have been released from the period 1962–2002. The ERA40 assimilation system does not use the QuikSCAT data. 2.2.3. NCEP re-analysis The NCEP re-analysis has been produced at the National Center for Environmental Prediction, in collaboration with the National Center for Atmospheric Research (NCAR; Kalnay et al., 1996). The surface winds are available four times per day on a Gaussian grid consistent with T62 resolution (i.e., triangular truncation, admitting 62 zonal wavenumbers). The grid has a resolution of almost 1.875° lon per 1.9° lat. NCEP Re-analysis data have been provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, USA, from their Web site at http://www.cdc. noaa.gov/. The NCEP-reanalysis assimilation system does not use the QuikSCAT data. 2.2.4. NCEP-blended Global 6-hourly maps of ocean surface winds are derived from a space and time blend of QuikSCAT scatterometer observations and NCEP re-analyses (hereafter referred as NCEPB). The blending method creates global fields by using QuikSCAT wind in swath regions, and modifying the NCEP fields in the regions not covered by satellite. The method adds to the lowwavenumber NCEP fields a high-wavenumber component, which is derived from monthly regional QuikSCAT statistics. The final blended product has a spatial resolution of 0.5° × 0.5°, and a global coverage from 88° S to 88° N. A detailed description of the blending procedure can be found in Chin et al. (1998), while the rain effects on QuikSCAT surface wind retrievals and on the NCEPB are explained in Milliff et al. (2004). The NCEPB ocean winds product has provided by Colorado Research Associates, Boulder, Colorado, USA, and are available from the Web (http://dss.ucar.edu/datasets/ds744.4/). 2.3. Buoy data The in-situ data used for the comparison are obtained from four buoys located in two different regions of the 37 Mediterranean sea (Fig. 1). Two buoys are managed by the Greek National Center for Marine Research as part of the POSEIDON system and are positioned in the Aegean Sea, the first near to the Island of Santorini (36.°16′ N, 25.°29′ E), the second near the Island of Mykonos (37.51°N, 25.46°E). The data acquisition scenario of the POSEIDON system provides observations every 3 h. The other two selected buoys are managed by Météo-France and are deployed in the northwestern Mediterranean Sea. More precisely, the ODAS-03FR buoy (Azur buoy in the following) is moored in the Ligurian Sea, (43°22′ N, 7° 51′ E), and the Gulf of Lion buoy (Lion in the following) is positioned in the Gulf of Lion (42.1° N and 4.7°E). The height of the POSEIDON and ODAS buoymounted anemometers is 3.2 m. The wind measurements are averaged over 10 min every hour for Côte d'Azur and Lion buoys and every 3 h for Santorini and Mykonos buoys. Details on the instrumental characteristics and on the sampling protocols of the two buoys are given by Nittis et al. (2002) for the Santorini and Mykonos buoys, while for the Azur and Lion buoys are summarized in the following. The Meteo-France buoys use a three cup anemometer (Vector Instruments A100 L2) and a self referencing wind vane (Vector Instruments SRW1GM) for wind measurements. The wind measurements are averaged over 10 min every hour. The average wind speed is given by the simple scalar average of the number of impulsions issued from the anemometer (10 Hz/kt). A vector average is used to calculate the mean wind direction. During 10 min, every 28 turns of the anemometer (the wind vane data are collected. The “u” and “v” components are then calculated for each observation. Next, the average of the “u” and “v” components are computed and the average wind direction is obtained from “arctan(u / v)”. 2.4. Collocation procedure To compare satellite and model wind data with buoy observations, matchup datasets of collocated (in space and time) wind pairs were produced. For each data set, a 0.15 degree square box centered on the buoy location has been considered. Following Mears et al. (2001), the dimension of the box is chosen so that if the buoy is near to the center of the QuikSCAT pixel, only that pixel is considered. If a pixel of the data set embeds entirely the square box of the buoy location, that particular pixel will be selected for the comparison. On the other hand, if the buoy box overlaps two or more data pixels, a weighted 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 the buoy location. Sample cases relative to the selected data sets are shown in Fig. 1, where the box positions and the selected pixels are indicated, for each box location and for each spatial resolution of the data. In the case of the Côte d'Azur and Lion buoys, the 0.15 degree square box of the buoy is totally within one QuikSCAT pixel, while for the case of Santorini and Mykonos systems the box overlaps 4 QuikSCAT pixels (Fig. 1). Regarding the temporal collocation, the gridded model-derived data sets match exactly with the buoy measurements (hourly for the Côte d'Azur and Lion, and every 3 h for Santorini and Mykonos), resulting in 4 matchups per day. The variable timing of the QuikSCAT observations requires a specific criterion to collocate in time satellite and buoy measurements. As the Côte d'Azur and Lion buoys wind are available hourly, and the Santorini and Mykonos buoys wind 3-hourly, the QuikSCAT over flight time is no more than 30 min and 90 min distant from the closest buoy observation, respectively. Thus, the time lag between the QuikSCAT over flight time and the closest buoy measurement varies in the range 0– 30 min (0–90 min) for Côte d'Azur and Lion (Santorini and Mykonos) buoys. A scatterometer data is then retained as a matchup when his temporal distance with a collocated in-situ observation is comprised in the timerange 30 to 90 min. In fact, the difference between satellite and buoy wind measurements is uniformly distributed as function of the collocation time step, supporting our temporal matching procedure. Additional consideration concerns the optimal processing 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 buoy data should depend on the spatial resolution of the comparison data sets. Considering a typical phase velocity of 5–10 m s− 1 for Mediterranean cyclones, buoy data should be averaged over 30–60 min when compared with scatterometer winds and over even longer times for ECMWF and NCEP analysis. Unfortunately, we only have buoy wind data averaged over 10 min. Thus, unresolved variability of the wind speed in the QuikSCAT footprint or in the grid box spatial average of analyzed winds can result in wind underestimation respect to the buoy data. The order of magnitude of this underestimation, as function of the spatial scale has been estimated by Levy (2000). He found that, for grid scales between 25 km and 250 km the sub-grid unresolved velocity scale should be in the range between few tenths of m s− 1 and about 1.5 m s− 1. Thus, in the comparison exercise only differences over this threshold should be considered significant. The described matchup procedure has been applied over the entire measurement period, resulting in 580 matchup points for Santorini, 365 matchup points for Mykonos, 370 matchup points for Côte d'Azur and 1890 matchup points for Lion. 2.5. Impact of wind speed correction on the in-situ data Surface observations measure the actual wind speed at the height of the anemometer instrument, which is, for buoy systems, typically located between 3 and 10 m (in our case 3.2 m). To achieve the comparison with the satellite or model estimates, the buoy derived winds have been converted to the equivalent neutral wind speed at a height of 10 m above sea level for comparisons with QuikSCAT observations or to the actual 10 m wind speed for comparisons with model estimate. The relationship that yields the measured wind speed at different heights is a function of air turbulence, which is, in turn, determined by the wind shear and by the buoyancy of the atmosphere (Garratt, 1992), that is strictly dependent on the vertical density stratification. Two methods have been developed to account for the described processes (Mears et al., 2001). In the first, a simple approach assumes a logarithmically varying wind vertical profile, so that the corrected wind speed at a height z is given by ULOG ðzÞ ¼ lnðz=z0 Þ=lnðzm =z0 Þ4U ðzm Þ In the expression, derived using a mixing-length approach and assuming neutral stability conditions, U(z) is the wind speed at a height z, zm is the measurement height and z0 is the roughness length (a typical oceanic value for z0 is 1.52 × 10− 4 m, Peixoto and Oort, 1992). This method does not take account of the atmospheric stratification and then can lead to significant errors in the extrapolation. A second method, named “neutral stability correction” (Liu and Tang, 1996), permits to vertically extrapolate the wind data with a minor uncertainty and it was then adopted here. The procedure requires air and sea surface temperatures, surface pressure and nearsurface relative humidity. The whole set of these parameters is, however, not always available, as the case for example of our buoy Santorini. It is then important to define the range of applicability of the two correction methods and to investigate the possible sources of the error when the less accurate log method is used. As a preliminary analysis, we consider a buoy for the western basin (Côte d'Azur) and a buoy for the eastern P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 39 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 buoy data only; b) Côte d'Azur, using ECMWF analysis; c) Santorini, using ECMWF analysis. basin (Santorini). Fig. 2a shows a scatter plot of the difference between Liu- and Log-corrected wind speeds as a function of measured wind intensity, for the Côte d'Azur buoy. The main difference is observed for strong winds (roughly above 15 m s− 1), where the log correction differs from the neutral stability correction by less than 0.5 m s− 1. In the North Western Mediterranean Area then it is possible to argue that, in the range of variability of the measured winds, the application of the logarithmic method does not introduce large errors. The unavailability of some of the parameters required to apply the neutral stability correction method at Santorini poses some problems in order to evaluate the difference between the two methods at this location. A possible solution could be to use the ECMWF analysis data to perform the analysis for Santorini. Thus, we first evaluate the skill of the ECMWF analysis at Côte d'Azur site (Fig. 2b), and then we compare the methods at Santorini (Fig. 2c). Fig. 2b shows the good performance of the ECMWF analysis compared to the in-situ data (Fig. 2a). The comparison of Fig. 2c with the other figures suggests that the log correction method does not produce large errors for the two sites, and that the bias attains the value of about 0.5 m s− 1 only for strong winds. 3. In-situ comparison In this section, we compare wind speeds and wind directions measured at the buoy sites against the corresponding gridded models (ECMWF, ERA40, NCEP, NCEPB) and satellite data (QuikSCAT). A statistical comparison has been performed using scatter diagrams, histograms and standard parameters (Mean Bias Error — MBE, Root Mean Square Error — RMSE, correlation coefficient R, slope and intercept of the regressed line). The comparison for the wind speed at the Côte d'Azur site is shown in Fig. 3, first column. The wind speed at the buoy site versus the wind speed measured by QuikSCAT is shown in Fig. 3-a1. The QuikSCAT observations, except from calm and light winds (b 5 m s− 1), are in good agreement with the buoy data. Taking into account only winds less than 5 m s− 1, the QuikSCAT data overestimate the measured wind. In fact, low wind speeds are unable to overcome the viscous damping and the Bragg waves cannot grow, so no microwave backscatter can be detected over the noise level (Plant, 2000). More specifically, the bias between the two wind measurements tends to zero for high winds, implying that the deviation of the slope from unity is essentially due to the overestimation at low wind speed. Considering the statistical parameters (Table 1), the wind speed correlation is 0.93, while the MBE and RMSE are respectively 0.59 m s− 1 and 1.5 m s− 1. Buoys and scatterometer correlate closely, as expressed by a slope of about 0.89 and intercept close to 1 m s− 1. The scatter diagrams for the other gridded model datasets are shown in Fig. 3b1–e1. The ECMWF data have the best agreement, with the closest to one slope (0.65), the higher correlation coefficient (0.81) and the lower RMSE (2.59 m s− 1). Nevertheless, the ECMWF data underestimate strong winds (N 10 m s− 1). The ERA40 data (Fig. 3-c1) show a strong underestimation for winds higher than about 5 m s− 1. Although the spread of the data points around the regression line is similar to that observed for ECMWF, the RMSE is higher (3.87 m s− 1) and the correlation coefficient is lower (0.64). Fig. 3-d1 shows the scatter diagram for the NCEP data. In this case the skill is quite low, with the lowest 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 buoy measurements for light winds and underestimate winds higher 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 overestimation. The RMSE is 3.49 m s− 1, lower than ERA40 and NCEP, while the correlation coefficient (0.61) is significantly higher than for NCEP. The Lion buoy shows a general improvement for all the datasets (Fig. 3b5–e5, and Table 2). The analysis of the correlation coefficient, MBE and RMSE depicts an overall improvement of the different datasets respect to the results of the Côte d'Azur, or in one case no relevant variations (NCEPB). A possible reason could be attributed to the off coast position of the Lion buoy. The Côte d'Azur could be affected by coastal topographic forcing more than the companion buoy, and these local effects could be misrepresented by the General Circulation Models used by the assimilation systems. The QuikSCAT performance is practically unchanged for the two buoys. Notice that, according to Chelton and Freilich (2005), the biases in the scatterometer versus buoys and in the models versus buoys are of opposite signs. This will have an impact on the computation of the heat budget. The analysis performed for the Santorini buoy is shown in Fig. 3, third column. The QuikSCAT data behave as in the Côte d'Azur case, i.e. in very good agreement, except for winds lower than 5 m s− 1. The good performance is confirmed by the value of the correlation coefficient (0.87) and of the RMSE (1.90 m s− 1) (Table 3). The ECMWF scatter (Fig. 3-b3) shows a behavior quite similar to that shown in Fig. 3-b1 for the western 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 the western basin, a slightly different behavior is observed for the ERA40 (Fig. 3-c3), NCEP (Fig. 3-d3) and NCEPB (Fig. 3-e3). ERA40 shows a higher slope (0.54) and, reduced MBE (− 1.33 m s− 1) and RMSE (2.89 m s− 1) respect to the Côte d'Azur, demonstrating the tendency to improve the wind simulation over the Aegean Sea. At the Santorini site, both NCEP and NCEPB wind speeds have a reduced spread around the regression line and an improvement of all the statistical parameters (see Table 3). The analysis of the Aegean Sea buoys is completed by using the data from Mykonos. The analysis of the scatter plots (Fig. 3a7–e7) and of the statistical parameters (Table 4) shows similar results to what observed in Santorini, but with a sensible increase of the MBE and RMSE. In this case, the location of the buoy and the shorter period of the time series could affect the Mykonos result. In order to complete the analysis and to identify possible reasons for the differences between western and eastern basins, a comparison on the wind directions have been performed (Fig. 3 pair columns). The measurements are divided into bins of 5° wide and a wind direction histogram has been plotted for each site and for each matchup data set. Each histogram is then divided in two 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 first prevailing direction corresponds to the Mistral, a strong northwesterly wind, which blows through the Garonne and Rhone valleys and then into the Gulf of Lion (for a detailed description of the main Mediterranean winds see also Zecchetto and Cappa, 2001). The regional pressure pattern, which drives the Mistral, is characterized by a pressure low over the Aegean Sea and a pressure high covering the Spain and the Atlas region. The low level flow blows into the Gulf of Lion from northwest, but a northern flow is present over the Italian peninsula and its deformation due to the Island forcing (Sardinia and Corsica) produces northeasterly winds over the Ligurian Sea. The second relevant direction represents a wind blowing from southwest, which could be associated with atmospheric highs entering in the Gulf of Lion from the west or southwest, and stationing over the Gulf of Genova. The flow is channeled along the coast and between the Ligurian topography and the Corsica Island. This atmospheric pattern explains the strong coastal effects observed in the Côte d'Azur buoy. QuikSCAT Fig. 3. Comparison between buoy and QuikSCAT wind data (first row), and between buoy and modeled wind data (second to fifth rows). Scatter plots of wind intensity in the Ligurian Sea (first column) and in the Aegean Sea, Santorini, (third column). Histograms of wind direction in the Ligurian sea (second column) and in the Aegean Sea (forth column). a1–a4) QuikSCAT versus buoy measurements. b1–b4) ECMWF versus buoy measurements. c1–c4) ERA40 versus buoy measurements. d1–d4) NCEP versus buoy measurements. e1–e4) Blended NCEP versus buoy measurements. 3bis. Comparison between buoy and QuikSCAT wind data (first row), and between buoy and modeled wind data (second to fifth rows). Scatter plots of wind intensity in the Gulf of Lion (first column) and in the Aegean sea, Mykonos, (third column). Histograms of wind direction in the Ligurian sea (second column) and in the Aegean Sea (forth column). a5–a8) QuikSCAT versus buoy measurements. b5–b8) ECMWF versus buoy measurements. c5–c8) ERA40 versus buoy measurements. d5–d8) NCEP versus buoy measurements. e5–e8) Blended NCEP versus buoy measurements. P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 (Fig. 3-a2) has a good skill in reproducing the prevailing directions, with a slight underestimation of the winds blowing from northeast. For the southwesterly winds, a 41 small shift is also evident in the histogram: buoy peak is in the interval −140° to −135°, while QuikSCAT peak is in the interval −120° to −115°. 42 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 Fig. 3 (continued ). P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 43 Table 1 Statistical parameters for the wind speed: Mean Bias Error — MBE, Root Mean Square Error — RMSE, correlation coefficient R, slope and intercept of the regressed line Table 3 Statistical parameters for the wind speed: Mean Bias Error — MBE, Root Mean Square Error — RMSE, correlation coefficient R, slope and intercept of the regressed line Côte d'Azur Intercept m s− 1 Slope CorrC MBE (m s− 1) RMSE (m s− 1) No. of pairs Santorini Intercept m s− 1 Slope R MBE (m s− 1) RMSE (m s− 1) No. of pairs QuikSCAT ECMWF ERA40 NCEP NCEPB 1.17 1.18 1.55 3.65 2.78 0.89 0.65 0.37 0.33 0.57 0.93 0.81 0.64 0.43 0.61 0.59 − 0.97 − 2.23 − 0.41 0.16 1.50 2.59 3.87 3.97 3.49 369 1249 1249 1249 1249 QuikSCAT ECMWF ERA40 NCEP NCEPB 1.88 2.38 1.60 2.33 2.78 0.75 0.61 0.54 0.48 0.67 0.87 0.77 0.73 0.63 0.72 0.24 − 0.14 − 1.33 − 0.99 0.64 1.90 2.39 2.89 3.13 2.82 580 1454 1454 1454 1454 Côte d'Azur buoy. Santorini buoy. The histograms for the other gridded model datasets are shown in Fig. 3, b2–e2. ECMWF displays a reasonably good performance, with only a slight northward shift of the direction for the easterly part. The ERA40 data capture the two main peaks observed in the buoy measurements, though relevant errors are still evident. In fact, ERA40 generates easterly more than northeasterly winds and westerly more than southwesterly winds. The error could be caused by the coarser resolution of the ERA40 model. This effect is even more evident in the histogram of the NCEP data, which does not show any preferential direction. The error in the direction appears to increase as the resolution degrades. On the other hand, the same data “corrected” by the use of QuikSCAT winds (i.e. the NCEPB) significantly increase their skill (Fig. 3-e2). We can argue that the improvement in the wind direction of the NCEPB data, is mainly due to the use of QuikSCAT data instead of the NCEP reanalysis, when the former are available. The Lion buoy is on the track of the Mistral main flow. Evidence of this comes from the unimodal peak in the direction histogram, a northwest predominant direction. All the models are able to capture the main Mistral flow. The large scale pressure pattern generating the Mistral is well reproduced by all the assimilation systems while, coastal features are strongly dependent on the resolution of the atmospheric model. So, considering both the western buoys, the ECMWF analysis has the best skill. The analysis for Santorini is shown in Fig. 3, fourth column. The comparison between QuikSCAT and the buoy data (Fig. 3-a4) shows a predominance of winds from north-northwest and west. In the summer months, the winds in the Aegean region are predominantly from the north (Aetesians). These winds begin to blow in May and June, reach full strength in July and August, and die off in September and October. The Aetesians are a consequence of a pressure gradient between a low pressure area over Pakistan (the Asian monsoon low), which extends its influence as far as the eastern Mediterranean, and the high pressure area over the Azores, which affects the western Mediterranean. The pressure gradient between these two stable pressure areas produces the constant northerlies observed in summer. Also for this site, the QuikSCAT displays a good skill in reproducing the wind direction. Both ECMWF and ERA40 (Fig. 3, b4–c4) reproduce well the main directions, although they overestimate the northwesterly component and they underestimate the westerly one. In the case of the NCEP wind direction distribution (Fig. 3-d4), the westerly winds are missed and the Aetesians are slightly more northeasterly than northwesterly. Once again, the blended product (NCEPB, Fig. 3-e4) improves the estimate Table 2 Statistical parameters for the wind speed: Mean Bias Error — MBE, Root Mean Square Error — RMSE, correlation coefficient R, slope and intercept of the regressed line Table 4 Statistical parameters for the wind speed: Mean Bias Error — MBE, Root Mean Square Error — RMSE, correlation coefficient R, slope and intercept of the regressed line Gulf du Lion Intercept m s− 1 Slope CorrC MBE (m s− 1) RMSE (m s− 1) No. of pairs Mykonos Intercept m s− 1 Slope CorrC MBE (m s− 1) RMSE (m s− 1) No. of pairs QuikSCAT ECMWF ERA40 NCEP NCEPB 0.02 0.66 0.77 1.60 3.38 1.06 0.85 0.60 0.70 0.54 0.97 0.92 0.84 0.78 0.52 0.52 − 0.52 − 2.28 − 0.81 − 0.19 1.44 1.77 3.33 2.90 4.36 1890 1428 961 1428 1428 QuikSCAT ECMWF ERA40 NCEP NCEPB 1.92 1.49 1.07 2.64 3.62 0.71 0.64 0.56 0.38 0.51 0.86 0.84 0.79 0.56 0.62 − 0.59 − 1.57 − 2.71 − 2.68 − 0.58 2.33 2.83 3.78 4.56 3.48 356 810 810 810 810 Gulf of Lion buoy. Mykonos buoy. 44 P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 of the wind direction with respect to the NCEP data, both for northerly and westerly winds. The Mykonos buoy shows prevailing north-northwest Aetesians. The models have quite a similar behavior, they simulate prevailing northerly winds. In this case, QuikSCAT fails for capturing the main peaks, it shows a broad maximum around northerly direction. This fact could be explained by the presence of many Islands, with problem for the scatterometer. The local topographic forcing could explain the systematic difference between buoy and models. The analysis of the wind direction poses a series of questions: why do the different datasets at the western site behave worse as the spatial resolution degrades, at least for coastal buoy? And why doesn't this happen at the eastern site? As already mentioned, the Côte d'Azur winds are characterized by the Mistral, and by southwesterly winds. The Mistral is the main signal in the Lion buoy. While, the different models behave worse as the spatial resolution degrades for the Côte d'Azur buoy, the same models have good skill for the Lion site. The Lion buoy is along the main track of the Mistral flow, governed by large scale pressure pattern, and all the models represent quite well the spatial structure originating the Mistral, as demonstrated by the histogram of the wind direction. On the contrary, the Côte d'Azur buoy is affected by the topography of the south of France and of the north of Italy, and, in turn, by the interaction between the atmospheric flow and the topography. In this case, the wind, i.e. the Mistral is the product of the interaction of the atmospheric flow with a complex orography. The representation of this interaction depends on the resolution of the model which produces the analysis. At T63, the resolution of the NCEP reanalysis, the Rhone valley is poorly represented. Then a lowering of the resolution of the model could explain the worsening of the Mistral representation. Considering the eastern buoy, all the data sets reproduce the peak in the northerly wind. The Aetesians are caused by a large scale pattern, which is well captured by all the analyses and reanalyses, i.e. the Aetesians are not affected by the model resolution since they depend on a large scale forcing. On the other hand, the easterly winds, apart from QuikSCAT and NCEPB, are not represented by either model. area covering the entire Mediterranean sea. The previous paragraph has shown the goodness of the QuikSCAT data respect to in-situ measurements. So, the satellite data become our “truth” to be compared with the models' results. One possible approach for this type of comparison is presented by Perlin et al. (2004). Here, we consider the vector correlation as our measure for the comparison between QuikSCAT and the numerical datasets. We limit the analysis to the ERA40 dataset which, even if it has shown some weakness in the coastal areas, has often been used as atmospheric forcing in many numerical simulation of the Mediterranean circulation. Vector correlations (Crosby et al., 1993) of the scatterometer and ERA40 Model wind fluctuations were computed at each point in the domain and normalized to 2 to yield values in the range 0 to 1 (Fig. 4). For this comparison, QuikSCAT winds were binned into the ERA40 1 degree grid to match with the ERA40 spatial resolution. The number of model-observation pairs used to compute the correlations exceeded 500 in the 88% of sea grid points in both years while only in the 4.6% of grid points the number of pairs drops to less than 100 both in 2000 (Fig. 4a) and 2001 (Fig. 4b). In general the spatial patterns of the statistical parameter in the 2 years are very similar. 4. Large domain comparison Since only QuikSCAT and the models provide gridded information to be used as a driver for ocean modeling, we want to test the model's skill over a wider Fig. 4. Vector correlations of the scatterometer and ERA40 Model wind fluctuations were computed at each point in the domain and normalized to 2 to yield values in the range 0 to 1. a) year 2000; b) year 2001. P.M. Ruti et al. / Journal of Marine Systems 70 (2008) 33–48 As already observed by previous authors (Perlin et al., 2004) for different regions in the world the vector correlation tends to decrease from more than 0.85 in the offshore region to less than 0.5–0.6 near many coastal areas. This decrease is less evident in coastal regions dominated by the Mistral in the gulf of Lion or Aetesian winds in the Levantine basin. While, it is confirmed over the Ligurian Sea (Côte d'Azur). The vector correlation analysis for the ERA40 dataset confirms what already observed for the in-situ comparison. The coastal area, where the interaction between the atmospheric flow and the topography dominates, shows low correlation values which may arise from the coarseness of the model respect to the process we want to simulate. 5. Towards a new dataset for Mediterranean ocean simulations The previous two sections analyzed the reliability of several gridded dataset in reproducing the surface wind over the Mediterranean basin. On the average, the QuikSCAT and the ECMWF analysis have shown the best performance. These wind fields can be applied to force ocean models over the Mediterranean region. Nevertheless, long climatic simulations need long time series of forcing which can be supplied only by the reanalysis products. Considering the surface wind of the two re-analyses, ERA40 shows slight improvements respect to NCEP, which has a poor performance. In order to produce a better forcing for long climate simulations, a possible solution could be to produce a blended dataset based on ERA40, correcting the main bias. Basing on the good performance of the QuikSCAT surface winds at the buoy's locations (Fig. 3), an empirical method to correct the surface wind speed can be applied: the slope of the ERA40 can be adjusted over each grid point by the mean of the QuikSCAT data. For each grid point of the Mediterranean Sea, a regression line between ERA40 and QuikSCAT has been estimated for all the overlapping grid points and for the year 2000. The slopes have been computed imposing that intercept equals zero (to avoid negative wind speeds), and only considering QuikSCAT wind values more intense than 5 m s− 1. The last assumption derives from the results presented in Section 3 i.e. the overestimation of QuikSCAT for wind speed less than 5 m s− 1 . In Fig. 5, we show the slope computed using ERA40 and QuikSCAT over the Mediterranean basin. The ERA40 shows a good behavior over the southern Ionian basin and over the Sicily channel, where slopes are close to 1. Underestimation of the ERA40 wind speed 45 Fig. 5. Linear fit (slope) between ERA40 and QuikSCAT over the Mediterranean basin for year 2000. is observed in the western basin, in the Adriatic Sea, in the 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 stronger underestimation over the Alboran sea, where ERA40 never exceeds 5 m s − 1 during all the year (2000). The ERA40 analysis, probably, does not represent the orographic channeling of the wind between the Sra Nevada and the Atlas mountain chain. The wind speed error does not directly affect the ocean circulation, but it is the wind stress and in turn the wind curl which forces the oceanic circulation. So, we should take into account how the differences in the wind speed actually induce the differences in the wind stress and wind stress curl. If we consider a relative wind speed error between ERA40 and QuikSCAT (see Fig. 5) of about 30%, this produces differences in the windstress ranging from 0.02 N m− 2 to 0.34 N m− 2 for wind speed ranging from 5 to 20 m s− 1. The relative error goes from 30% for the wind speed to 40% for the wind stress. A computation of the Ekman pumping vertical velocity using the ERA40 and QuikSCAT data, produces a reduction of the vertical velocity of a factor 2. Then, we empirically corrected the ERA40 wind field by applying the local value of the slope to the intensity of each vector (adjusted ERA40). The computed wind data sets have been then used to calculate the heat budget over the basin, as a preliminary test. The knowledge of the heat fluxes between ocean and atmosphere is a requirement for understanding and modeling the Mediterranean climate system. The air–sea heat flux is characterized by four components: shortwave and longwave radiation, latent and sensible heat fluxes. Different parametrization have been used to compute the surface fluxes, using meteorological and oceanographical data (bulk formulae, for a review see Krahmann et al., 2000, and references therein). Since the wind speed is a key meteorological parameter in the bulk formulae 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 a particular wind dataset can affect the estimation of the annual Mediterranean heat budget. As a first step, the net surface heat flux at the air–sea interface has been estimated in the Mediterranean basin using a combination of ERA40 analysis (sea level atmospheric pressure, 2 m air and dew point temperatures, cloud cover, zonal and meridional wind components) and Pathfinder AVHRR sea surface temperatures (for a detailed description of the parameterizations used see Appendix A in Marullo et al., 2003). The same computation has been performed using the adjusted ERA40 winds, in order to evaluate the impact on the heat fluxes of the proposed empirical correction. The results are shown in Fig. 6. The spatial patterns between the two datasets are quite similar. The most evident difference between the two plots is a predominance of positive fluxes (i.e. the ocean absorbs heat from the atmosphere) in the ERA40 case, with respect to the blended ERA40. In fact, whereas in the ERA40 map the regions with negative budget are confined to the Gulf of Lion and to the Aegean regions only, in the blended ERA40 case the ocean releases heat to the atmosphere over most of the basin, except for the Sicily channel and the extreme western Mediterranean. Moreover, when the averaged total heat budget over the entire Mediterranean 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 of Fig. 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 analysis presented here cannot be considered exhaustive, mainly because the study has been conducted only on a single year, and hypothesizing the wind to be the main error source in the heat budget computation. Nevertheless, the blended ERA40 derived Mediterranean heat budget is closer to the climatology (Bethoux, 1979; Garrett and Outerbridge, 1993; Macdonald et al., 1994), which suggests a basin total budget ranging between − 7 and − 5 W/m2. The difference between the two estimations is fairly relevant and it is clearly explained by the analysis presented before i.e. underestimation of the wind by ERA40. The observed discrepancies on the mean total budget and on the spatial distribution of the heat fluxes certainly will affect the climatic simulations, which are strongly dependent on such terms. 6. Summary and conclusions Wind speed analysis from ECMWF routine assimilation system and from ERA40 and NCEP reanalysis have been compared to wind speeds derived from QuikSCAT and to in-situ measurements (buoy-mounted anemometers). The comparison has been extended to a blended product, which corrects the NCEP reanalysis using the QuikSCAT data. The analysis has been carried out to verify the reliability of the analyzed wind speeds to be used as forcing for Mediterranean Sea simulations. As a final test, the effect of the different wind parameterizations on the heat fluxes budget has been evaluated. The comparison between the satellite winds and the sea truth data demonstrated the ability of the QuikSCAT instrument in the retrieving the dynamics of the wind fields at the two buoy sites. The results of the matchup analysis demonstrated that the winds obtained from satellite data reproduced the in-situ variability for both the direction and the intensity. The only significant discrepancy is obtained for winds with magnitude less than 5 m s− 1, for which it is well known that the scatterometers have minor problems. The gridded data sets, when compared with the sea measurements, have shown a poorer accuracy respect to QuikSCAT, even if relevant differences are retrieved among the models. The ECMWF wind field demonstrated the best performances in both the regions examined, while the results are becoming increasingly weaker considering the ERA40 and NCEP data. Noticeably, for the NCEP and the ERA40, the weak performances concerned both wind direction and intensity, resulting in a general degradation of the truthfulness of the wind speed field for these two data sets, particularly P.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 a relevant amelioration of the NCEP wind field, demonstrating that the method could significantly improve the wind retrieval. The results of the matchup analysis highlight the problems of the available wind data sets in the Mediterranean. Even though the study has been mainly conducted on three sites only (two buoys in the Aegean Sea, one in the Ligurian Sea and one in the Guf of Lion), the results obtained could be considered of general applicability in the basin. Indeed, the buoys are located in regions where the wind field exhibits strong variability, being exposed to the regimes of the Mistral and of the Aetesians, two among the most important winds of the Mediterranean area. The spatial resolution of the data sets is supposed to be one of the main relevant sources of error in the analyzed wind fields, explaining the worst results of the reanalysis data and the relative accuracy of the ECMWF. The NCEPB datasets deserve particular consideration. In fact, our study demonstrated that the blending method significantly increases the truthfulness of the starting data set: NCEP alone was not able to capture some of the important features of the wind regime at the buoy sites, while NCEPB did, mainly exploiting the enhanced resolution and accuracy of the QuikSCAT data. NCEPB, however, remains affected by relevant errors when compared with in-situ data. Our results suggest that this could be ascribed to NCEP's poor spatial resolution, which is demonstrated to have an important role in determining the performances of the gridded data sets. In our opinion, it is important to explore the degree of amelioration of a model-derived wind field, when a blending method is applied to a relatively high spatial resolution data set (i.e. ERA40), but with long temporal extension to be used for forcing ocean climatic simulations. Without reproducing the sophisticated and complex methodologies applied to build up the NCEPB fields, we retrieved for each ERA40 grid point a linear relationship relating ERA40 and QuikSCAT data. As a final analysis, we verified the impact of the choice of the wind data set on the computation of the Mediterranean heat budget. The point is quite crucial for the simulation of the basin physical dynamics via OGCMs. In fact, it was demonstrated (Artale et al., 2002) that the outputs of the same model, forced with even slightly different wind fields, can differentiate substantially. This is not surprising, because the wind field plays an important role in determining the momentum and heat fluxes, which primarily force the ocean models. For the ERA40 wind fields and its blended product, the total heat budget of the 47 Mediterranean has been calculated via bulk formulas, and the resulting fields have been compared with previous estimates. The analysis cannot be considered exhaustive and complete mainly because it has been conducted for the year 2000 only. However, the blended product results realistic in the heat budget estimations, and, compared with the basic products, produced an evident amelioration. Acknowledgements We thank the JPL PO.DAAC that makes the QuikSCAT data freely available, Dr. K. Nittis for the Santorini and Mykonos buoys data and METEOFRANCE for the ODAS-03FR Côte d'Azur buoy data. We thank an anonymous reviewer. We would also like to thank Dr. R. Evans, Dr. F. Bignami and Dr. R. Santoleri for the helpful comments. The NCEP reanalysis data was provided to us by NOAA's Climate Diagnostic Center. QSCAT/NCEP Blended Ocean Winds have been obtained from Colorado Research Associates http://dss. ucar.edu/. ECMWF ERA-40 data used in this study have been obtained from the ECMWF data server. The work was carried out with the support of the Agenzia Spaziale Italiana (ASI I/R/206/02). 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