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: 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 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),
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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.
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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
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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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