Wind spatial heterogeneity in a coastal area (Alfacs Bay, northwestern
Mediterranean Sea) is described using a set of observations and modelling
results. Observations in three meteorological stations (during 2012–2013)
along the coastline reveal that wind from the N–NW (strongest winds in the
region) appears to be affected by the local orography promoting high wind
variability on relatively short spatial scales (of the order of few kilometres). On the
other hand, sea breezes in late spring and summer also show noticeable
differences in both spatial distribution and duration. The importance of
wind models' spatial resolution is also assessed, revealing that high
resolution (
In the open sea and ocean, wind variability responds mostly to mesoscale structures like cyclones and anticyclones, as well as more permanent structures such as easterly (polar) and westerly (middle latitudes) winds. But when orographic constraints appear, such as oceanic islands (Chavanne et al., 2002) or mountains in coastal areas (Jiang et al., 2009), the wind presents high spatial variability, showing important curl gradients and becoming less predictable. In coastal areas, several examples of high spatial variability due to topographic constraints have been described (e.g. Herrera et al., 2005; Boldrin et al., 2009). In recent years, the application of numerical models in both the atmosphere and oceans has contributed to improving the understanding and description of this variability (Schaeffer et al., 2011). Moreover, modelling studies have revealed that the model resolution is a key factor for the correct representation of wind patterns, which could be essential in a correct prediction of flood episodes (Brecht and Frank, 2014) and could allow for the correct application of hydrodynamic modelling (Signell et al., 2005; Bignami et al., 2007). Several authors have described wind variability in lakes (e.g. Venäläinen et al., 2003), whilst only few studies focused on the wind description in a small-scale domain such as small estuaries or coastal areas. Considering the importance of wind on hydrodynamics, water mixing, waves and air quality, this contribution seeks to fulfill this gap, presenting an example of wind variability in a small-scale coastal area through observations and modelling results. A small bay, Alfacs Bay, in the northwestern Mediterranean Sea was selected.
This contribution is organized as follows. First, a short description of the study area (Sect. 2) and the set of observations is presented (Sect. 3). In this section the spatial and temporal variability observed is described. Then, the numerical weather prediction model implementation and outputs are shown, compared to the observed winds (Sect. 4). Finally, results for selected wind events and patterns observed are discussed.
The Ebro Delta (NE coast of Spain) forms two semi-enclosed bays, Fangar and
Alfacs (to the north and south, respectively). The dimensions of Alfacs Bay are
16 km from head to mouth (Fig. 1c), 4 km wide and a mouth connection to
the open sea of about 2.5 km. The bay is surrounded by rice fields to the
north – which spill around 10 m
Wind roses for M-A
The synoptic winds on the Catalan coast are affected by orographic constraints, such as the blocking winds of the Pyrenees that promote tramuntana (N) and mistral (NW) winds over some areas, and the wind channelling due to river valleys (Sánchez-Arcilla et al., 2008). Northerly winds in the region are mainly produced by high pressures over the Azores and lows over the British Isles and Italy; other synoptic situations could also lead to strong winds from the NW in the Ebro Delta (Martín Vide, 2005). Winds in the bay have been characterized as having a northwestern and southwestern predominance, with the strongest ones coming from the NW (channelized by the Ebro River valley; see Fig. 1b), being also the most common strongest winds on the Catalan coast during autumn and winter (Bolaños et al., 2009). On the other hand, some authors have reported the high spatial heterogeneity of the wind fields inside the bay (Camp, 1994), in agreement with observed events during field campaigns by the authors of this manuscript, in which winds from the northwest were blowing inside the bay whilst in the mouth of the bay the wind was almost calm.
Atmospheric data – wind, atmospheric pressure, solar radiation and humidity – were
obtained from three fixed land stations: Alcanar (M-A); Sant Carles
(M-SC) from Xarxa d'Intruments Meteorològics de Catalunya (XIOM)
described in Bolaños et al. (2009); and Alfacs-Meteocat (M-Met), which
belongs to the automatic weather stations network of the Meteorological
Service of Catalunya (
Observations from June 2012 to June 2013 at three meteorological stations
show noticeable differences (Fig. 2), confirming high variability
among them. This period has been chosen for this analysis because it is the
only one with data from all three stations. In M-SC (Fig. 2c) a
bimodal behaviour with the most common winds from the southwest and
north–northeast are shown. These winds are in agreement with data acquired at the same
station for about 16 years (not shown), indicating high representation
for the period selected. The highest intensities correspond to N–NW winds
(
Summary of the main characteristics of the three different model configurations used in this study.
Wind direction comparison between M-A and M-SC for 1 year.
The coloured data indicate mean wind velocity values (m s
Two different atmospheric models, provided by Meteocat, are used in this
study to assess the role of spatial heterogeneity in the bay. For this
purpose, the Weather Research and Forecasting (WRF) model has been selected
(see
Taylor diagram for summer 2013
On the other hand, an additional simulation has been considered to derive atmospheric data at a very high resolution (400 m). In particular, the WRF-ARW outputs at 3 km are downscaled by a diagnostic meteorological model called CALMET. CALMET, a component of the CALPUFF Modeling System designed for the simulation of atmospheric pollution dispersion (Scire et al., 1999), is a diagnostic three-dimensional meteorological model which includes parameterized treatments of slope flows, kinematic terrain effects and terrain blocking effects, among others. These particular aspects help to better represent regional flows with an efficient computational cost.
The operational numerical model resolutions (spatial and temporal) were selected considering both reliability and computational costs. The computational cost for a 3-day forecast for CALMET (at 400 m resolution) is about 2 h using 10 CPUs. On the other hand, a similar domain and forecast horizon using WRF3 (3 km of resolution) take around 1 h (30 CPUs). In this sense, the application of WRF at higher resolution is not currently considered as an operational product due to computational limitations. For all the verification processes the daily first 24 h of prediction from the operational system are used.
Correlation among the three different atmospheric models and observational data for 3-day-long events during summer 2013 and winter 2014. No correlation for winter 2014 (M-SC dismantled on September 2013).
In this section, we present the results of verification studies to assess
the performance of wind velocity and direction prediction from the models
against the observation. The verification of both WRFs (WRF3 and WRF9) and
CALMET is shown in Fig. 4 for summer 2013 (Fig. 4a) and winter 2014
(Fig. 4b). Different oceanographic campaigns were developed at Alfacs
Bay during the same periods. We chose that period in order to coincide the
meteorological observations and model results with oceanographic data.
Verification of all the models and resolutions is done against hourly means
from observational data. The wind module velocities measured in both M-A and
M-SC are graphically compared to modelling results for both systems (model
data is interpolated through bi-lineal interpolation to corresponding
points) using a Taylor diagram (Taylor, 2001). In this diagram we can observe
the comparison between observations and model through the correlation, the
centred root-mean-square difference (CRMSD) and the standard deviations
(SDs; see Appendix). In order to compare in the same figure different models
against different observations, the standard deviations and CRMSD are
normalized over the standard deviation of the corresponding observations
(Grifoll et al., 2013). The model skill improves as the points get closer to
the observation reference point in the diagram. In summer 2013, better
correlation (around 0.6) is observed in M-SC, while in M-A the values
decrease to
Weibull distributions for summer 2013 in M-A
The wind module velocities measured in both M-A and M-SC are compared in
Fig. 5a and b, respectively, for summer 2013 with modelling
results using a Weibull distribution, which is defined as a two-parameter
function commonly used to fit the wind speed frequency distribution. In both
stations, the best fit between the model and observational Weibull
distributions is observed for CALMET. In M-A, CALMET and the observational
distribution show almost equally shaped coefficient; even observational data
present stronger winds. WRF3 also has similar shape, with even more
energy distributed at medium wind intensities (
Some characteristic events representing the most usual winds in the area
have also been analysed in order to understand the behaviour of each model
under different conditions (resumed in Table 2). A period of 3
days is considered to have enough data to compare. Results show that winds from
CALMET and WRF3 have higher correlations (except in northwest 2) than
WRF9. The worst results are observed during northwesterly winds in summer 2013.
This is clearly affected by the topography, and the observed wind in
M-A is not reproduced by any of the models. Slightly better results are
obtained in M-SC, especially by CALMET (but still with poor correlation).
This event was characterized by its shortness (less than 6–8 h) and
unsteadiness. On the other hand, in the winter period, another NW wind event
(lasting for more than 1 day) was reproduced with noticeable agreement in
M-A. In this case, the simulation WRF9 seems to reproduce quite well the
wind time series in M-A, but no data for M-SC are available to compare.
Southern winds – southeast and diurnal regime of sea breeze – are better
reproduced by the finest models, being the highest improvement between WRF3 and
WRF9. During sea breezes and considering the complete diurnal–nocturnal
cycle, the CALMET model seems to reproduce winds better than the coarser ones.
Considering the daily duration of the sea breeze – between 6 and 8 h –
all the models would be able to reproduce it (Table 2). However, the
temporal variability of such processes could only
be reproduced using high-temporal-resolution models (
The three different models configurations are plotted for three
snapshots of typical wind events at Alfacs Bay.
Model wind snapshots for the three different resolutions are used to understand the spatial structures associated with most common winds in the area (Fig. 6). Three events have been chosen, representing a case with higher variability (Fig. 6a, d and g) to one with an almost homogeneous wind field (Fig. 6c, f and i). For northwesterly winds (left column panels in Fig. 6) it is clear that Serra de Montsià exerts a physical barrier on wind fields, thus revealing areas in the inner bay with high wind intensities and areas down the mountain with almost calm winds or with different direction – shadow effect, described in other environments such as the Hawaiian Islands in Chavanne et al. (2002). These effects were also observed for winds coming from the north (not shown). Atmospheric pressure at surface on 4 April 2014 shows low pressure over the North Atlantic and a high-pressure area over north Africa. This synoptic situation promotes winds from the north–northwest (triggered by the Ebro River valley) in the study area. The modelled winds corresponding to observations in M-A and M-SC locations are similar, not showing all the direction variability measured in observations (Figs. 2 and 3). However, the wind patterns in both WRF3 and CALMET are similar and show spatial wind variability inside the bay, thus indicating that the medium-resolution model is able to reproduce topographic constraints under these circumstances. On the other hand, the coarser model (Fig. 6a) does not reveal such a variability – expected for the dimensions of the bay and model resolution, with pixels almost half of the bay size. In summary, CALMET reproduces with higher accuracy these kind of winds in M-SC, while in M-A the errors are similar to coarser models. Both stations are located near the maximum transition zone between high- and lower-intensity winds (Fig. 6g), corresponding to the areas where modelling errors would be more sensitive to topographic effects.
An intensification of sea breezes (Fig. 6b, e and h) at midday in inner areas of the bay is clearly represented as well as a clockwise gyre of wind in M-SC related to M-A. The modelled highest intensities in the inner bay are not able to be validated, due to the lack of more observational data in this area (M-Met has been defined as a bad indicator of wind field). On the other hand, differences from coarser to the finest model configurations are noticeable. Both WRF3 and CALMET show some spatial structures in daily regimes not solved by WRF9. In the time series, sea breezes time-lag between M-A and M-SC observations is not reproduced by any of the models.
On the other hand, spatially homogeneous wind fields have also been observed
during several events. In this case, winds from the northeast are shown in
Fig. 6c, f and i. The wind fields reproduced by observations and
atmospherical models indicate homogeneous spatial winds, not affected by
topography in the Ebro Delta (winds coming along the coast). For these
winds, the coarser model does reproduce the wind pattern similar to the
finest model. In Fig. 3a there is an area where directions around
0–45
This contribution presents an example of high wind variability in a coastal area (Alfacs Bay, NW Mediterranean Sea) during the period 2012–2013. Observational data demonstrate that wind direction seems to be affected by the surrounding mountains. These effects are maximized during northwesterly winds, in which the local mountains exert noticeable shadowing effects over the mouth of the bay. These results are in agreement with Camp (1994), showing high wind spatial variability at Alfacs Bay, as well as other similar studies which show the wind-channelling effects in some rias of Galicia (Herrera et al., 2005). Other winds, like sea breezes, also show noticeable variability, not only in space but also in time, and probably related to orography, land uses and different sea-water temperature in the bay and in the open sea. Winds from the S–SE not related to sea breezes are likely affected by local orography, but not enough events were recorded to confirm the observed pattern. Due to the short length of observational period (around 1 year), the results have no climatic significance.
The spatial heterogeneity also plays an important role in wind modelling results in this coastal area. The coarse model (9 km) does not reproduce the spatial variability associated with most topographically influenced winds (northwesterlies and sea breezes). The medium-resolution model (3 km) has proven to represent the wind spatial fields with enough accuracy according to the observations. This indicates that the effects of the main topographic structures on the area are recognized by this resolution model, contrary to other places where similar model resolution was not able to reproduce all the wind variability due to orography (Cerralbo et al., 2012). The predicted wind from the CALMET model at the highest resolution (400 m) also improves the spatial variability and shows the highest correlation with observational data under some circumstances. In this sense, the CALMET model could be an interesting and useful product for ocean and wave modelling, minimizing the information losses due to the downscaling processes. In summary, all the systems analysed reproduce with enough accuracy some of the characteristic winds observed at Alfacs Bay. The highest-resolution model shows better responses, since it reproduces more realistically wind fields and discriminates topographic structures such as mountains and gaps between them. However, correlation in some cases is higher in coarse models (WRF9), agreeing with Signell et al. (2005), who demonstrate that sometimes the higher-resolution models would present lower correlation due to higher “noise” (more variability) compared to the coarser models. Other authors (e.g. Miglietta et al., 2012; De Biasio et al., 2014) also argue that local models could show more details (more detailed flow patterns) although worse statistics due to errors in timing and location, whilst global models would produce smoother results and probably much skillful forecasts. At this point, the computational cost would indicate which should be the atmospheric model to be considered depending on the skill assessment requirements.
The effects of wind spatial variability on relatively short length scales would be an important factor to be considered in studies dealing with biology and ecology hazards, e.g. harmful algal blooms as described in Quijano-Scheggia et al. (2008), hydrodynamics (Cucco and Umgiesser, 2006) and water quality parameters (Grifoll et al., 2011) in coastal waters.
The statistics used in the normalized Taylor diagram are defined as follows,
where “obs” corresponds to observations,
This work was supported by a FPI-UPC pre-doctoral fellowship from the
European project FIELD_AC (FP7-SPACE-2009-1-242284
FIELD_AC), the Spanish project PLAN_WAVE
(CTM2013-45141-R) and the Secretariat d'Universitats i Recerca del
Dpt. d'Economia i Coneixement de la Generalitat de Catalunya (Ref 2014SGR1253).
The campaigns were carried out thanks to the MESTRAL project
(CTM2011-30489-C02-01). We would like to thank Joan Puigdefàbregas,
Jordi Cateura, Joaquim Sospedra and Elena Pallarés from Laboratori
d'Enginyeria Marítima for all their help with campaigns and data
analysis, as well as the Ebro Irrigation Community (Comunitat de Regants
de la dreta de l'Ebre,