Mesoscale Wind Climate Analysis: Identification of Anemological Regions and Wind Regimes
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INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 28: 629–641 (2008) Published online 12 June 2007 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/joc.1561 Mesoscale wind climate analysis: identification of anemological regions and wind regimes M. Burlando,a,b* M. Antonellia,b and C. F. Rattoa,b a Department of Physics, University of Genoa, Via Dodecaneso, Genoa, Italy b National Consortium of Universities for Physics of Atmosphere and Hydrosphere (CINFAI), Italy ABSTRACT: Following the idea that the climatological study of a physical variable should aim at the comprehension of its mean state as well as the characterization of its dynamics, cluster analysis has been applied to study the wind climate of Corsica (France) in order to identify the anemological regions (mean state) and the wind regimes (weather variability) which characterize its coastal areas. The analysis is based on a 3-year long time-series of measurements of the wind velocity from 11 anemometric stations located along the perimeter of the island. Since the present study was an analysis preliminary to the subsequent assessment of the wind potential of Corsica, we have worked only with wind intensities. Nevertheless, at the end of our analysis, we have also considered wind directions for the final interpretation of the results. The anemological regions are defined through the comparison of 15 different clustering techniques resulting from the combination of three distance measures and five agglomerative methods. As confirmed by geographical considerations, the results identify three distinct anemological regions: the eastern region (ER), the north-western region (NWR), the south-western region (SWR). The wind regimes are identified by means of a two-stage classification scheme based on a hierarchical cluster analysis followed by a partitional clustering. The final classification identifies eight regimes: the four wind regimes corresponding to the main weather patterns of Western Europe, as proposed by Plaut and Simonnet, and another four clusters corresponding to breeze regimes. Copyright 2007 Royal Meteorological Society KEY WORDS anemological regions; hierarchical and partitional clustering; mesoscale wind climate; wind regimes Received 15 May 2006; Revised 25 February 2007; Accepted 15 April 2007 1. Introduction such as the orthogonal eigen-modes decomposition, and cluster analysis. Linear approaches rely on the hypoth- The interest in regional climatological studies aimed at esis that, in the multidimensional phase space of the defining recurrent weather patterns or delineating zones wind patterns (defined like the vector field of the wind, of similar climate has recently grown considerably in i.e. the map which assigns each point of the space the the international scientific community. In particular, this corresponding wind vector), the mean wind field is at interest is promoted by the need for defining objective the centre of the phase space, while wind regimes (the techniques for climate classification and monitoring cli- most recurrent wind patterns) are identified by specific mate changes. In this framework, the present study is directions defined by eigenvectors. In this case, it should focused on two main questions concerning the classifica- be possible to reveal strong correlations among spa- tion of meso-β scale (after Orlanski, 1975) wind fields: tially stationary meteorological wind conditions. Ludwig the regionalization of a territory from an anemological et al. (2004), for example, have been able to identify point of view and the identification of its main wind the most important wind patterns in valleys south of regimes. the Great Salt Lake (Utah) from empirical orthogonal First attempts at the regionalization of wind climate function (EOF) analysis. In particular, they clearly dis- and classification of wind regimes based on the anal- tinguished the circulation of up- and down-valley flows ysis of an ensemble of wind or meteorological fields for negative and positive coefficients of EOF1, respec- date back to the late 1980s, when automated methods tively. However, they could not identify satisfactorily relying on statistical procedures to reduce large, multi- the secondary wind patterns because these are neither variate datasets into distinct weather types began to take necessarily orthogonal to EOF1 nor coupled in spatial place thanks to increasing computational resources. In structures with inverse polarity, as constrained by the particular, two main mathematical approaches have been linear decomposition. Non-linear approaches, on the con- used extensively to study wind data: linear techniques, trary, are based on the identification of attractors which correspond to weather regimes (Lorenz, 1963), defined as peaks of the probability density function in the clima- * Correspondence to: M. Burlando, Department of Physics, University of Genoa, Via Dodecaneso 33, 16 146 Genoa, Italy. tological phase space. Cluster analysis, in particular, is a E-mail: burlando@fisica.unige.it multivariate statistical technique based on the assumption Copyright 2007 Royal Meteorological Society 630 M. BURLANDO ET AL. that a collection of events can be grouped into a small conditions, but the atmosphere does not merely evolve number of representative states according to a given crite- around a mean state; on the contrary it spends most of rion of similarity (Everitt, 1977). This is why cluster anal- the time among a few peculiar large-scale states. The ysis has been widely adopted in climatology for grouping study of weather regimes is therefore necessary in order stations or grid points to define climate regions, as well to understand the interactions between the forcing mech- as for grouping meteorological patterns into classes or anisms at synoptic and local scales which mainly con- climate regimes. tribute defining different climate regions. The idea that In defining climate regions through clustering tech- climate must be studied not only through the comprehen- niques, measurements from a set of stations or gridded sion of its mean state but also through the understanding data from numerical simulations or data assimilation are of its dynamics is the leading point of the present research generally used. For example, Davis and Kalkstein, in into the wind climate regions and regimes of Corsica. 1990, developed a spatial synoptic surface climatology In the next section an overview of the main synoptic for the continental U.S. to group locations with homo- weather regimes of Western Europe and a short descrip- geneous weather conditions. Fovell and Fovell (1993) tion of the territory under study are reported, in order accomplished a regionalization of the U.S. in climate to give an idea of the expected large- and local-scale zones through the hierarchical cluster analysis of tem- forcing to surface flow fields. Section 3 describes the perature and precipitation data over 50 years. On the available anemological data, their standardization in order basis of the same databases, Bunkers et al. (1996) rede- to calculate the distances for clustering, as well as a short fined the climate regions in U. S. northern Plains through presentation of clustering methods. It is worth noting that a hierarchical clustering method followed by a pseudo- the present study was preliminary to the assessment of the hierarchical iterative procedure to optimize the final clas- wind energy potential of Corsica, so that we have based sification through element reassignment. Finally, Mim- our analysis only on wind intensity patterns, i.e. scalar mack et al. (2001) applied a hierarchical clustering to fields of the wind intensity mapped into the physical cumulated monthly rainfalls to define rainfall regions in space. The vector fields of wind intensity and direction South Africa. have been used just for the final interpretation of the Alternatively, a great number of examples concern the results. In Section 4 a great number of clustering algo- use of cluster analysis to identify climate regimes by rithms to define wind climate regions are compared, and grouping recurrent time-series of contemporary meteoro- the corresponding results are shown. Then, the method- logical observations or gridded data. In 1987, Kalkstein ology for the identification of different wind climate et al. used a combination of principal component analysis regimes and the resulting classification are reported in and clustering to study the time-series of seven meteoro- Section 5. Conclusions are drawn in Section 6. logical variables collected in a single surface location, in order to develop a synoptic index to classify the tempo- ral succession of synoptic situations. Davis and Walker 2. Synoptic and local forcing to wind climate (1992) applied a similar technique to upper-air synop- tic climatology using thermal, moisture and flow data As already stated, the present study was the first part of a from a rawinsonde to classify seasonal and inter-annual more general research concerning the assessment of the synoptic scale variations in hydrodynamic and thermody- wind potential of Corsica. In this framework, we had the namic conditions. Eder et al. (1994) proposed a two-stage necessity of defining the main large-scale synoptic flows clustering classification scheme designed to elucidate the as well as the local forcing on wind patterns. dependence of ozone on meteorology. Mengelkamp et al. In Western Europe there are approximately two main (1997) studied the large-scale wind climatology making a states for the atmosphere: the westerly or zonal