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Gaetani M.,Complutense University of Madrid | Gaetani M.,CNR Institute for Biometeorology | Mohino E.,CNR Institute for Biometeorology
Journal of Climate | Year: 2013

In this study the capability of eight state-of-the-art ocean-atmosphere coupled models in predicting the monsoonal precipitation in the Sahel on a decadal time scale is assessed. To estimate the importance of the initialization, the predictive skills of two different CMIP5 experiments are compared, a set of 10 decadal hindcasts initialized every 5 years in the period 1961-2009 and the historical simulations in the period 1961- 2005. Results indicate that predictive skills are highly model dependent: the Fourth Generation Canadian Coupled Global Climate Model (CanCM4), Centre National de Recherches Météorologiques Coupled Global ClimateModel, version 5 (CNRM-CM5), andMax Planck Institute Earth SystemModel, low resolution (MPI-ESM-LR) models show improved skill in the decadal hindcasts, while the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) is skillful in both the decadal and historical experiments. The Beijing Climate Center, Climate System Model, version 1.1 (BCC-CSM1.1), Hadley Centre Coupled Model, version 3 (HadCM3), L'Institut Pierre-Simon Laplace Coupled Model, version 5, coupled with NEMO, low resolution (IPSL-CM5A-LR), and Meteorological Research Institute Coupled Atmosphere-Ocean General Circulation Model, version 3 (MRI-CGCM3) models show insignificant or no skill in predicting the Sahelian precipitation. Skillful predictions are produced by models properly describing the SST multidecadal variability and the initialization appears to play an important role in this respect. © 2013 American Meteorological Society.


Haworth M.,CNR Institute for Biometeorology | Elliott-Kingston C.,University College Dublin | McElwain J.C.,University College Dublin
Oecologia | Year: 2013

Plant stomata display a wide range of short-term behavioural and long-term morphological responses to atmospheric carbon dioxide concentration ([CO2]). The diversity of responses suggests that plants may have different strategies for controlling gas exchange, yet it is not known whether these strategies are co-ordinated in some way. Here, we test the hypothesis that there is co-ordination of physiological (via aperture change) and morphological (via stomatal density change) control of gas exchange by plants. We examined the response of stomatal conductance (Gs) to instantaneous changes in external [CO2] (Ca) in an evolutionary cross-section of vascular plants grown in atmospheres of elevated [CO2] (1,500 ppm) and sub-ambient [O2] (13. 0 %) compared to control conditions (380 ppm CO2, 20. 9 % O2). We found that active control of stomatal aperture to [CO2] above current ambient levels was not restricted to angiosperms, occurring in the gymnosperms Lepidozamia peroffskyana and Nageia nagi. The angiosperm species analysed appeared to possess a greater respiratory demand for stomatal movement than gymnosperm species displaying active stomatal control. Those species with little or no control of stomatal aperture (termed passive) to Ca were more likely to exhibit a reduction in stomatal density than species with active stomatal control when grown in atmospheres of elevated [CO2]. The relationship between the degree of stomatal aperture control to Ca above ambient and the extent of any reduction in stomatal density may suggest the co-ordination of physiological and morphological responses of stomata to [CO2] in the optimisation of water use efficiency. This trade-off between stomatal control strategies may have developed due to selective pressures exerted by the costs associated with passive and active stomatal control. © 2012 Springer-Verlag.


Gualtieri G.,CNR Institute for Biometeorology | Secci S.,Fedi Impianti Srl
Renewable Energy | Year: 2011

In the present work a computation of wind shear coefficients (WSCs) based on 1-h measured wind data has been performed by three stations located over coastal sites in Southern Italy, i.e., Brindisi (BR), Portoscuso (PS) and Termini Imerese (TI). Wind observations have been collected through a 6-year period (January 1, 1997 to December 31, 2002) by wind mast recording at the same two sensor heights (i.e., 10 and 50m AGL), thus enabling a proper wind profile analysis. WSC overall mean values were found to be 0.271 at BR, 0.232 at PS, and 0.150 at TI. In addition, a detailed analysis has been carried out to describe the WSC yearly, monthly and diurnal variation, as well as by wind direction. The characteristics of z0 surface roughness length have been also investigated as an estimate for neutral stability conditions only, resulting in overall mean values of 0.526m at BR, 0.287m at PS, and 0.027m at TI. The z0 variation by year, month and hour of the day, as well as by wind direction, has been analysed, too. The European "Corine Land Cover 2000" classification of the study areas has been employed to deeply investigate the land use influence on both WSC and z0 characteristics as a function of wind direction.Based on temperature and pressure surface measurements, the computation of site-specific mean air density as well as monthly variation has been also performed.Site-related 50-m wind resource has been assessed by means of wind roses and wind speed frequency distributions, as well as Weibull's parameters. The potential turbine-converted wind energy yield has been also investigated, enabling to detect, for each site, the most suitable 50-m hub height turbine model regardless of its rated power. Furthermore, a number of comparisons have been made to assess the discrepancy in 50-m energy yield resulting if using data extrapolated from 10 m, both with 0.143 default and overall mean WSC value, instead of actually 50-m measured data. © 2010 Elsevier Ltd.


Gualtieri G.,CNR Institute for Biometeorology | Secci S.,Fedi Impianti Srl
Renewable Energy | Year: 2011

Among all uncertainty factors affecting the wind power assessment at a site, wind speed extrapolation is probably one of most critical ones, particularly if considering the increasing size of modern multi-MW wind turbines, and therefore of their hub height. This work is intended as a contribution towards a possible harmonisation of methods and techniques, necessarily including surface roughness and atmospheric stability, aimed at extrapolating wind speed for wind energy purposes. Through the years, different methods have been used to this end, such as power law (PL), logarithmic law (LogL), and log-linear law (LogLL). Furthermore, aside from applying PL by using a mean wind shear coefficient observed between two heights (α), a number of methods have been developed to estimate PL exponent α when only surface data are available, such as those by Spera and Richards (SR), Smedman-Högström and Högström (SH) and Panofsky and Dutton (PD).The main purpose of this work is to analyse and compare the skill of some of most commonly used extrapolation methods once applied to a case study over a coastal location in Southern Italy. These are LogLL, LogL, as well as PL by using different approaches to estimate α (i.e., PL-α, PL-SR, PL-SH, and PL-PD). In doing so, the influence of atmospheric stability and surface roughness (z0), with special attention to their variability with time and wind characteristics, has been also investigated. In addition, a comparison among the three α-estimating methods by SR, SH and PD has been carried out. A 6-year (1997-2002) 1-h meteorological dataset, including wind measurements at 10 and 50 m, has been used. In particular, the first 5 years were used to analyse site meteorology, stability conditions, and wind pattern, derive α and z0, as well as compare α-estimating methods, while the latter (2002) to test the skill of the extrapolation methods. Starting from 10-m wind speed observations, the computation of 50-m wind speed and power density, as well as wind resource and energy yield, has been made. The Weibull distribution and related parameters have been used for the wind resource assessment, while AF, CF and AEY were calculated to evaluate the potential wind energy yield. © 2011 Elsevier Ltd.


Increasing knowledge on wind shear models to strengthen their reliability appears as a crucial issue, markedly for energy investors to accurately predict the average wind speed at different turbine hub heights, and thus the expected wind energy output. This is particularly helpful during the feasibility study to abate the costs of a wind power project, thus avoiding installation of tall towers, or even more expensive devices such as LIDAR or SODAR. Thepower law (PL) was found to provide the finest representation of wind speed profiles and is hence the focus of the present study. Besides commonly used for vertical extrapolation of wind speed time series, the PL relationship between "instantaneous" wind profiles was demonstrated by Justus and Mikhail to be consistent with the height variation of Weibull distribution. Therefore, in this work a comparison is performed between these two different PL-based extrapolation approaches to assess wind resource to the turbine hub height: (i) extrapolation of wind speed time series, and (ii) extrapolation of Weibull wind speed distribution. The models developed by Smedman-Högström and Högström (SH), and Panofsky and Dutton (PD) were used to approach (i), while those from Justus and Mikhail (JM) and Spera and Richards (SR) to approach (ii). Models skill in estimating wind shear coefficient was also assessed and compared.PL extrapolation models have been tested over a flat and rough location in Apulia region (Southern Italy), where the role played by atmospheric stability and surface roughness, along with their variability with time and wind characteristics, has been also investigated. A 3-year (1998-2000) 1-h dataset, including wind measurements at 10 and 50m, has been used. Based on 10-m wind speed observations, the computation of 50-m extrapolated wind resource, Weibull distribution and energy yield has been made. This work is aimed at proceeding the research issue addressed within a previous study, where PL extrapolation models were tested and compared in extrapolating wind resource and energy yield from 10 to 100m over a complex-topography and smooth coastal site in Tuscany region (Central Italy). As a result, wind speed time series extrapolating models proved to be the most skilful, particularly PD, based on the similarity theory and thus addressing all stability conditions. However, comparable results are returned by the empirical JM Weibull distribution extrapolating model, which indeed proved to be preferable as being: (i) far easier to be used, as z0-, stability-, and wind speed time series independent; (ii) more conservative, as wind energy is underpredicted rather than overpredicted. © 2013 Elsevier Ltd.


Gualtieri G.,CNR Institute for Biometeorology | Secci S.,Fedi Impianti Srl
Renewable Energy | Year: 2012

An accurate wind shear model is crucial to extrapolate the observed wind resource from the available lower heights to the steadily increasing hub height of modern wind turbines. Among power law (PL) and logarithmic law (LogL), i.e., the two most commonly used analytical models, the former was found to give a better representation of wind speed profiles and thus set as the reference model addressed by the present study. As well as commonly used for vertical extrapolation of 1-h wind speed records, the PL wind profile was proved to be consistent with the Weibull wind speed distribution. As a matter of fact, Justus and Mikhail suggested being more useful to deal with the full range of wind speed, such as required to specify the wind speed probability distribution, rather than using the "instantaneous" records. Therefore, in this work a comparison is proposed between these two PL-based extrapolation approaches to the turbine hub height, not only in terms of wind resource and energy yield computation skill, but also of simplicity and usefulness: (i) extrapolation of 1-h wind speed records, and (ii) extrapolation of the Weibull distribution. In particular, the models of Smedman-Högström and Högström (SH) and Panofsky and Dutton (PD) were used to approach (i), while those from Justus and Mikhail (JM) and Spera and Richards (SR) to approach (ii). In addition, a comparison of models in estimating wind shear coefficient was carried out. PL extrapolation models have been tested over a coastal and complex-topography location in Tuscany, Italy, where thus the role played by atmospheric stability and surface roughness (z 0), as well as their variability with time and wind characteristics, required to be deeply investigated. A 5-year (1997-2001) 1-h dataset, including wind measurements at 10 and 100m, has been used. Starting from 10-m wind speed observations, the computation of 100-m extrapolated wind resource, Weibull distribution and energy yield has been made, where the latter was performed once a site most efficient 100-m hub height turbine was detected and then applied. © 2012 Elsevier Ltd.


Gualtieri G.,CNR Institute for Biometeorology
Renewable Energy | Year: 2015

Based on power law (PL), a novel method is proposed to extrapolate surface wind speed to the wind turbine (WT) hub height, via assessment of wind shear coefficient (WSC), by only using surface turbulence intensity, a parameter actually regarded as a merely critical one in wind energy studies. A 2-year (2012-2013) dataset from the meteorological mast of Cabauw (Netherlands) was used, including 10-min records collected at 10, 20, 40, and 80m. WT hub heights of 40 and 80m have been targeted for the extrapolation, being accomplished based on turbulence intensity observations at 10 and 20m. Trained over the year 2012, the method was validated over the year 2013.Good scores were returned both in wind speed and power density extrapolations, with biases within 7 and 8%, respectively. Wind speed extrapolation was better predicted 10-40m (NRMSE=0.16, r=0.95) than 10-80 and 20-80m (NRMSE=0.20-0.24, r=0.86-0.91), while for power density even finer scores than wind speed were achieved (. r=0.98 at 40m, and r=0.96 at 80m). Method's skills were also assessed in predicting wind energy yield. Application over sites with different terrain features and stability conditions is expected to provide further insight into its application field. © 2015 Elsevier Ltd.


Maselli F.,CNR Institute for Biometeorology
International Journal of Remote Sensing | Year: 2011

The objective of this article is to develop and test a methodology capable of using medium spatial resolution satellite imagery to improve forest-area statistics derived from ground sampling. The methodology builds on the evidence that multitemporal Normalized Difference Vegetation Index (NDVI) images bring significant information on the spatial distribution of forest surfaces. Consequently, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images are potentially useful to improve forest-area assessment based on ground data. This expectation is verified in Tuscany (central Italy) using forest-area references extracted from the Coordination of Information on Environment (CORINE) land-cover map. The accuracy of forest-area statistics obtained at province level by different reference samplings is first assessed. Next, locally calibrated regression analyses are applied to multitemporal MODIS NDVI images in order to obtain per-pixel forest-area estimates. Two statistical methods (the direct expansion and the regression estimator) are finally used to combine these estimates with the ground data and produce corrected per-province statistics. The experimental results confirm that MODIS NDVI data contain relevant information on forest distribution, which can be efficiently extended over the land surface by locally calibrated regressions. The obtained estimates can be combined with the ground data for enhancing forest-area assessment at province level. To this aim, the regression estimator gives the best performance for all sampling densities of the reference data. © 2011 Taylor & Francis.


Gualtieri G.,CNR Institute for Biometeorology
Renewable Energy | Year: 2016

Based on a 3-year (2011-2013) dataset of 10-min records collected at 10, 20, 40, and 80 m from the met mast of Cabauw, a time-varying investigation of the wind shear coefficient (WSC) relationship with atmospheric stability was addressed. WSC interdaily and interannual variability was analysed according to a 2-D combined representation, which confirmed a clear oval-shaped "solar shadow" caused by solar warming observed during diurnal unstable hours, and large WSCs occurring under strong stable conditions during the summer nights.Three different power law based approaches were compared to extrapolate wind resource to the turbine hub height according to the following WSC settings: (i) site's previously measured overall yearly average; (ii) site's previously measured stability-varying yearly averages; (iii) 10-min theoretically predicted values by applying the Panofsky and Dutton (PD) model. The latter proved to be the finest approach, providing extrapolated wind resource biased by 1-5% and energy yield by 5.51-10.57%, and showing the highest accuracy occurring under the most frequent (and most energetic) neutral conditions, when Weibull distribution's tail including the highest wind speed bins is particularly finely reproduced.This work confirmed how instrumental availability of detailed information on site's atmospheric stability classification is for wind energy studies. © 2015 Elsevier Ltd.


Gualtieri G.,CNR Institute for Biometeorology
International Journal of Renewable Energy Research | Year: 2012

An integrated wind resource assessment tool has been developed to help public operators and private investors in wind farm planning. The two-parameter Weibull probability density function is used to calculate wind speed frequency distribution. The system takes advantage of an integrated database including an updated list of more than 200 windgenerators manufactured by the most experienced worldwide companies. Main wind resource and turbine-converted energy indicators are computed, such as mean wind speed and power density, Weibull's scale and shape factors, Betz annual specific energy, availability and capacity factors, annual energy production, and full-load hours. A comprehensive energy report is eventually created for any site, including plots and tables such as wind rose and Joint Frequency Functions, Weibull's wind speed distribution and cumulated probability, annual energy production vs. turbine power curve. Thereby, the system is suitable for on-site pre-feasibility studies. Furthermore, it operates according to a point-by-point fashion, as it may be routinely run in an automated mode for a large number of points over the study area. This later enables main wind energy contouring maps to be plotted. The system has been applied to perform a wind turbine comparison aimed at detecting the site most efficient windgenerator within a number of works. As a benchmark, an extensive application of the developed system has been carried out to assess the large-scale wind potential of Tuscany region, Italy. Hourly wind estimates calculated by the coupled Weather Research and Forecasting (WRF) and CALMET models at a 2-Km resolution have been processed by the system over the region through a 4-year time period.

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