Browsing by Author "Ibarra-Berastegi, Gabriel"
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Item Analysis of atmospheric thermodynamics using the R package aiRthermo(Elsevier, 2019-01) Saenz, Jon; González-Rojí, Santos J.; Carreno-Madinabeitia, Sheila; Ibarra-Berastegi, GabrielThe publicly available R package aiRthermo is presented in this study, which allows the user to process information relative to atmospheric thermodynamics, ranging from calculating the density of dry or moist air and converting data between moisture indices to processing a full sounding, obtaining factors such as the convective available potential energy, additional instability indices, or adiabatic evolutions of particles. The package also provides the possibility to present information using customisable Stüve diagrams. Many of the functions are written inside a C extension to ensure that the computations are fast. The results of applying this package to five years of real soundings measured over the Iberian Peninsula are also presented as an example. The package considerably extends the capabilities of R to process atmospheric soundings or model results. This will be useful for many practical environmental forecasting applications at different scales, such as statistical downscaling for climate analysis, quantitative precipitation forecasting (particularly precipitation extremes), diagnosing storms, flash floods, and lightning, and in aviation and other fields where computing atmospheric convection and its related parameters is important.Item Calculation of Lebanon offshore wind energy potential using ERA5 reanalysis: impact of seasonal air density changes(IEEE. Institute of Electrical and Electronics Engineers Inc., 2019) Ibarra-Berastegi, Gabriel; Gonzalez-Roji, Santos J.; Ulazia, Alain; Carreno-Medinabeitia, Sheila; Saenz, JonIn this work, data from the ERA5 reanalysis (2010-2017) have been used to estimate the seasonal offshore wind energy potential for the Lebanese coast. Additionally, for this estimation, the effect of seasonal changes of air density has been incorporated. As a reference, the SIEMENS 160/6 turbine has been adopted and wind energy potential has been expressed as the capacity factor (CF) associated to this turbine. The spatial distribution of CF provides an idea of available wind energy potential in the Lebanese coast. The impact of seasonal air density changes has been assessed as percentage reduction in this indicator. In summer, the CF reduction due to high temperatures and lower air density, reaches in some Southern regions of the Lebanese coast to values around 5.5%. The use of such reanalyses is likely to increase in the future, thus making consultancy work easier since a lot of computational work with state-of-the-art meteorological models like WRF or MC2 (used to draw the National Wind Atlas of Lebanon) may not be necessary. Therefore, most likely in the future, for wind potential estimations, rather than heavy calculation efforts, the know-how for consultancy companies will focus into deeper analysis and interpretation of readily-available data from reanalyses.Item Changes in the simulation of atmospheric instability over the Iberian Peninsula due to the use of 3DVAR data assimilation(Copernicus GmbH, 2021-06) González-Rojí, Santos J.; Carreno-Madinabeitia, Sheila; Sáenz, Jon; Ibarra-Berastegi, GabrielThe ability of two downscaling experiments to correctly simulate thermodynamic conditions over the Iberian Peninsula (IP) is compared in this paper. To do so, three parameters used to evaluate the unstable conditions in the atmosphere are evaluated: the total totals index (TT), convective available potential energy (CAPE), and convective inhibition (CIN). The Weather and Research Forecasting (WRF) model is used for the simulations. The N experiment is driven by ERA-Interim's initial and boundary conditions. The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00:00, 06:00, 12:00, and 18:00 UTC. Eight radiosondes are available over the IP, and the vertical temperature and moisture profiles from the radiosondes provided by the University of Wyoming and the Integrated Global Radiosonde Archive (IGRA) were used to calculate three parameters commonly used to represent atmospheric instability by our own methodology using the R package aiRthermo. According to the validation, the correlation, standard deviation (SD), and root mean squared error (RMSE) obtained by the D experiment for all the variables at most of the stations are better than those for N. The different methods produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these quantities on the initial conditions assumed for the calculation of a lifted air parcel. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the parameter) the variability of the reference values of the parameters, but D is able to capture it in most of the seasons. In general, D is able to produce more reliable results due to the more realistic values of dew point temperature and virtual temperature profiles over the IP. The heterogeneity of the studied variables is highlighted in the mean maps over the IP. According to those for D, the unstable air masses are found along the entire Atlantic coast during winter, but in summer they are located particularly over the Mediterranean coast. The convective inhibition is more extended towards inland at 00:00 UTC in those areas. However, high values are also observed near the southeastern corner of the IP (near Murcia) at 12:00 UTC. Finally, no linear relationship between TT, CAPE, or CIN was found, and consequently, CAPE and CIN should be preferred for the study of the instability of the atmosphere as more atmospheric layers are employed during their calculation than for the TT index.Item The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential(MDPI, 2019) Ulazia, Alain; Nafarrate, Ander; Ibarra-Berastegi, Gabriel; Sáenz, Jon; Carreno-Madinabeitia, SheilaHywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( CF ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that CF values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying.Item Global estimations of wind energy potential considering seasonal air density changes(Elsevier Ltd, 2019-11-15) Ulazia, Alain; Sáenz, Jon; Ibarra-Berastegi, Gabriel; González-Rojí, Santos J.; Carreno-Madinabeitia, SheilaThe literature typically considers constant annual average air density when computing the wind energy potential of a given location. In this work, the recent reanalysis ERA5 is used to obtain global seasonal estimates of wind energy production that include seasonally varying air density. Thus, errors due to the use of a constant air density are quantified. First, seasonal air density changes are studied at the global scale. Then, wind power density errors due to seasonal air density changes are computed. Finally, winter and summer energy production errors due to neglecting the changes in air density are computed by implementing the power curve of the National Renewable Energy Laboratorys 5 MW turbine. Results show relevant deviations for three variables (air density, wind power density, and energy production), mainly in the middle-high latitudes (Hudson Bay, Siberia, Patagonia, Australia, etc.). Locations with variations from −6% to 6% are identified from summers to winters in the Northern Hemisphere. Additionally, simulations with the aeroelastic code FAST for the studied turbine show that instantaneous power production can be affected by greater than 20% below the rated wind speed if a day with realistically high or low air density values is compared for the same turbulent wind speed.Item The Sailor diagram – A new diagram for the verification of two-dimensional vector data from multiple models(Copernicus GmbH, 2020-07) Sáenz, Jon; Carreno-Madinabeitia, Sheila; Esnaola, Ganix; González-Rojí, Santos J.; Ibarra-Berastegi, Gabriel; Ulazia, AlainA new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the objective, as in the Taylor diagram, of providing a visual diagnostic tool which allows an easy comparison of simulations by multiple models against a reference dataset. However, the Sailor diagram extends this ability to two-dimensional quantities such as currents, wind, horizontal fluxes of water vapour and other geophysical variables by adding features which allow us to evaluate directional properties of the data as well. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the two orthogonal directions (empirical orthogonal functions; EOFs) which best describe the vector data. Since there is no truncation of the retained EOFs, these orthogonal directions explain the total variability of the original dataset. We test the performance of this new diagram to identify the differences amongst the reference dataset and a series of model outputs by using some synthetic datasets and real-world examples with time series of variables such as wind, current and vertically integrated moisture transport. An alternative setup for spatially varying time-fixed fields is shown in the last examples, in which the spatial average of surface wind in the Northern and Southern Hemisphere according to different reanalyses and realizations from ensembles of CMIP5 models are compared. The Sailor diagrams presented here show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows us also to easily retrieve the individual diagnostics of the different components of the mean squared error and additional diagnostics which can be presented in tabular form.Item Seasonal Air Density Variations over The East of Scotland and The Consequences for Offshore Wind Energy(IEEE, 2018-12) Ulazia, Alain; Gonzalez-Roji, Santos J.; Ibarra-Berastegi, Gabriel; Carreno-Madinabeitia, Sheila; Saenz, Jon; Nafarrate, AnderIn this communication, offshore wind energy is studied around the East of Scotland, where, among other farms, the pioneering floating wind farm Hywind-Scotland is located. SIEMENS 160/6 turbines have been implemented in this farm, and we have thus used this turbine for our study. The main purpose is to compute the Capacity Factor (CF) changes due to air density variations in the study area. The impact of seasonal air density changes has been assessed as percentage reduction, but individual extreme cases have also been considered at Hywind-Scotland farm. Temperature, pressure, and wind speed data from the reanalysis ERA5 have been used for that. As a results, in winter, the CF increment due to low temperatures and denser air reaches values around 3% and summer-winter difference can reach the 4%, that is, 0.52 GWh of energy production for one SIEMENS 160/6.Item Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula(MDPI AG, 2019) Ulazia, Alain; Ibarra-Berastegi, Gabriel; Sáenz, Jon; Carreno-Madinabeitia, Sheila; González-Rojí, Santos J.A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.Item Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)(MDPI AG, 2020) Carreno-Madinabeitia, Sheila; Ibarra-Berastegi, Gabriel; Sáenz, Jon; Zorita, Eduardo; Ulazia, AlainThis study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts.