Search

Mutriku has recently become the first commercial wave farm to release its operating data. The plant has 14 OWC operating turbines, and this study has conducted an analysis of hourly data corresponding to the 2014–2016 period. The plant's capacity factor has been calculated for this period, and its seasonal evolution characterized. Additionally, a plant efficiency index has been defined as the ratio between the wave energy flux at a reference buoy and the average power generation across the active turbines. The Mutriku wave farm's annual output in the period analysed has been 246,468.7 kW-h, with an average of around ten working turbines. The results indicate that Mutriku's average capacity factor is around 0.11, with higher values in winter than in summer. These values are below the capacity factors reported for other renewable energy sources. The plant efficiency index is 0.26, and further advances in regulation and control may also raise this parameter's values, as may lower rated power alternators. This will also help to improve the Mutriku wave plant's capacity factor, and OWC technology in general. [ABSTRACT FROM AUTHOR]

This paper analyzes the performance of three types of statistical models and a well-known physics-based model for forecasting the wave energy flux. The forecasts are run over horizons of 1–24 h at five buoys located in the Bay of Biscay. The data resolution is hourly. The 1999–2005 timeframe is used to train the models. The forecasts are run and evaluated over the six-year period from 2006 to 2012. The statistical forecasting models use three techniques: analogues, random forests (a machine learning algorithm) and a combination of the two. The physics model is the Wave Model (WAM). The forecasts are compared at a 95% confidence level with the simplest prediction – Persistence – and also with the nearest grid point of the WAM forecasts. Over horizons between 3 and 16–19 h at locations near the coast (where wave farms may be installed), the random forests models outperform the others, including WAM and Persistence. These models exploit the inherent predictability associated with the strong autocorrelation present in ocean energy values. The additional prognostic capabilities that random forests models provide over Persistence, are due to their ability to sucessfully incorporate the information that both, atmospheric and sea-state variables provide. [ABSTRACT FROM AUTHOR]

Abstract: In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network''s capability to correctly follow and represent SO2 fields in Bilbao, in the frame of a continuous network optimization process. The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area. To assess the spatial variability of SO2 measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R. [Copyright &y& Elsevier]

This work focuses on the prediction of hourly levels up to 8h ahead for five pollutants (SO2, CO, NO2, NO and O3) and six locations in the area of Bilbao (Spain). To that end, 216 models based on neural networks (NNs) were built. The database used to fit the NNs were historical records of the traffic, meteorological and air pollution networks existing in the area corresponding to year 2000. Then, the models were tested on data from the same networks but corresponding to year 2001. At a first stage, for each of the 216 cases, 100 models based on different types of neural networks were built using data corresponding to year 2000. The final identification of the best model was made under the criteria of simultaneously having at a 95% confidence level the best values of R 2, d 1, FA2 and RMSE when applied to data of year 2001. The number of hourly cases in which due to gaps in data predictions were possible range from 11% to 38% depending on the sensor. Depending on the pollutant, location and number of hours ahead the prediction is made, different types of models were selected. The use of these models based on NNs can provide Bilbao''s air pollution network originally designed for diagnosis purposes, with short-term, real time forecasting capabilities. The performance of these models at the different sensors in the area range from a maximum value of R 2 =0.88 for the prediction of NO2 1h ahead, to a minimum value of R 2 =0.15 for the prediction of ozone 8h ahead. These boundaries and the limitation in the number of cases that predictions are possible represent the maximum forecasting capability that Bilbao''s network can provide in real-life operating conditions. [Copyright &y& Elsevier]

This study analysed temporal and spatial changes in offshore wind power density (WPD) and capacity factor (CF) around the Iberian Peninsula during the 20th century by analysing data from ERA20 and ERA5. Both WPD and CF were calculated using reanalysis data considering a wind turbine with a hub height of 90 m and incorporating the effect of air density changes. Since ERA5 assimilates more observations, the data from ERA20 was bias-corrected using quantile matching, with ERA5 reanalysis data as the reference. As both variables are based on wind speed (WS), this variable was also corrected and analysed. The results show that the mean values for WPD, CF, and WS during the 20th century were highest in the Atlantic zone and the Gulf of Lyon and lowest around the Balearic Islands. The results of the assessment of decadal trends using the Theil–Sen estimator show that all indicators increased significantly in the waters of the Iberian Peninsula during the study period (1900–2010). Considering the mean slope over this period, the change over the entire period could amount to 174 Wm-2 for WPD, 8.8% for CF, and 1.1 ms−1 for WS. Based on these changes, offshore wind turbines would have increased their returns by approximately 20% over the 11 decades. • Wind power density and capacity factor changes over the 20th century are analysed. • ERA-20C is bias-corrected by quantile matching using ERA5 reanalysis data. • The most energetic waters in the Iberian Peninsula are the Atlantic and Gulf of Lyon. [ABSTRACT FROM AUTHOR]

Mutriku is a wave farm on the Spanish coast of the Bay of Biscay that has now been continuously supplying electricity for more than nine years. Since 1979, there has been a growing trend in wave energy flux for the whole Bay of Biscay. ERA5 data at the grid point nearest to Mutriku indicate an increase of 0.146 kW/m per decade for the 1979–2019 period. In this paper, a Self-Organizing Map (SOM) with a 2X5 architecture has been fitted to identify ten major sea-state types, each with a distinctive electricity generation pattern at a daily scale. This rendered it possible to reconstruct the daily electric power that would have been generated if the Mutriku wave farm had been operational over the entire 1979–2019 period and, accordingly, evaluate the impact that the observed changes in the wave climate and associated wave energy flux would have had on the electricity production. The results indicate that the electricity production or power flow would have remained constant during that period despite the increasing trend in wave energy flux. This is due to the regulation procedures and mechanisms used in the operation of Mutriku's Oscillating Water Column (OWC) wave energy converters, which dampen the effect of the increasing trend observed. The main conclusion is that the power flow levels off above a given threshold, making it more stable than the wave energy flux. [Display omitted] • In the 1979-2019 period, the wave energy flux exhibits an increasing trend in the Bay of Biscay. • Mutriku is a OWC wave farm located in the Bay of Biscay. • The changes in electricity generation driven by changes in wave energy flux have been analysed. • Self-Organizing Maps have been used to estimate the electric yield at Mutriku for the 1979-2019 period. • The electric production remained constant in the 1979-2019 period despite the positive trend of the energy carried by waves. [ABSTRACT FROM AUTHOR]

We present a new method for analysing the fatigue loads of offshore floating wind turbines over the long term. In this method, bias-corrected wind and wave data from the 20th century (ERA20C versus ERA5 reanalysis) is used for an energetic sea location in western Ireland. To reduce the computational cost and theoretical complexity, the sea states were clustered into categories to indicate how these categories evolve over three climate periods during a period of 30 years (1921–1950, 1951–1980, and 1981–2010). OpenFAST aeroelastic code simulation in floating mode was then conducted at each sea state for spar-type 5-MW and semi-submersible 15-MW wind turbines. This shows the damage loads and fatigue increments over the century, with some relevant cases (rotor torque, blade pitch and flapping, and tower side–side and fore–aft moments) showing fatigue increments of 5%–8%. Thus, in the new method, historical data are used to create a model to represent the evolution of sea states and corresponding fatigue over the long term, which can be applied globally to future projections. • Floating offshore wind energy interesting for decarbonization. • Climate change can affect fatigue loads of floating wind turbines. • West of Ireland presents strong historical trends for fatigue loads. • Some elements of the floating structure show increments of 5%–8% per century. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

This paper combines random forests with physics-based models to forecast the electricity output of the Mutriku wave farm on the Bay of Biscay. The period analysed was 2014–2016, and the forecast horizon was 24 h in 4-h steps. The Random Forest (RF) machine-learning technique was used, with three sets of inputs: i) the electricity generated at Mutriku, ii) the wave energy flux (WEF) prediction made by the ECMWF wave model at Mutriku's nearest gridpoint, and iii) ocean and atmospheric data for the Bay of Biscay. For this last input, extended empirical orthogonal functions (EOFs) were calculated to reduce the dimensionality of these data, while retaining most of the information. The forecasts are evaluated using the R-Squared, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The model easily outperforms a persistence forecast at 8–10 h and beyond. The most accurate forecasts are achieved by using all three of these inputs. This approach may help to effectively integrate wave farms into the electricity market. • The electric power generated at the Mutriku wave farm was predicted for a 24-h forecast lead time. • The RF model proved to be a better prediction tool than Persistence itself beyond 8–10 h. • This is the first time the output power of a fully operational wave farm has been forecast up to a 24-h ahead horizon through the use of logged electric power data. • Wave energy needs to maximise the accuracy of the forecasting methods to solve the intermittency and variability problems associated with renewable energies and the study presented here makes an initial contribution to this objective. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of different analog-based downscaling models for probabilistic quantitative precipitation forecasts over the metropolitan area of Bilbao, Spain. The analog-based statistical downscaling models used are a probability of the exceedance models in which the probability is derived from the probabilities given by a set of analogs and three Bayesian models. Results show that the differences in the performance of the models are subtle. The simplest model, which makes no use of Bayesian methods, performs better than the other models in the forecast of very low precipitation events, very likely due to the quality of the found analogs, since these categories are very populated in the phase space and forecast is very easy. However, as precipitation rates increase, Bayesian models perform better than the simple one based on probability of exceedance from individual analogs. The Bayesian models involving precipitation in the computation of the likelihood show in general the smallest biases in the forecast versus observed probabilities. [ABSTRACT FROM AUTHOR]

The 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. • Air density presents relevant seasonal variations for wind energy assessment. • ERA5 is used to represent global maps of these variations. • Variations from −6% to 6% in capacity factor are identified (Hudson Bay). • FAST simulations show variations greater than 20% in instantaneous power. [ABSTRACT FROM AUTHOR]

A 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. [ABSTRACT FROM AUTHOR]

A 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 idea of extending the Taylor diagram to two dimensional quantities such as currents, wind, or horizontal fluxes of water vapour, salinity, energy and other geophysical variables. 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 empirical orthogonal functions of the data. We test the performance of this new diagram to identify the differences amongst the reference dataset and the different model outputs by using examples with wind, current, vertically integrated moisture transport and wave energy flux time series. An alternative setup is shown in the last examples, where the spatial average of surface wind in the Northern and Southern Hemispheres according to different reanalyses and realizations of CMIP5 models are compared. The examples of use of the Sailor diagram presented 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 also to easily retrieve the individual diagnostics of the different components of the mean squared error. [ABSTRACT FROM AUTHOR]

There have been numerous statistical and dynamical downscaling model comparisons. However, differences in model skill can be distorted by inconsistencies in experimental set-up, inputs and output format. This paper harmonizes such factors when evaluating daily precipitation downscaled over the Iberian Peninsula by the Statistical DownScaling Model (SDSM) and two configurations of the dynamical Weather Research and Forecasting Model (WRF) (one with data assimilation (D) and one without (N)). The ERA-Interim reanalysis at 0. 75 ∘ resolution provides common inputs for spinning-up and driving the WRF model and calibrating SDSM. WRF runs and SDSM output were evaluated against ECA&D stations, TRMM, GPCP and EOBS gridded precipitation for 2010–2014 using the same suite of diagnostics. Differences between WRF and SDSM are comparable to observational uncertainty, but the relative skill of the downscaling techniques varies with diagnostic. The SDSM ensemble mean, WRF-D and ERAI have similar correlation scores ( r = 0.45 –0.7), but there were large variations amongst SDSM ensemble members ( r = 0.3 –0.6). The best Linear Error in Probability Space ( LEPS = 0.001 –0.007) and simulations of precipitation amount were achieved by individual members of the SDSM ensemble. However, the Brier Skill Score shows these members do not improve the prediction by ERA-Interim, whereas precipitation occurrence is reproduced best by WRF-D. Similar skill was achieved by SDSM when applied to station or gridded precipitation data. Given the greater computational demands of WRF compared with SDSM, clear statements of expected value-added are needed when applying the former to climate impacts and adaptation research. [ABSTRACT FROM AUTHOR]

Hywind-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 ( C F ) 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 C F 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. [ABSTRACT FROM AUTHOR]

The use of the Noah Land Surface Model (LSM) in regional modeling has increased during the last decades, while data assimilation is not so commonly used. Thus, this study focuses on the impact of both options on WRF simulations, and how they affect the summer surface temperature bias and energy balance over the Iberian Peninsula (IP). For that purpose, a numerical downscaling exercise over the IP was run nesting the WRF model inside ERA-Interim. Four different configurations of the model were run during 2010-2014 after a one-year spin-up (2009). In the first experiment (N), including the Noah LSM for the calculation of the soil temperature and moisture, the boundary conditions drive the model after the initialization. The second experiment (D) includes also the LSM, but the 3DVAR data assimilation step is run every six hours (00Z, 06Z, 12Z and 18Z). The third and fourth experiments (S and C respectively) present the same configurations as the previous experiments, but without the Noah LSM. The domain covers the IP with a 15x15 km2 grid and 51 vertical levels. The sea surface temperature is updated daily and observations from the PREPBUFR dataset (NCEP ADP Global Upper Air and Surface Weather Observations) are assimilated inside a 120-minute window centered at the analysis times in the 3DVAR assimilated runs (C and D). Results show that the experiment including the data assimilation and the Noah LSM (D) produces the best correlations and the smallest RMSE. Both experiments including the Noah LSM (D and N) showed similar biases, but the RMSE obtained by N is not as good as that from D. A larger bias is obtained by the C experiment (with data assimilation but without the LSM), but not as remarkable as that from the experiment without both options (S). However, the correlations obtained by C are comparable to those from D. According to these results, the effect of the Noah LSM in temperature is similar to that derived from the data assimilation scheme for temperature bias. Conversely, for day-to-day variations (correlation coefficient), data assimilation performs much better. Thus, in terms of mean square error, the combination of land surface model and data assimilation yields the optimum combination. The spatial distribution of the Bowen ratio was also calculated for each experiment. Those experiments including the Noah LSM are the ones obtaining the most similar results to those obtained by previous studies, particularly the N experiment. The mean Bowen Ratio value for the experiments without the Noah LSM is around 0.5, while for D is 1.48 and for N 3.78. If we focus on the energy balance, the experiments including Noah LSM are those able to better close the balance (residual below 25 W/m2 – mean value for the entire IP). The residual of the experiments without Noah LSM can reach values of 200 W/m2 because the estimation of Ground Flux is severely affected by the use of a diffusive soil scheme. [ABSTRACT FROM AUTHOR]

Abstract The 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. Highlights • A new R-package providing new functions in atmospheric thermodynamics is presented. • The package provides analyses and Stüve diagrams not previously available in R. • A case-study (2010–2014) of soundings over the Iberian Peninsula is presented. • The package can play a key role in advanced forecast and diagnostic systems. • The most critical functions are written in C to speed up computation. [ABSTRACT FROM AUTHOR]

We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to single and multi-core processors. They compute multivariate PDFs by means of kernels, the optimal bandwidth using smoothed bootstrap and the index of agreement between multidimensional PDFs. Their use is illustrated with two case-studies. The first one assesses the ability of seven global climate models to reproduce the seasonal cycle of zonally averaged temperature. The second case study analyzes the ability of an oceanic reanalysis to reproduce global Sea Surface Temperature and Sea Surface Height. Results show that the proposed methodology is robust to variations in the optimal bandwidth used. The technique is able to process multivariate datasets corresponding to different physical dimensions. The methodology is very sensitive to the existence of a bias in the model with respect to observations. [ABSTRACT FROM AUTHOR]

This paper analyses the ability exhibited by seven coupled global climate models of the Climate Model Inter-comparison Project 3 used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change to simulate current zonally averaged surface air temperature (ZASAT) meridional profiles. The expansion in second order of ZASAT profiles by means of Legendre polynomials was compared with the same expansion carried out over the ZASAT profiles provided by the ERA40 and National Centers for Environmental Prediction reanalysis from 1961 to 1998. According to the theoretical support provided by the one-dimensional energy balance models (1D-EBMs), the Legendre coefficients corresponding to the ZASAT profile can be qualitatively interpreted as the independent modes that represent the meridional energy flux from the equator to the poles. We find that three models, MIROC3.2-MR, MIROC3.2-HR and MPI-ECHAM5 may be considered as the models that best reproduce the meridional structure of current ZASAT, although the differences between the models are not really large. Consequently, the results shown in this paper support the accuracy of the models in representing the poleward meridional heat fluxes and global thermal inertia under the qualitative interpretation provided by the 1D-EBM approach. [ABSTRACT FROM AUTHOR]

This paper reports analysis of the ability of 24 coupled global climate models that were used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change to simulate the current monthly seasonal cycle of sea level pressure, surface air temperature and precipitation over the Iberian Peninsula in the last two decades of the twentieth century. The period investigated runs from 1979 to 1998. In order to assess the performance of the models, averaged seasonal cycles and probability density functions (PDFs) calculated from model simulations are compared with the corresponding seasonal cycles, whilst PDFs are also obtained using the data from the ERA40 reanalysis and the Global Precipitation Climatology Project. We found that simulated PDFs generally provided a better fit to actual PDFs than seasonal cycles do. This conclusion indicates that when evaluating model performance, the climate variability as measured by means of PDFs is not the only climatic element that should be tested. Regarding the comparison based on the seasonal cycle, results also show that the root mean square skill score is more useful than the r skill score. To rank the AR4 models, sea level pressure, surface air temperature and precipitation variables were selected and a group of five AR4 models were identified as the models which best reproduce current climate in the area: MIROC3.2-HIRES, MPI-ECHAM5, GFDL-CM2.1, BCCR-BCM2.0 and UKMO-HADGEM1. The rank obtained should not be understood in a hierarchical manner because there is a certain degree of internal variability in the model ensembles. Finally, it should be noted that these results are in good agreement with other classifications found in the scientific literature. [ABSTRACT FROM AUTHOR]

The question of how to obtain the best inocula for conventional biofilters arises when an acclimation/adaptation procedure is to be applied. Bearing in mind that no standardized procedure for acclimating inocula exists, certain preliminary strategies for obtaining an active inoculum from wastewater treatment sludge are proposed in this work. Toluene was the contaminant to be degraded. Concerning the prior separation of sludge phases, no obvious advantage was found in separating the supernatant phase of the sludge before acclimation. As far as a continuous or discontinuous acclimation mode is concerned, the latter is recommended for rapidly obtaining acclimated sludge samples by operating the system for no longer than 1 month. The continuous mode rendered similar degradation rates, although it required longer operating time. Nevertheless, the great advantage of the continuous system lay in the absence of daily maintenance and the ready availability of the activated sample. [ABSTRACT FROM AUTHOR]

Biotechnology has emerged as an affordable, effective, and eco-friendly alternative to treat carbon disulfide (CS2) containing waste gases. Carbon disulfide is a chemical of widespread use both in the past and in the present. Its industry demand has dramatically changed over the last two decades and is expected to grow in future in step with those industries involving fibre, mining, rubber products, and the agro-chemical sector. This compound, classified as a hazardous air pollutant about 20 years ago, has been re-appraised, as increasingly restrictive pollution standards are expected to be introduced. An array of physical-chemical technologies for treating CS2 containing air are already in use, but they have a series of drawbacks, such as high energy consumption (incineration, thermal oxidation), the immobilization of the contaminant solely from one phase to another (adsorption), and the generation of secondary by-products that require additional treatment (hydrolysis). Thus, technological research on affordable processing alternatives is of considerable interest for related industries using or producing CS2. The biological technologies whose efficiency is based on the activity of microorganisms to biodegrade air pollutants overcome many of the disadvantages of conventional techniques and are especially useful for the removal of relatively low concentrations of pollutants. In this paper, the main properties, sources, and uses of CS2 are summarized, and alternative biotreatments for CS2 are reviewed. Several applications of the technical and economical feasibility of biofilters and biotrickling filters are shown, although their widespread industrial application still requires further research. [ABSTRACT FROM AUTHOR]

In this paper, we have estimated the spatiotemporal distribution of moisture recycling over the Iberian Peninsula (IP). The recycling ratio was computed from two simulations over the IP using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 15 km spanning the period 2010–2014. The first simulation (WRF N) was nested inside the ERA-Interim with information passed to the domain through the boundaries. The second run (WRF D) is similar to WRF N, but it also includes 3DVAR data assimilation every six hours (12:00 a.m., 6:00 a.m., 12:00 p.m. and 6:00 p.m. UTC). It was also extended until 2018. The lowest values of moisture recycling (3%) are obtained from November to February, while the highest ones (16%) are observed in spring in both simulations. Moisture recycling is confined to the southeastern corner during winter. During spring and summer, a gradient towards the northeastern corner of the IP is observed in both simulations. The differences between both simulations are associated with the dryness of the soil in the model and are higher during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture. [ABSTRACT FROM AUTHOR]

In addition to human error, manufacturing tolerances for blades and hubs cause pitch angle misalignment in wind turbines. As a consequence, a significant number of turbines used by existing wind farms experience power production loss and a reduced turbine lifetime. Existing techniques, such as photometric technology and laser-based methods, have been used in the wind industry for on-field pitch measurements. However, in some cases, regular techniques have difficulty achieving good and accurate measurements of pitch angle settings, resulting in pitch angle errors that require cost-effective correction on wind farms. Here, the authors present a novel patented method based on laser scanner measurements. The authors applied this new method and achieved successful improvements in the Annual Energy Production of various wind farms. This technique is a benchmarking-based approach for pitch angle calibration. Two case studies are introduced to demonstrate the effectiveness of the pitch angle calibration method to yield Annual Energy Production increase. [ABSTRACT FROM AUTHOR]

The wave energy resource in the Chilean coast shows particularly profitable characteristics for wave energy production, with relatively high mean wave power and low inter-annual resource variability. This combination is as interesting as unusual, since high energetic locations are usually also highly variable, such as the west coast of Ireland. Long-term wave resource variations are also an important aspect when designing wave energy converters (WECs), which are often neglected in resource assessment. The present paper studies the long-term resource variability of the Chilean coast, dividing the 20th century into five do-decades and analysing the variations between the different do-decades. To that end, the ERA20C reanalysis of the European Centre for Medium-Range Weather Forecasts is calibrated versus the ERA-Interim reanalysis and validated against buoy measurements collected in different points of the Chilean coast. Historical resource variations off the Chilean coast are compared to resource variations off the west coast in Ireland, showing a significantly more consistent wave resource. In addition, the impact of historical wave resource variations on a realistic WEC, similar to the Corpower device, is studied, comparing the results to those obtained off the west coast of Ireland. The annual power production off the Chilean coast is demonstrated to be remarkably more regular over the 20th century, with variations of just 1% between the different do-decades. [ABSTRACT FROM AUTHOR]