Predicciones climáticas estacionales para el sector de la energía eólica : métodos y herramientas para el desarrollo de un servicio climático

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física de la Tierra y Astrofísica, leída el 14-06-2019Seasonal forecasts have shown high potential for their application in different socioeconomic sectors (e.g. energy, agriculture, transport, tourism, health, ...). Nevertheless, the deployment of these forecasts in different decision-making processes requires that the seasonal forecasts are adapted to be easily integrated into different applications. To satisfy this need, the climate services research line has recently emerged to transform climate information into products that can be used by the society and the industry. One of the sectors that could benefit the most from seasonal forecasts is the wind energy sector. Wind energy is one of the most important sources of renewable energy for the mitigation of climate change effects on the society..Las predicciones climáticas estacionales han mostrado un gran potencial para su aplicación en distintos sectores socioeconómicos (energía, agricultura, transporte, turismo, salud, etc). Sin embargo, la integración de estas predicciones en procesos de toma de decisión requiere que éstas sean adaptadas para poder ser utilizadas de una manera automática y sencilla en distintas actividades. Con el fin de satisfacer esta necesidad ha surgido la línea de investigación de los servicios climáticos, que tiene como objetivo la transformación de información climática en productos que puedan beneficiar a la sociedad y la industria. Uno de los sectores que puede obtener un mayor beneficio de las predicciones estacionales es el sector de la energía eólica, que es una de las fuentes de energía renovable más importantes para la mitigación de los efectos del cambio climático...Depto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEunpu

Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison

Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980–2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.The authors acknowledge funding support from the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2), the New European Wind Atlas (NEWA) project funded by ERA-NET Plus, Topic FP7 ENERGY.2013.10.1.2, the RESILIENCE (CGL2013–41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO), and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Thanks to Daniel Cabezón and Sergio Lozano for their valuable feedback. We acknowledge the s2dverification R-based package (http://cran.rproject. org/web/packages/s2dverification) developers. Finally, we would like to thank Pierre-Antoine Bretonniere, Júlia Giner, Nicolau Manubens and Javier Vegas for their technical support at different stages of this project.Peer ReviewedPostprint (published version

Seasonal predictions of energy-relevant climate variables through Euro-Atlantic Teleconnections

The goal of this analysis is the better understanding of how the large-scale atmospheric patterns affect the renewable resources over Europe and to investigate to what extent the dynamical predictions of the large-scale variability might be used to formulate empirical prediction of local climate conditions (relevant for the energy sector). The increasing integration of renewable energy into the power mix is making the electricity supply more vulnerable to climate variability, therefore increasing the need for skillful weather and climate predictions. Forecasting seasonal variations of energy relevant climate variables can help the transition to renewable energy and the entire energy industry to make better informed decision-making. At seasonal timescale climate variability can be described by recurring and persistent, large-scale patterns of atmospheric pressure and circulation anomalies that interest vast geographical areas. The main patterns of the North Atlantic region (Euro Atlantic Teleconnections, EATCs) drive variations in the surface climate over Europe. We analyze reanalysis dataset ERA5 and the multi-system seasonal forecast service provided by Copernicus Climate Change Service (C3S). We found that the observed EATC indices are strongly correlated with surface variables. However, the observed relationship between EATC patterns and surface impacts is not accurately reproduced by seasonal prediction systems. This opens the door to employ hybrid dynamical-statistical methods. The idea consists in combining the dynamical seasonal predictions of EATC indices with the observed relationship between EATCs and surface variables. We reconstructed the surface anomalies for multiple seasonal prediction systems and benchmarked these hybrid forecasts with the direct variable forecasts from the systems and also with the climatology. The analysis suggests that hybrid methodology can bring several improvements to the predictions of energy relevant Essential Climate Variables.This work was supported by the European Union’s Horizon 2020 research and innovation programme [Grant Numbers. No 776787, H2020 S2S4E] and by the National Italian project PAR 2019–2021 1.8 ‘Energia dal Mare’.Peer ReviewedPostprint (published version

Uncertainty in near-surface wind speed trends at seasonal time scales

Observational studies have identified wind speed trends in the last decades [1,2] attributed to several factors such as changes in the land use, aerosol emissions or atmospheric circulation. However, in spite of the potential impact of this long-term variability in wind energy activities, this type of variability has not been fully characterized yet. As a consequence such information is not currently incorporated in wind power decision-making processes related to planning and management. long-term wind speed variability. For some of these users it is still difficult to identify the most suitable dataset for their specific needs, because a comparison of the quality of the wind speed data from different reanalyses at global scale is not readily available. For this reason, the present study investigates the wind speed long-term trends at global scale in the last decades (1981-2015) using three state-of-the-art reanalyses: ERA-Interim (ERA-I), the Japanese 55-year Reanalysis (JRA-55) and Modern Era Retrospective-Analysis for Research and Applications (MERRA-2)

On the reliability of global seasonal forecasts: sensitivity to ensemble size, hindcast length and region definition

One of the key quality aspects in a probabilistic prediction is its reliability. However, this property is difficult to estimate in the case of seasonal forecasts due to the limited size of most of the hindcasts that are available nowadays. To shed light on this issue, this work presents a detailed analysis of how the ensemble size, the hindcast length and the number of points pooled together within a particular region affect the resulting reliability estimates. To do so, we build on 42 land reference regions recently defined for the IPCC-AR6 and assess the reliability of global seasonal forecasts of temperature and precipitation from the European Center for Medium Weather Forecasts SEAS5 prediction system, which is compared against its predecessor, System4. Our results indicate that whereas longer hindcasts and larger ensembles lead to increased reliability estimates, the number of points that are pooled together within a homogeneous climate region is much less relevant.This research has been partially supported by the AfriCultuReS (“Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing”) and FOCUS-Africa projects, which received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreements No. 77465 and 869575, respectively.Peer ReviewedPostprint (published version

State-of-the-art climate predictions for energy climate services

Seasonal predictions of 10-m wind speed can be used by the wind energy sector in a number of decision making processes. Two different techniques of post-processing are applied in order to correct the unavoidable systematic errors present in all forecast systems. Besides an assessment of the impact of these corrections on the quality of the probabilistic forecast system is provided

State-of-the-art climate predictions for energy climate services

Seasonal predictions of 10-m wind speed can be used by the wind energy sector in a number of decision making processes. Two different techniques of post-processing are applied in order to correct the unavoidable systematic errors present in all forecast systems. Besides an assessment of the impact of these corrections on the quality of the probabilistic forecast system is provided

Multi-model seasonal forecasts for the wind energy sector

An assessment of the forecast quality of 10 m wind speed by deterministic and probabilistic verification measures has been carried out using the original raw and two statistical bias-adjusted forecasts in global coupled seasonal climate prediction systems (ECMWF-S4, METFR-S3, METFR-S4 and METFR-S5) for boreal winter (December–February) season over a 22-year period 1991–2012. We follow the standard leave-one-out cross-validation method throughout the work while evaluating the hindcast skills. To minimize the systematic error and obtain more reliable and accurate predictions, the simple bias correction (SBC) which adjusts the systematic errors of model and calibration (Cal), known as the variance inflation technique, methods as the statistical post-processing techniques have been applied. We have also built a multi-model ensemble (MME) forecast assigning equal weights to datasets of each prediction system to further enhance the predictability of the seasonal forecasts. Two MME have been created, the MME4 with all the four prediction systems and MME2 with two better performing systems. Generally, the ECMWF-S4 shows better performance than other individual prediction systems and the MME predictions indicate consistently higher temporal correlation coefficient (TCC) and fair ranked probability skill score (FRPSS) than the individual models. The spatial distribution of significant skill in MME2 prediction is almost similar to that in MME4 prediction. In the aspect of reliability, it is found that the Cal method has more effective improvement than the SBC method. The MME4_Cal predictions are placed in close proximity to the perfect reliability line for both above and below normal categorical events over globe, as compared to the MME2_Cal predictions, due to the increase in ensemble size. To further compare the forecast performance for seasonal variation of wind speed, we have evaluated the skill of the only raw MME2 predictions for all seasons. As a result, we also find that winter season shows better performance than other seasons.Peer ReviewedPostprint (author's final draft

Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.The authors acknowledge funding support from the RESILIENCE (CGL2013-41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Special thanks to Nube Gonzalez-Reviriego and Albert Soret for helpful comments and discussion. We also acknowledge the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2) and the New European Wind Atlas (NEWA) project funded from ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2. We acknowledge the s2dverification and SpecsVerification R-based packages. Finally we would like to thank Pierre-Antoine Bretonnière, Oriol Mula and Nicolau Manubens for their technical support at different stages of this project.Peer ReviewedPostprint (author's final draft