On the Application of Principal Component Analysis for Accurate Statistical-dynamical Downscaling of Wind Fields

Chávez-Arroyo, Roberto; Lozano-Galiana, Sergio; Rodrigo, Javier Sanz; Probst, Oliver

doi:10.1016/j.egypro.2013.08.009

Cited by 8 publications

(11 citation statements)

References 21 publications

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“…While the sources for discrepancies between the NARR‐ and ASOS‐based wind speeds are manifold, including the many modelling and data assimilation steps involved in producing the 10 m wind speed output in NARR, two possible explanations may be mentioned: (1) The topography model associated with the eta co‐ordinate system used in NARR corresponds to a rather crude representation of the true orography. Using mesoscale model output as an input to a CFD model with a detailed topography model Chávez et al (, ) showed for the specific case of Northwestern Mexico that a reasonable agreement could be obtained between the meso‐CFD model and the observational data, as opposed to the case where the direct output of several mesoscale models was used, where systematic over‐predictions of the measured wind resource were observed. (2) In spite of significant quality assurance efforts it cannot be ruled out that a certain fraction of (near‐) zero readings of the ASOS actually correspond to missing data, leading to a negative bias in the affected data sets.…”

Section: Resultsmentioning

confidence: 99%

A surface wind speed map for Mexico based on NARR and observational data

et al. 2015

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A systematic comparison of the surface wind speed records extracted from the North American Regional Reanalysis (NARR) database and the network of automated surface observation stations of the Mexican National Weather Service has been conducted. The work presented here is based on a multi-step procedure that initiates with a point-to-point comparison of surface observations and NARR grid point, followed by the reconstruction of missing data from the quality-controlled observational data set using an iterative technique based on principal component analysis (PCA). Subsequently, the empirical orthogonal function (EOF) sets for NARR and the reconstructed observational data set were compared globally using a group comparison criterion that is also based on PCA. Different observational periods were evaluated, leading to the highest similarity between the two sets for the case in the year 2010. A wind speed map for Mexico was created by joining the quality-controlled, filtered and reconstructed observational data with the NARR map using a weighting technique based on distance weighting, local similarity of the EOFs and data recovery.

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Section: Resultsmentioning

confidence: 99%

A surface wind speed map for Mexico based on NARR and observational data

et al. 2015

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“…Two other techniques recently published and used for comparison in the present work are the principal component analysis (PCA) based approach proposed by Chávez‐Arroyo et al . () and the SOM method used by Chávez‐Arroyo et al . ().…”

Section: Methods and Datamentioning

confidence: 99%

“…Practical solutions to the dilemma outlined above are hybrid methods, often termed statistical‐dynamical downscaling (SDD), which combine the deterministic approach of dynamical downscaling with statistical techniques. SDD methods can be roughly grouped into three categories: (1) approaches based on the classification of weather types (Frey‐Buness et al ., ), (2) algorithmic methods with suitable evaluation metrics where almost no pre‐classification by the user is required (Fuentes and Heimann, ; Cutler et al ., ; Hagemann, ; Chávez‐Arroyo et al ., ; Martinez et al ., ; Rife et al ., ; Tammelin et al ., ) and (3) the construction of statistical relations between long‐term and short‐term dynamical downscaled data (Hahmann et al ., ). Weather classification approaches are rather typical in the atmospheric sciences, and algorithmic methods have made a more recent appearance.…”

Section: Introductionmentioning

confidence: 97%

“…() were also addressed by Chávez‐Arroyo et al . (, 2015). They employed the stratified sampling technique (Rife et al ., ) to generate a large pool of candidate years that were ranked according to their similarity to the long‐term climate.…”

Section: Introductionmentioning

confidence: 97%

“…Chávez‐Arroyo et al . (, 2015) and Hagemann () used large scale atmospheric circulation predictors, which probe representativeness at a mesoscale level. Recently, Vanvyve et al .…”

Section: Introductionmentioning

confidence: 99%

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A novel approach to statistical‐dynamical downscaling for long‐term wind resource predictions

Chávez-Arroyo

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Sanz-Rodrigo

et al. 2017

Meteorological Applications

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A new method for the long-term prediction of the wind resource based on the concept of statistical-dynamical downscaling is presented. This new approach uses mean sea level pressure maps from global reanalysis data (National Centers for Environmental Prediction Department of Energy Atmospheric Model Intercomparison Project (NCEP-DOE AMIP-II)) and image processing techniques to identify a synthetic reference period which optimally matches the corresponding long-term maps. Four different image processing techniques, averaged into one image similarity error index, are used to evaluate image similarity. A representative set of days is selected by requiring the error index to be minimal. Validation of representativeness in terms of the wind resource for the Iberian domain is performed against 10 years of measured wind data from Navarra (Spain), as well as mesoscale simulations of the Iberian Peninsula. The new approach is shown to outperform not only the industry-standard method but also other recently proposed methods in its capability to achieve mesoscale level representativeness. A particular advantage of the new method is its capability of simultaneously providing a representative period for all potential wind farm sites located within large regional domains without requiring re-running of the method for different candidate sites.

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Seasonal and subseasonal wind power characterization and forecasting for the Iberian Peninsula and the Canary Islands: A systematic review

Bayo-Besteiro

García-Rodríguez

Labandeira

et al. 2021

Intl Journal of Climatology

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Renewable energy has a key role to play in the transition towards a low-carbon society. Despite its importance, relatively little attention has been focused on the crucial impact of weather and climate on energy demand and supply, or the generation or operational planning of renewable technologies. In particular, to improve the operation and longer-term planning of renewables, it is essential to consider seasonal and subseasonal weather forecasting. Unfortunately, reports that focus on these issues are not common in scientific literature. This paper presents a systematic review of the seasonal forecasting of wind and wind power for the Iberian Peninsula and the Canary Islands, a region leading the world in the development of renewable energies (particularly wind) and thus an important illustration in global terms. To this end, we consider the scientific literature published over the last 13 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021). An initial search of this literature produced 14,293 documents, but our review suggests that only around 0.2% are actually relevant to our purposes. The results show that the teleconnection patterns (North Atlantic Oscillation [NAO], East Atlantic [EA] and Scandinavian [SCAND]) and the stratosphere are important sources of predictability of winds in the Iberian Peninsula. We conclude that the existing literature in this crucial area is very limited, which points to the need for increased research efforts, that could lead to great returns. Moreover, the approach and methods developed here could be applied to other areas for which systematic reviews might be either useful or necessary.

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On the Application of Principal Component Analysis for Accurate Statistical-dynamical Downscaling of Wind Fields

Cited by 8 publications

References 21 publications

A surface wind speed map for Mexico based on NARR and observational data

A surface wind speed map for Mexico based on NARR and observational data

A novel approach to statistical‐dynamical downscaling for long‐term wind resource predictions

Seasonal and subseasonal wind power characterization and forecasting for the Iberian Peninsula and the Canary Islands: A systematic review

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