An R package for climate forecast verification

Manubens, Nicolau; Caron, Louis-Philippe; Hunter, Alasdair; Bellprat, Omar; Exarchou, Eleftheria; Fučkar, Neven S.; Garcı́a-Serrano, Javier; Massonnet, François; Ménégoz, Martin; Sicardi, Valentina; Batté, Lauriane; Prodhomme, Chloé; Torralba, Verónica; Cortesi, Nicola; Mula-Valls, Oriol; Serradell, Kim; Guémas, Virginie; Doblas‐Reyes, Francisco J.

doi:10.1016/j.envsoft.2018.01.018

Cited by 38 publications

(53 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A conservative interpolation method from the R package s2dverification (Manubens et al . ) has been employed to this end.…”

Section: Methodsmentioning

confidence: 99%

“…Acknowledgments are due to each of the four reanalysis production centres ECMWF, NASA, JMA, and NCEP/NCAR for facilitating access to the reanalysis data. We also thank the developers of the R package s2dverification (Manubens et al, 2018). Very special thanks are given to Pierre-Antoine Bretonnière and Margarida Samsó for their valuable work.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…We also thank the developers of the R package s2dverification (Manubens et al . ). Very special thanks are given to Pierre‐Antoine Bretonnière and Margarida Samsó for their valuable work.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…All grids have been interpolated to the F47(T62) horizontal grid, which is R1's grid and is the coarsest out of the five reanalysis grids. A conservative interpolation method from the R package s2dverification (Manubens et al, 2018) has been employed to this end. The differences in terms of seasonal mean wind speeds, the interannual variability (IAV)-computed separately for each season as the standard deviation of the seasonal means within the 1980-2017 period and normalized by the seasonal climatology of all years under consideration-and the long-term trends have been assessed.…”

Section: Reanalysis Intercomparisonmentioning

confidence: 99%

See 3 more Smart Citations

What global reanalysis best represents near‐surface winds?

Ramón

Lledó

Torralba

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

233

184

View full text Add to dashboard Cite

Since global reanalysis datasets first appeared in the 1990s, they have become an essential tool to understand the climate of the past. The wind power industry uses those products extensively for wind resource assessment, while several climate services for energy rely on them as well. Nowadays various datasets coexist, which complicates the selection of the most suitable source for each purpose. In an effort to identify the products that best represent the wind speed features at turbine hub heights, five state‐of‐the‐art global reanalyses have been analysed: ERA5, ERA‐Interim, the Japanese 55‐year Reanalysis (JRA55), the Modern Era Retrospective Analysis for Research and Applications‐2 (MERRA2), and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 (R1). A multi‐reanalysis ensemble approach is used to explore the main differences amongst these datasets in terms of surface wind characteristics. Then, the quality of the surface and near‐surface winds is evaluated with a set of 77 instrumented tall towers. Results reveal that important discrepancies exist in terms of boreal winter seasonal means, interannual variability (IAV), and decadal linear trends. The differences in the computation of these parameters, which are mainly concentrated inland, reach up to the order of magnitude of the parameters themselves. Comparison with in situ observations shows that the ERA5 surface winds offer the best agreement, correlating and reproducing the observed variability better than a multi‐reanalysis mean in 35.1% of the tall tower sites on a daily time‐scale. However, none of the reanalyses stands out from the others when comparing seasonal mean winds. Regarding the IAV, near‐surface winds from ERA5 offer the values closest to the observed IAV.

show abstract

“…A conservative interpolation method from the R package s2dverification (Manubens et al . ) has been employed to this end.…”

Section: Methodsmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Reanalysis Intercomparisonmentioning

confidence: 99%

See 2 more Smart Citations

What global reanalysis best represents near‐surface winds?

Ramón

Lledó

Torralba

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

233

184

View full text Add to dashboard Cite

show abstract

“…Frías et al [27] proposed an R package that is part of the climate4R bundle, thus, providing transparent access to the ECOMS-UDG climate data service, which allowed a flexible application of visualizeR to a wide variety of specific seasonal forecasting problems and datasets. Manubens et al [28] proposed an open-source R package: s2dverification v2.8.0 for the quality assessment of climate forecasts using state-of-the-art verification scores, in order to provide tools for each step of the forecast verification process: data retrieval, processing, calculation of verification measures, and visualization of the results. As abovementioned, statistical Software R can improve statistics, drawing, programming interface, and data input/output through the packages of users, and can provide various language interfaces, such as R, LaTeX, Java, C or C++, and Fortran.…”

Section: Domestic and Foreign Literature Analysis And Development Tecmentioning

confidence: 99%

A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

Chen

Yang

2018

Applied Sciences

View full text Add to dashboard Cite

This study focuses on presenting a development trend from the perspective of data-oriented evidence, especially open data and technologies, as those numbers can verify and prove current technology trends and user information requirements. According to the practical progress of Dr. What-Info I and II, this paper continues to develop Dr. What-Info III. Moreover, big data technology, the MapReduce paralleled decrement mechanism of the cloud information agent CEOntoIAS, which is supported by a Hadoop-like framework, Software R, and time series analysis are adopted to enhance the precision, reliability, and integrity of cloud information. Furthermore, the proposed system app receives a collective satisfaction score of 80% in terms of Quesenbery's 5Es and Nielsen ratings. In addition, the verification results of the interface design show that the human-machine interface of our proposed system can meet important design preferences and provide approximately optimal balance. The top-n experiment shows that the top-5 recommendations would be better for solving the traditional tradeoff between output quality and processing time. Finally, the system effectiveness experiments indicate that the proposed system receives an overall up-to-standard function rate of 87.5%, and such recommendations provide this system with high information correctness and user satisfaction. Although there is plenty of room for improvement in experience, the feasibility of this service architecture has been proven.

show abstract

Challenges in the selection of atmospheric circulation patterns for the wind energy sector

Torralba

González-Reviriego

Cortesi

et al. 2020

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

Atmospheric circulation patterns that prevail for several consecutive days over a specific region can have consequences for the wind energy sector as they may lead to a reduction of the wind power generation, impacting market prices or repayments of investments. The main goal of this study is to develop a user-oriented classification of atmospheric circulation patterns in the Euro-Atlantic region that helps to mitigate the impact of the atmospheric variability on the wind industry at seasonal timescales. Particularly, the seasonal forecasts of these frequencies of occurrence can be also beneficial to reduce the risk of the climate variability in wind energy activities. K-means clustering has been applied on the sea level pressure from the ERA5 reanalysis to produce a classification with three, four, five and six clusters per season. The spatial similarity between the different ERA5 classifications has revealed that four clusters are a good option for all the seasons except for summer when the atmospheric circulation can be described with only three clusters. However, the use of these classifications to reconstruct wind speed and temperature, key climate variables for the wind energy sector, has shown that four clusters per season are a good choice. The skill of five seasonal forecast systems in simulating the year-to-year variations in the frequency of occurrence of the atmospheric patterns is more dependent on the inherent skill of the sea level pressure than on the number of clusters employed. This result suggests that more work is needed to improve the performance of the seasonal forecast systems in the Euro-Atlantic domain to extract skilful forecast information from the circulation classification. Finally, this analysis illustrates that from a user perspective it is essential to consider the application when selecting a classification and to take into account different forecast systems.

show abstract

An R package for climate forecast verification

Cited by 38 publications

References 33 publications

What global reanalysis best represents near‐surface winds?

What global reanalysis best represents near‐surface winds?

A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

Challenges in the selection of atmospheric circulation patterns for the wind energy sector

Contact Info

Product

Resources

About