Reducing storage of global wind ensembles with stochastic generators
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Cemse Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, , Saudi Arabia
KAUST Grant NumberOSR-2015-CRG4-2640
Permanent link to this recordhttp://hdl.handle.net/10754/627535
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AbstractWind has the potential to make a significant contribution to future energy resources. Locating the sources of this renewable energy on a global scale is however extremely challenging, given the difficulty to store very large data sets generated by modern computer models. We propose a statistical model that aims at reproducing the data-generating mechanism of an ensemble of runs via a Stochastic Generator (SG) of global annual wind data. We introduce an evolutionary spectrum approach with spatially varying parameters based on large-scale geographical descriptors such as altitude to better account for different regimes across the Earth’s orography. We consider a multi-step conditional likelihood approach to estimate the parameters that explicitly accounts for nonstationary features while also balancing memory storage and distributed computation. We apply the proposed model to more than 18 million points of yearly global wind speed. The proposed SG requires orders of magnitude less storage for generating surrogate ensemble members from wind than does creating additional wind fields from the climate model, even if an effective lossy data compression algorithm is applied to the simulation output.
CitationJeong J, Castruccio S, Crippa P, Genton MG (2018) Reducing storage of global wind ensembles with stochastic generators. The Annals of Applied Statistics 12: 490–509. Available: http://dx.doi.org/10.1214/17-AOAS1105.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2640.
PublisherInstitute of Mathematical Statistics
JournalThe Annals of Applied Statistics