Reducing storage of global wind ensembles with stochastic generators

Jaehong Jeong, Stefano Castruccio, Paola Crippa, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Wind 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.

Original languageEnglish (US)
Pages (from-to)490-509
Number of pages20
JournalAnnals of Applied Statistics
Issue number1
StatePublished - Mar 2018


  • Axial symmetry
  • Nonstationarity
  • Spatio-temporal covariance model
  • Sphere
  • Stochastic generator
  • Surface wind speed

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Modeling and Simulation


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