Translator Disclaimer
March 2018 Reducing storage of global wind ensembles with stochastic generators
Jaehong Jeong, Stefano Castruccio, Paola Crippa, Marc G. Genton
Ann. Appl. Stat. 12(1): 490-509 (March 2018). DOI: 10.1214/17-AOAS1105

Abstract

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.

Citation

Download Citation

Jaehong Jeong. Stefano Castruccio. Paola Crippa. Marc G. Genton. "Reducing storage of global wind ensembles with stochastic generators." Ann. Appl. Stat. 12 (1) 490 - 509, March 2018. https://doi.org/10.1214/17-AOAS1105

Information

Received: 1 June 2017; Revised: 1 September 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894715
MathSciNet: MR3773402
Digital Object Identifier: 10.1214/17-AOAS1105

Rights: Copyright © 2018 Institute of Mathematical Statistics

JOURNAL ARTICLE
20 PAGES


SHARE
Vol.12 • No. 1 • March 2018
Back to Top