Title: Reducing Storage of Global Wind Ensembles with Stochastic Generators,
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.