# Reducing Storage of Global Wind Ensembles with Stochastic Generators

## TL;DR

This paper introduces a stochastic generator model for global wind data that significantly reduces storage needs while accurately reproducing wind ensemble data, facilitating large-scale renewable energy analysis.

## Contribution

It develops a novel evolutionary spectrum-based statistical model with spatially varying parameters to efficiently generate surrogate wind data ensembles.

## Key findings

- Requires orders of magnitude less storage than climate model outputs.
- Effectively reproduces wind data across diverse geographical regimes.
- Handles large datasets with a multi-step likelihood estimation approach.

## 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.