Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical models

TL;DR

This paper introduces a statistical space-time model that integrates physical models and historical data to generate probabilistic wind speed forecasts, improving accuracy over traditional numerical predictions.

Contribution

It presents a novel Gaussian multivariate framework combining multiple data sources for enhanced probabilistic wind speed prediction.

Findings

Improved mean-squared forecast accuracy

Enhanced probabilistic forecast scores

Realistic wind scenario generation

Abstract

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements along with model predictions in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on a ground wind speed forecast for several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, the samples are shown to produce realistic wind scenarios based on the sample spectrum.