Wind models and cross-site interpolation for the refugee reception islands in Greece

TL;DR

This paper analyzes wind data from Greek Aegean Sea islands to develop statistical models for short-term wind forecasting, aiding in assessing sea crossing risks for refugees.

Contribution

It introduces ARMA-based models for wind prediction across multiple sites, revealing significant spatial correlations and improving short-term forecasting accuracy.

Findings

ARMA(7,5) models achieve RMSE < 1.9 km/h for 1-day ahead wind speed

Strong cross-site wind correlations identified, useful for predictive modeling

Data-driven approaches reveal counter-intuitive spatial associations

Abstract

In this study, the wind data series from five locations in Aegean Sea islands, the most active `hotspots' in terms of refugee influx during the Oct/2015 - Jan/2016 period, are investigated. The analysis of the three-per-site data series includes standard statistical analysis and parametric distributions, auto-correlation analysis, cross-correlation analysis between the sites, as well as various ARMA models for estimating the feasibility and accuracy of such spatio-temporal linear regressors for predictive analytics. Strong correlations are detected across specific sites and appropriately trained ARMA(7,5) models achieve 1-day look-ahead error (RMSE) of less than 1.9 km/h on average wind speed. The results show that such data-driven statistical approaches are extremely useful in identifying unexpected and sometimes counter-intuitive associations between the available spatial data nodes, which is very important when designing corresponding models for short-term forecasting of sea condition, especially average wave height and direction, which is in fact what defines the associated weather risk of crossing these passages in refugee influx patterns.