Spatial Temporal Approach for High-Resolution Gridded Wind Forecasting across Southwest Western Australia

TL;DR

This paper presents a spatial-temporal machine learning model for high-resolution wind forecasting in Southwest Western Australia, improving accuracy and reliability over large areas and various prediction horizons.

Contribution

It introduces a novel approach combining diverse meteorological data and terrain features for high-resolution wind prediction across extensive regions.

Findings

Enhanced wind forecasting accuracy demonstrated.

Effective integration of sparse observational data.

Model performs well across different spatial and temporal scales.

Abstract

Accurate wind speed and direction forecasting is paramount across many sectors, spanning agriculture, renewable energy generation, and bushfire management. However, conventional forecasting models encounter significant challenges in precisely predicting wind conditions at high spatial resolutions for individual locations or small geographical areas (< 20 km2) and capturing medium to long-range temporal trends and comprehensive spatio-temporal patterns. This study focuses on a spatial temporal approach for high-resolution gridded wind forecasting at the height of 3 and 10 metres across large areas of the Southwest of Western Australia to overcome these challenges. The model utilises the data that covers a broad geographic area and harnesses a diverse array of meteorological factors, including terrain characteristics, air pressure, 10-metre wind forecasts from the European Centre for Medium-Range Weather Forecasts, and limited observation data from sparsely distributed weather stations (such as 3-metre wind profiles, humidity, and temperature), the model demonstrates promising advancements in wind forecasting accuracy and reliability across the entire region of interest. This paper shows the potential of our machine learning model for wind forecasts across various prediction horizons and spatial coverage. It can help facilitate more informed decision-making and enhance resilience across critical sectors.