A computer vision approach for satellite-driven wind nowcasting over complex terrains

Abstract

Accurate wind speed and direction nowcasting in regions with complex terrains remains a challenge, and critical for applications like aviation. This study proposes a new methodology by harnessing Convolutional Neural Networks and Long Short-Term Memory models with satellite imagery to address wind predictions in a complex terrain, centered on Madeira International Airport, Portugal, using satellite data as input. Results demonstrated adeptness in capturing wind transitions, pinpointing shifts up to two hours ahead, with errors of 1.74 m s ^−1 and 30.98° for wind speed and direction, respectively. Highlighting its aptitude in capturing the intricate atmospheric dynamics of such areas, the study reinforces the viability of computer vision for remote sites where conventional monitoring is either inefficient or expensive. With the widespread availability of satellite imagery and extensive satellite coverage, this method presents a scalable approach for worldwide applications.

Keywords

• environmental remote sensing
• atmospheric science with AI
• atmospheric modeling
• wind forecast