Translating NWP Outputs into UAV-Specific Predictions Using Machine Learning

David Sládek

2025

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical, weather-sensitive operations. However, the direct use of Numerical Weather Prediction (NWP) model outputs often fails to address the specific operational thresholds and spatial–temporal needs of UAV missions. This study introduces a machine learning (ML) framework that translates standard NWP forecasts into UAV-specific feasibility assessments. We integrate both global (GFS) and local high-resolution (ARPEGE, AROME) models to generate real-time, interpretable indices or GO/NO-GO indicators tailored to UAV performance limits. Our case study over Nantes (France) for the 2017–2023 period demonstrates the added value of ML-enhanced predictions in terms of spatial precision, temporal consistency, and decision-support utility. The proposed approach also offers an effective method to fill gaps in local model availability by learning from global models, ensuring continuity and operational resilience. By combining observation statistics, NWP forecasts, and ML interpretation, this methodology supports scalable, automated pre-flight planning under varying weather scenarios.

Paper Citation

in Harvard Style

Sládek D. (2025). Translating NWP Outputs into UAV-Specific Predictions Using Machine Learning. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 184-191. DOI: 10.5220/0013674100003982

in Bibtex Style

@conference{icinco25,
author={David Sládek},
title={Translating NWP Outputs into UAV-Specific Predictions Using Machine Learning},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={184-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013674100003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Translating NWP Outputs into UAV-Specific Predictions Using Machine Learning
SN - 978-989-758-770-2
AU - Sládek D.
PY - 2025
SP - 184
EP - 191
DO - 10.5220/0013674100003982
PB - SciTePress