Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

Jacob J. Coburn, S. C. Pryor

Weather and Forecasting 2022 article cited by 16

importance 6/10 ai assisted Meteorological Phenomena and Simulations aviation forecasting stage: research

Problem

Short-term forecasting of wind gust occurrence and magnitude, relevant to high passenger volume US airports, is extremely challenging.

Why it matters

Abstract states intense gusts are societally relevant, including for three high-volume US airports.

What the AI did

Artificial neural networks using ERA5 reanalysis predictors plus an autoregressive term forecast gust occurrence and magnitude, assessed over 1000 random train/test splits.

Results

Five-layer ANNs showed consistently higher fidelity than regression for gust occurrence; fidelity depended on autoregressive term; no numeric scores in abstract.

Impact

Suggests ANNs can improve short-term gust occurrence forecasts at major airports.

Record

Venue	Weather and Forecasting
Published	2022
Work type	article
Authors	Jacob J. Coburn, S. C. Pryor
Citations	16 (OpenAlex, at import time)
References	82
Model or method	Artificial neural networks (ANN), five-layer
Organization	not stated in source
Primary source	https://doi.org/10.1175/waf-d-21-0118.1
Open access	closed
Keywords	wind gusts, forecasting, neural networks, ERA5, aviation weather

Importance rating

6 / 10

significant — thousands to millions affected

Abstract states intense gusts are societally relevant and models target three high passenger volume US airports; improved gust occurrence/magnitude forecasts aid aviation safety at major hubs.

Basis: stated. The source itself states the scale. Stated scale: three high passenger volume US airports. Method and limits.

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