Short‐Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

Coburn, Jacob; Arnheim, Julian; Pryor, Sara C.

doi:10.1029/2022EA002486

Title: Short‐Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

Journal Article · Fri Dec 23 00:00:00 EST 2022 · Earth and Space Science

DOI:https://doi.org/10.1029/2022EA002486· OSTI ID:1906763

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Department of Earth and Atmospheric Sciences Cornell University Ithaca NY USA

Abstract Short‐term forecasting of wind gusts, particularly those of higher intensity, is of great societal importance but is challenging due to the presence of multiple gust generation mechanisms. Wind gust observations from eight high‐passenger‐volume airports across the continental United States (CONUS) are summarized and used to develop predictive models of wind gust occurrence and magnitude. These short‐term (same hour) forecast models are built using multiple logistic and linear regression, as well as artificial neural networks (ANNs) of varying complexity. A suite of 19 upper‐air predictors drawn from the ERA5 reanalysis and an autoregressive (AR) term are used. Stepwise procedures instruct predictor selection, and resampling is used to quantify model stability. All models are developed separately for the warm (April–September) and cold (October–March) seasons. Results show that ANNs of 3–5 hidden layers (HLs) generally exhibit higher hit rates than logistic regression models and also improve skill with respect to wind gust magnitudes. However, deeper networks with more HLs increase false alarm rates in occurrence models and mean absolute error in magnitude models due to model overfitting. For model skill, inclusion of the AR term is critical while the majority of the remaining skill derives from wind speeds and lapse rates. A predictive ceiling is also clearly demonstrated, particularly for the strong and damaging gust magnitudes, which appears to be partially due to ERA5 predictor characteristics and the presence of mixed wind climates.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: DE‐SC0016605

OSTI ID:: 1906763

Alternate ID(s):: OSTI ID: 1995929

Journal Information:: Earth and Space Science, Journal Name: Earth and Space Science Vol. 9 Journal Issue: 12; ISSN 2333-5084

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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