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February 6, 20260 citationsOpen Access

Enhancing Spatio-Temporal Models for Wind Speed Prediction with Crowdsourced Data

Enhancing the accuracy of spatio-temporal models for wind speed prediction by incorporating bias-corrected crowdsourced data

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Authors

EOEamonn OrganUniversity of LimerickMUMaeve UptonDADenis AllardInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

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Overview

Demonstrates improved prediction accuracy in wind speed by incorporating bias-corrected crowdsourced data, suggesting a viable enhancement for real-time forecasting.

Key Points

  • The aim is to enhance wind speed prediction accuracy by integrating bias-corrected data from personal weather stations into spatiotemporal models.
  • Developed a framework for incorporating data from personal weather stations (PWS)
  • Conducted bias correction on PWS data using reanalysis data
  • Implemented a Bayesian hierarchical spatiotemporal model to account for measurement errors
  • Validated predictions against official meteorological station data
  • Incorporating bias-corrected PWS data resulted in an average 5% reduction in prediction error
  • The model's accuracy was comparable to popular reanalysis products
  • The approach allows real-time data availability and improved uncertainty quantification
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Cite This Study

Organ et al. (2026) studied this question.

synapsesocial.com/papers/698585db8f7c464f230099f0https://doi.org/10.34961/19220
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