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Wind velocity field estimation from aircraft derived data using Gaussian process regression.

Marius Marinescu1, Alberto Olivares1, Ernesto Staffetti1

  • 1School of Telecommunication Engineering, Rey Juan Carlos University, Fuenlabrada, Madrid, Spain.

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Summary
This summary is machine-generated.

This study estimates wind velocity fields using aircraft surveillance data and Gaussian process regression. The method accurately reconstructs and predicts wind, crucial for air traffic management.

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Area of Science:

  • Atmospheric Science
  • Aerospace Engineering
  • Data Science

Background:

  • Accurate wind velocity field knowledge is essential for advanced air traffic management and aircraft performance.
  • Current methods for wind estimation face limitations in spatio-temporal resolution and real-time application.

Purpose of the Study:

  • To develop and validate a method for spatio-temporal wind velocity field estimation using aircraft-derived data.
  • To reconstruct past/present wind fields and perform short-term wind predictions.

Main Methods:

  • Utilized Gaussian process regression (GPR), a flexible and statistically consistent estimation technique.
  • Employed data from aircraft surveillance systems (Mode-S and ADS-B) for indirect wind data acquisition.
  • Spatial modeling performed on raw data without grid reliance, enabling estimation at any spatio-temporal location.

Main Results:

  • Gaussian process regression demonstrated reliable estimation of wind velocity fields from aircraft-derived data.
  • The method showed statistical consistency, converging to ground truth with increased data availability.
  • Fast training phase (under 5 minutes) allows for online tracking and data assimilation.

Conclusions:

  • Gaussian process regression is a viable and effective tool for estimating spatio-temporal wind velocity fields.
  • The approach offers high accuracy, confidence intervals, and adaptability for real-time air traffic management applications.
  • Validation against ERA5 reanalysis data confirms the reliability of aircraft-derived wind field estimations.