Estimation and prediction of weather variables from surveillance data using spatio-temporal Kriging
Document typeConference report
Rights accessOpen Access
State-of-the-art weather data obtained from numerical weather predictions are unlikely to satisfy the requirements of the future air traffic management system. A potential approach to improve the resolution and accuracy of the weather predictions could consist on using airborne aircraft as meteorological sensors, which would provide up-to-date weather observations to the sur- rounding aircraft and ground systems. This paper proposes to use Kriging, a geostatistical interpolation technique, to create short- term weather predictions from scattered weather observations derived from surveillance data. Results show that this method can accurately capture the spatio-temporal distribution of the temperature and wind fields, allowing to obtain high-quality local, short-term weather predictions and providing at the same time a measure of the uncertainty associated with the prediction.
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CitationDalmau, R., Perez-Batlle, M., Prats, X. Estimation and prediction of weather variables from surveillance data using spatio-temporal Kriging. A: Digital Avionics Systems Conference. "DASC 2017: 36th Digital Avionics Systems Conference: St. Petersburg, Florida, USA: September 17-21, 2017: proceedings papers". St. Petersburg, Florida: 2017, p. 1-8.