Latest revision as of 22:52, 1 February 2021

Abstract

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Best paper award in Weather session at the 36th DASC - Digital Avionics Systems Conference

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.

Peer Reviewed

Award-winning

Document type: Conference object

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Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1109/dasc.2017.8102132 under the license https://creativecommons.org/licenses/by-nc-nd

• http://hdl.handle.net/2117/112695

• https://upcommons.upc.edu/bitstream/2117/112695/1/dalmau_17_kriging.pdf under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

• http://xplorestaging.ieee.org/ielx7/8093739/8101896/08102132.pdf?arnumber=8102132,

http://dx.doi.org/10.1109/dasc.2017.8102132 under the license cc-by-nc-nd

• https://upcommons.upc.edu/handle/2117/112695,

https://academic.microsoft.com/#/detail/2978281297