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Estimation and prediction of weather variables from surveillance data using spatio-temporal Kriging

Author
Dalmau, R.; Perez-Batlle, M.; Prats, X.
Type of activity
Presentation of work at congresses
Name of edition
36th Digital Avionics Systems Conference
Date of publication
2017
Presentation's date
2017-09-21
Book of congress proceedings
DASC 2017: 36th Digital Avionics Systems Conference: St. Petersburg, Florida, USA: September 17-21, 2017: proceedings papers
First page
1
Last page
8
DOI
https://doi.org/10.1109/DASC.2017.8102132 Open in new window
Rewarded activity
Yes
Repository
http://hdl.handle.net/2117/112695 Open in new window
URL
http://ieeexplore.ieee.org/document/8102132/ Open in new window
Abstract
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 s...
Citation
Dalmau, 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.
Group of research
ICARUS - Intelligent Communications and Avionics for Robust Unmanned Aerial Systems

Participants

Attachments