Name
Abstract | ||
|---|---|---|
Up-to-date meteorological information about upper air conditions is crucial for accurate weather modeling and forecasting. Existing techniques to sense meteorological parameters in the atmosphere are costly and provide only limited temporal and spatial sensing resolutions. In this paper, we propose crowdsourcing air traffic control data as a new cost-efficient method to achieve a high temporal and spatial resolution, and large coverage. Our solution leverages Secondary Surveillance Radar Mode S and ADS-B transponder signals that are continuously transmitted by aircraft for air traffic control purposes. It builds on signals captured by the OpenSky Network, a global-scale sensor network crowdsourcing 15+ billions of transponder messages per day from aircraft up to an altitude of 13 km. Based on the decoded data, we infer meteorological conditions such as air temperature, wind speed, wind direction and atmospheric pressure. Our evaluation demonstrates that our approach is effective at estimating these parameters with high resolutions along the tracks of more than 50 percent of all aircraft monitored by the OpenSky Network. Our method delivers estimations for temperature with 0.11°C, wind speed with 0.09 m/s, wind direction with 1.00°, and air pressure with 0.10 hPa average deviation, making those measurements suitable for the assimilation in numerical weather models.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IPSN.2018.00010 | IPSN |
Keywords | Field | DocType |
crowdsourcing, atmosphere, meteorology, air traffic control, sensor network, temperature, wind, pressure | Radar,Meteorology,Secondary surveillance radar,Wind speed,Computer science,Air traffic control,Real-time computing,Atmospheric model,Transponder (aeronautics),Wind direction,Numerical weather prediction | Conference |
ISBN | Citations | PageRank |
978-1-5386-5298-5 | 1 | 0.41 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roman Trüb | 1 | 3 | 0.77 |
| Daniel Moser | 2 | 19 | 3.45 |
| Matthias Schäfer | 3 | 114 | 16.20 |
| Rui Pinheiro | 4 | 1 | 0.41 |
| Vincent Lenders | 5 | 737 | 72.53 |