paper
2022
Applications & impacts
Observations & remote sensing Application study
Classical ML (trees, GP, linear, kernels) Journal
2022-04-08
008
Apr 2022
Arif Armağan Gözütok, Ümit Cezmi YILMAZ, Selman Demirel
International Journal of Satellite Communications and Networking
Feed-forward neural network driven by WRF rainfall output predicts rain attenuation fading on a geostationary satellite downlink during a dust-storm event.
Preliminary results on estimation of signal fading on telecommunication satellite telemetry signals with hybrid numerical weather prediction and artificial neural network approach under presence of aerosol effect
In one sentence. Feed-forward neural network driven by WRF rainfall output predicts rain attenuation fading on a geostationary satellite downlink during a dust-storm event.
Labels
- Topic
- Applications & impacts, Observations & remote sensing
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Satellite, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1002/sat.1442 OpenAlex:W4225985935
- Added
- 2026-10-22
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