paper
2022
Applications & impacts
Post-processing & calibration Application study
Classical ML (trees, GP, linear, kernels)6 h–3 daysNASACalifornia Institute of Technology (Caltech)University of California, Los Angeles (UCLA) Journal
2022-11-01
043
Nov 2022
Longtao Wu, David D. Morabito, Joaquim Teixeira et al.
Radio Science
NASA, California Institute of Technology (Caltech), University of California, Los Angeles (UCLA)
Random forest trained on GFS forecasts and radiometer data predicts zenith atmospheric noise temperature at NASA Deep Space Network sites, enabling up to 2.43 dB downlink gain.
Prediction of Atmospheric Noise Temperature at the Deep Space Network With Machine Learning
In one sentence. Random forest trained on GFS forecasts and radiometer data predicts zenith atmospheric noise temperature at NASA Deep Space Network sites, enabling up to 2.43 dB downlink gain.
Labels
- Topic
- Applications & impacts, Post-processing & calibration
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1029/2022rs007483 OpenAlex:W4307836689
- Added
- 2026-10-22
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