paper
2021
Observations & remote sensing
Post-processing & calibration Application study
Classical ML (trees, GP, linear, kernels)University of Washington Journal
2021-08-12
026
Aug 2021
Callie McNicholas, Clifford F. Mass
Weather and Forecasting
University of Washington
Machine learning bias correction of billions of smartphone pressure observations cuts errors 43-57% and yields analyses 21-31% better than MADIS networks.
Bias Correction, Anonymization, and Analysis of Smartphone Pressure Observations Using Machine Learning and Multiresolution Kriging
In one sentence. Machine learning bias correction of billions of smartphone pressure observations cuts errors 43-57% and yields analyses 21-31% better than MADIS networks.
Labels
- Topic
- Observations & remote sensing, Post-processing & calibration
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Station / in-situ
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1175/waf-d-20-0222.1 OpenAlex:W3196148980
- Added
- 2026-10-22
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