Using machine learning to model uncertainty for water vapor atmospheric motion vectors
<p>Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking clouds or water vapor across spatial–temporal fields. Thorough error characterization of wind-tracking algorithms is critical in properly assimilating AMVs into weather forecast models and climate reanalysis data...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-03-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/14/1941/2021/amt-14-1941-2021.pdf |