Can machine learning correct microwave humidity radiances for the influence of clouds?
<p>A methodology based on quantile regression neural networks (QRNNs) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise-free clear-sky (NFCS) radiances, providing nearly bi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-04-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/14/2957/2021/amt-14-2957-2021.pdf |