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 |
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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 |
| _version_ | 1818921534028251136 |
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| author | I. Kaur P. Eriksson S. Pfreundschuh D. I. Duncan |
| author_facet | I. Kaur P. Eriksson S. Pfreundschuh D. I. Duncan |
| author_sort | I. Kaur |
| collection | DOAJ |
| description | <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 bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels – the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %–6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements.</p> |
| first_indexed | 2024-12-20T01:39:10Z |
| format | Article |
| id | doaj.art-27c7246ebbad42649b86e7fd39e200e1 |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-20T01:39:10Z |
| publishDate | 2021-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-27c7246ebbad42649b86e7fd39e200e12022-12-21T19:57:56ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482021-04-01142957297910.5194/amt-14-2957-2021Can machine learning correct microwave humidity radiances for the influence of clouds?I. Kaur0P. Eriksson1S. Pfreundschuh2D. I. Duncan3Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenEuropean Centre for Medium-Range Weather Forecasts, Reading, UK<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 bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels – the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %–6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements.</p>https://amt.copernicus.org/articles/14/2957/2021/amt-14-2957-2021.pdf |
| spellingShingle | I. Kaur P. Eriksson S. Pfreundschuh D. I. Duncan Can machine learning correct microwave humidity radiances for the influence of clouds? Atmospheric Measurement Techniques |
| title | Can machine learning correct microwave humidity radiances for the influence of clouds? |
| title_full | Can machine learning correct microwave humidity radiances for the influence of clouds? |
| title_fullStr | Can machine learning correct microwave humidity radiances for the influence of clouds? |
| title_full_unstemmed | Can machine learning correct microwave humidity radiances for the influence of clouds? |
| title_short | Can machine learning correct microwave humidity radiances for the influence of clouds? |
| title_sort | can machine learning correct microwave humidity radiances for the influence of clouds |
| url | https://amt.copernicus.org/articles/14/2957/2021/amt-14-2957-2021.pdf |
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