Radiometric Scale Transfer Using Bayesian Model Selection
The key input quantity to climate modelling and weather forecasts is the solar beam irradiance, i.e., the primary amount of energy provided by the sun. Despite its importance the absolute accuracy of the measurements are limited—which not only affects the modelling but also ground truth te...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3900/33/1/32 |
| Summary: | The key input quantity to climate modelling and weather forecasts is the solar beam irradiance, i.e., the primary amount of energy provided by the sun. Despite its importance the absolute accuracy of the measurements are limited—which not only affects the modelling but also ground truth tests of satellite observations. Here we focus on the problem of improving instrument calibration based on dedicated measurements. A Bayesian approach reveals that the standard approach results in inferior results. An alternative approach method based on monomial based selection of regression functions, combined with model selection is shown to yield superior estimations for a wide range of conditions. The approach is illustrated on selected data and possible further enhancements are outlined. |
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| ISSN: | 2504-3900 |