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: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-02-01
|
| Series: | Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3900/33/1/32 |
| id |
doaj-6fb02ef8f2394d3aa4aa629a859398ec |
|---|---|
| record_format |
Article |
| spelling |
doaj-6fb02ef8f2394d3aa4aa629a859398ec2020-11-25T02:33:56ZengMDPI AGProceedings2504-39002020-02-013313210.3390/proceedings2019033032proceedings2019033032Radiometric Scale Transfer Using Bayesian Model SelectionDonald W. Nelson0Udo von Toussaint1Longmont, Colorado, Max-Planck-Institut für Plasmaphysik, 85748 Garching, GermanyLongmont, Colorado, Max-Planck-Institut für Plasmaphysik, 85748 Garching, GermanyThe 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.https://www.mdpi.com/2504-3900/33/1/32broadbandirradiancereferencesolar radiationclimate modellingpyrheliometerbayesian model comparisonevidence |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Donald W. Nelson Udo von Toussaint |
| spellingShingle |
Donald W. Nelson Udo von Toussaint Radiometric Scale Transfer Using Bayesian Model Selection Proceedings broadband irradiance reference solar radiation climate modelling pyrheliometer bayesian model comparison evidence |
| author_facet |
Donald W. Nelson Udo von Toussaint |
| author_sort |
Donald W. Nelson |
| title |
Radiometric Scale Transfer Using Bayesian Model Selection |
| title_short |
Radiometric Scale Transfer Using Bayesian Model Selection |
| title_full |
Radiometric Scale Transfer Using Bayesian Model Selection |
| title_fullStr |
Radiometric Scale Transfer Using Bayesian Model Selection |
| title_full_unstemmed |
Radiometric Scale Transfer Using Bayesian Model Selection |
| title_sort |
radiometric scale transfer using bayesian model selection |
| publisher |
MDPI AG |
| series |
Proceedings |
| issn |
2504-3900 |
| publishDate |
2020-02-01 |
| description |
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. |
| topic |
broadband irradiance reference solar radiation climate modelling pyrheliometer bayesian model comparison evidence |
| url |
https://www.mdpi.com/2504-3900/33/1/32 |
| work_keys_str_mv |
AT donaldwnelson radiometricscaletransferusingbayesianmodelselection AT udovontoussaint radiometricscaletransferusingbayesianmodelselection |
| _version_ |
1724811391915786240 |