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Open AccessProceedings

Radiometric Scale Transfer Using Bayesian Model Selection

Longmont, Colorado, Max-Planck-Institut für Plasmaphysik, 85748 Garching, Germany
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Presented at the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Garching, Germany, 30 June–5 July 2019.
Proceedings 2019, 33(1), 32; https://doi.org/10.3390/proceedings2019033032
Published: 3 February 2020
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.
Keywords: broadband; irradiance; reference; solar radiation; climate modelling; pyrheliometer; Bayesian model comparison; evidence broadband; irradiance; reference; solar radiation; climate modelling; pyrheliometer; Bayesian model comparison; evidence
MDPI and ACS Style

Nelson, D.W.; Toussaint, U. Radiometric Scale Transfer Using Bayesian Model Selection. Proceedings 2019, 33, 32.

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