McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

By Mireille Lefevre, A. Oumbe, Philippe Blanc, Bella Espinar, B. Gschwind, Z. Qu, Lucien Wald, M. Schroedter-Homscheidt, C. Hoyer-Klick, Antti Arola and 3 more. 2013

393 citations recorded by the source corpus.

Abstract. A new fast clear-sky model called McClear was developed to estimate the downwelling shortwave direct and global irradiances received at ground level under clear skies. It is a fully physical model replacing empirical relations or simpler models used before. It exploits the recent results on aerosol properties, and total column content in water vapour and ozone produced by the MACC project (Monitoring Atmosphere Composition and Climate). It accurately reproduces the irradiance computed by the libRadtran reference radiative transfer model with a computational speed approximately 105 times greater by adopting the abaci, or look-up table, approach combined with interpolation functions. It is therefore suited for geostationary satellite retrievals or numerical weather prediction schemes with many pixels or grid points, respectively. McClear irradiances were compared to 1 min measurements made in clear-sky conditions at several stations within the Baseline Surface Radiation Network in various climates. The bias for global irradiance comprises between −6 and 25 W m−2. The RMSE ranges from 20 W m−2 (3% of the mean observed irradiance) to 36 W m−2 (5%) and the correlation coefficient ranges between 0.95 and 0.99. The bias for the direct irradiance comprises between −48 and +33 W m−2. The root mean square error (RMSE) ranges from 33 W m−2 (5%) to 64 W m−2 (10%). The correlation coefficient ranges between 0.84 and 0.98. This work demonstrates the quality of the McClear model combined with MACC products, and indirectly the quality of the aerosol properties modelled by the MACC reanalysis.

Subfields: Artificial Intelligence, Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b078a770809862cd9cc425e8245b9532d6e5ae0a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mireille Lefevre
2. Philippe Blanc
3. Bella Espinar
4. Lucien Wald
5. Pierre Ineichen

In Atmospheric Science

1. Bella Espinar
2. Lucien Wald

In Global and Planetary Change

1. Philippe Blanc
2. Bella Espinar
3. Lucien Wald

Citations in the corpus

• Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat-4 method (2017, methodology): “One may observe for the sites of Palaiseau and Payerne also studied in Lefèvre et al. (2013), a tendency of the bias in cloud-free conditions to decrease as θS increases for θS > 30 °.”