Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Schmidt, Thomas; Kalisch, John; Lorenz, Elke; Heinemann, Detlev

doi:https://doi.org/10.5194/acp-16-3399-2016

Articles | Volume 16, issue 5

Atmos. Chem. Phys., 16, 3399–3412, 2016

https://doi.org/10.5194/acp-16-3399-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: HD(CP)² Observational Prototype Experiment (AMT/ACP...

Atmos. Chem. Phys., 16, 3399–3412, 2016

https://doi.org/10.5194/acp-16-3399-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 5

Research article 15 Mar 2016

Research article | 15 Mar 2016

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Thomas Schmidt et al.

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Final revised paper (published on 15 Mar 2016)
Preprint (discussion started on 07 Oct 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C11091: 'Review of manuscript “Evaluating the spatio-temporal performance of sky images based on solar irradiance analysis and forecasts”, by Schmidt et al.', Anonymous Referee #1, 04 Jan 2016
- AC C12331: 'Response to reviewer comment #1', Thomas Schmidt, 12 Feb 2016
RC C11727: 'Review of acp-2015-672', Anonymous Referee #2, 19 Jan 2016
- AC C12414: 'Response to reviewer comment #2', Thomas Schmidt, 15 Feb 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Thomas Schmidt on behalf of the Authors (17 Feb 2016) Author's response

ED: Publish as is (02 Mar 2016) by Herman Russchenberg

Short summary

We performed an irradiance forecast experiment based on analysis of hemispheric sky images and evaluated results on a large data set of 99 pyranometers distributed over 10 × 12 km. We developed a surface irradiance retrieval from cloud information derived from the images. Very high resolution forecasts were processed up to 25 min. A main finding is that forecast skill is enhanced in complex cloud conditions leading to high variability in surface irradiance.