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

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...

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, John Kalisch, Elke Lorenz, and Detlev Heinemann

Download

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

- Printer-friendly version

- 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.