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

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

doi: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

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Nov 2016	9	23	2	34
Dec 2016	5	26	2	33
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Feb 2017	16	34	0	50
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May 2017	36	57	7	100
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Jul 2017	29	38	6	73
Aug 2017	17	48	6	71
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Oct 2017	17	57	2	76
Nov 2017	21	67	5	93
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Apr 2018	18	96	5	119
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Mar 2020	11	17	2	30
Apr 2020	22	26	3	51
May 2020	13	24	6	43
Jun 2020	33	31	6	70
Jul 2020	26	32	8	66
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Sep 2022	25	17	0	42
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Nov 2022	20	28	0	48
Dec 2022	15	14	1	30
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Apr 2023	8	17	0	25
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Sep 2023	41	12	3	56
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Nov 2023	26	8	1	35
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Apr 2024	88	21	2	111
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Dec 2024	17	20	5	42
Jan 2025	35	16	0	51
Feb 2025	28	10	0	38
Mar 2025	23	19	2	44
Apr 2025	27	21	0	48
May 2025	26	21	1	48
Jun 2025	52	15	2	69
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Aug 2025	20	26	0	46
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Nov 2025	29	30	5	64
Dec 2025	39	35	5	79
Jan 2026	61	38	9	108
Feb 2026	17	14	0	31

Cumulative views and downloads (calculated since 07 Oct 2015)

Month	HTML	PDF	XML	Total
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Nov 2015	32	31	10	73
Dec 2015	11	20	4	35
Jan 2016	47	25	5	77
Feb 2016	37	16	2	55
Mar 2016	105	56	2	163
Apr 2016	25	26	3	54
May 2016	23	17	0	40
Jun 2016	13	8	3	24
Jul 2016	17	17	3	37
Aug 2016	12	7	3	22
Sep 2016	14	15	2	31
Oct 2016	13	21	3	37
Nov 2016	9	23	2	34
Dec 2016	5	26	2	33
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Feb 2017	16	34	0	50
Mar 2017	27	57	4	88
Apr 2017	30	54	5	89
May 2017	36	57	7	100
Jun 2017	34	40	3	77
Jul 2017	29	38	6	73
Aug 2017	17	48	6	71
Sep 2017	18	58	3	79
Oct 2017	17	57	2	76
Nov 2017	21	67	5	93
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Oct 2018	32	99	9	140
Nov 2018	24	81	4	109
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Jan 2019	25	71	4	100
Feb 2019	24	54	4	82
Mar 2019	12	71	7	90
Apr 2019	13	73	4	90
May 2019	24	96	11	131
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Jun 2023	41	6	1	48
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Oct 2023	60	13	3	76
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Dec 2023	49	12	3	64
Jan 2024	51	14	0	65
Feb 2024	40	15	0	55
Mar 2024	91	17	3	111
Apr 2024	88	21	2	111
May 2024	39	29	1	69
Jun 2024	53	22	1	76
Jul 2024	49	15	2	66
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Oct 2024	20	12	0	32
Nov 2024	14	8	3	25
Dec 2024	17	20	5	42
Jan 2025	35	16	0	51
Feb 2025	28	10	0	38
Mar 2025	23	19	2	44
Apr 2025	27	21	0	48
May 2025	26	21	1	48
Jun 2025	52	15	2	69
Jul 2025	28	32	1	61
Aug 2025	20	26	0	46
Sep 2025	48	20	2	70
Oct 2025	34	31	1	66
Nov 2025	29	30	5	64
Dec 2025	39	35	5	79
Jan 2026	61	38	9	108
Feb 2026	17	14	0	31

Latest update: 09 Feb 2026

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.