My presentation for the PICO session on Climate Services at the EGU 2016 in Vienna

1. Measuring the skill benefits of
climate forecasts in predicting
PV power production
Matteo De Felice, Andrea Alessandri and Maurizio
Pollino

2. Solar Power and Climate
• Today we have plenty of weather/climate datasets of
solar radiation (satellites, reanalyses, NWP, climate
forecasts)
• Here we focus on seasonal predictability of solar
radiation
• The aim of this paper is an assessment of the skills of
seasonal forecasts to predict solar radiation over Europe
• May the information provided by climate forecasts help
the solar power sector to improve their decision-making?
EGU2016-18336 - Climate Services - Underpinning Science Session

3. Skill of seasonal forecasts
ECMWF System4 vs Heliosat (SARAH) - Summer, 1983-2013
EGU2016-18336 - Climate Services - Underpinning Science Session

4. Is this enough?

5. More information sources
• Skill of seasonal forecasts in predicting PV power
output
• PV Solar Installed capacity
• Solar radiation inter-annual variability
• Using land-cover to mask areas not-suitable for PV
EGU2016-18336 - Climate Services - Underpinning Science Session

6. Measuring the benefits
EGU2016-18336 - Climate Services - Underpinning Science Session

7. And now the long story…

8. What is a good forecast?
Allan Murphy in 1993 categorised the “goodness”
of a forecast in…
1 Consistency
Correspondence between forecasts and
judgements
2 Quality
Correspondence between forecasts and
observations
3 Value Incremental benefits of forecasts to users
EGU2016-18336 - Climate Services - Underpinning Science Session

9. “Quality” means “value”?
• A. Murphy underlined that forecasts do not have an
intrinsic value but instead they gain it when they
have a positive influence on on the decisions
made by users of the forecasts.
• Value of a forecast is strictly linked with its quality
but their relationship is rarely linear
EGU2016-18336 - Climate Services - Underpinning Science Session

10. Information layers
Here we assume that the benefit of a climate
forecast of solar power is affected by the following
three factors:
1. Statistical Skill (e.g. BSS): the more the better
2. Installed Capacity: good forecast will have a greater
impact in areas with high installed capacity
3. Inter-annual variability: a forecast can help to cope with the
high variability of solar radiation
EGU2016-18336 - Climate Services - Underpinning Science Session

11. (1/3) Statistical skill
ECMWF System4 vs
Heliosat (SARAH)
1983-2013
Lower tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session

12. (1/3) Statistical skill
ECMWF System4 vs
Heliosat (SARAH)
1983-2013
Upper tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session

13. (1/3) Statistical skill
Modelled PV production
of ECMWF System4 vs
Heliosat (SARAH) +
EOBS
1983-2013
Lower tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session

14. (1/3) Statistical skill
Modelled PV production
of ECMWF System4 vs
Heliosat (SARAH) +
EOBS
1983-2013
Upper tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session

15. PV Suitability
• Map of suitability of PV
derived by the work by
Hansen & Thorn (PV
potential and potential PV
rent in European regions)
• Based on the Corine Land
Cover 2006 (CLC2006)
• Used to mask out grid
points from analysis
EGU2016-18336 - Climate Services - Underpinning Science Session

16. (2/3) Installed Capacity
• PV cumulative installed capacity in 2014 (Data
extrapolated from the Solar-Power Europe Global
Market Outlook)
EGU2016-18336 - Climate Services - Underpinning Science Session

17. (3/3) Inter-annual variability
Relative Std. Dev.
Heliosat (SARAH)
1983-2013
Lower tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session

18. (3/3) Inter-annual variability
Relative Std. Dev.
Heliosat (SARAH)
1983-2013
Lower tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session

19. Putting things together
A matrix of this type should be designed in
collaboration with the end-user
EGU2016-18336 - Climate Services - Underpinning Science Session

20. Measuring the benefits
EGU2016-18336 - Climate Services - Underpinning Science Session

21. Comments
• We should focus not only on skill but on all the
factors influencing the decisions
• When providing a service focus on value and not
(only) on quality
EGU2016-18336 - Climate Services - Underpinning Science Session