Solar Intelligence Lessons learnt in Australia

1. Solar IntelligenceLessons learnt in Australia Bruce W ForganObservation & Infrastructure BranchObservations and Infrastructure DivisionAustralian Bureau of Meteorology (ABoM)

2. Some Australia Statistics • Climate: Desert & semi-arid - low rainfall • Significant solar resources • Populations: Aust 23 M • Population Centres: >80% in 12 cities • Energy source: fossil fuels • National renewable solar plans: Solar Flagships (Aust)

3. BoM Solar Measurements • Primary Focus:Data for Environmental Intelligence ~13 year cycle of boom and bust for solar resourcing monitoring since 1960s

4. Surface Network – pre 1993 • 1968 – 1992 ‘Weather’ Solar Network • Global exposure (30 min) • Global & Diffuse (30 min) • Tuned to 1963 Model (Archer, 1963) • All major climate events tuned out

5. Post 1992:Bureau approach to solar monitoring Multi-Tiered approach initiated in 1992 • 1st Tier • 3 state-of-art BSRN stations including in situ calibration • 2nd Tier • Basic network using identical instrumentation and protocols as tier 1except reduced in situ calibration and audit frequency • Number fluctuates due to measurement priorities(13 -> 5 -> 13 -> ?) • 3rd Tier • Satellite products – daily, hourly, (10 min) global & direct exposures

6. Fit for Purpose Basic - U95 Global * Can be dominated by either under-sampling or missing data+ To surface network kWhm-2

7. Surface network – Basic Station

8. Standard Station Measurements • Solar (280-4000 nm) • Direct (total) pyrheliometer (body temperature) • Direct Spectral transmission (412, 500, 610, 778, (368, 812,868), 10 nm FWHM) • Diffuse pyranometer (body temp) • (Global pyranometer , body temp) • Terrestrial (>4000 nm) – shaded pyrgeometer • Data acquisition • 1 Hz archived • Minute signal statistics (mean, max, min, std. devn, U95.) distributed • Sunshine seconds (120 Wm-2, 95 Wm--2, 144 Wm--2………) • ‘quick look’to intranet every 10 minutes • Daily for processing post midnight • Cleaning (and attendance) monitor • Local statistics for observer • Ancillary • All weather elements monitored at the Bureau staffed station(T,P,RH,Ceilometer, ‘daily’ cleaning…….)

9. Surface Network: QA/QC

10. Surface Network: Irradiance Outputs

11. Aerosol Optical Depth Climate

12. Solar Flagships 2012-2014 Up to 4 large scale solar power stations $Aust 1.5 billion Solar Mapping (+ ancillary data) component Geoscience Australia, National Geographic Information Group $5 million BoM solar resource component (~$Aust 3 m of $5m)

13. Partnership with Geosciences Australia: Pre-competitivemapping portal Regions of High Prospectivity Solar Data National Datasets vegetation energy networks – electricity gas water sources infrastructure land tenure digital elevation Spatial Analysis resource mapping layers Solar radiation, slope, aspect, proximity to grid………………

14. Project deliverables Gridded datasets (0.05° grid over Australian land) Time series of daily & monthly global exposure (1990-) Monthly climatologies of hourly global & direct exposure Uncertainty characterisation of each dataset Detailed online supporting information & metadata

15. Surface network & Solar Flagships expansion

16. How Many Stations Do We Need?

17. Benefits to Bureau Better quality, adjustment and uncertainty assessment of existing satellite data products Temporary network expansion provides data for analysing optimal network for satellite adjustment and assessment Some improvement in Climate Centre data handling- free 1 minute data access from web- total data set (station years 265 + satellite) for cost of media (~$200 for 1 Tb) Preparation of user support information to reduce Bureau staff interaction and better data use

18. User Support Services • Enhance metadata and measurement intelligence • Basic terminology • Radiation theory and the relationship with other weather • Discussion about instrumentation and processes • Discussion of uncertainty • International standards and benchmarking • Practical data issues • Delivering one minute solar statistics • http://www.bom.gov.au/climate/data-services/solar-information.shtml

19. Satellite-surface comparisons • Before & after June 2012 revision for Solar Flagships project

20. What the Solar Flagshipsproject does not provide Expanded network operation beyond Dec 2013 Viable climatology and long-term representativeness (e.g. typical year) Ability to adjust satellite data when new satellites come on stream in 2015

21. Lessons Learnt • Typical 13 year boom-bustcycle for solar monitoring support • Spending more on capital equipment and QA reduces the total end-to-end cost for fit-for-purpose data • Solar power resource, water budgeting & climate change dominate the requirement for ‘high quality’ low U95 over long time scales – so work together • Solar resource monitoring requirements pushsolar monitoring metrology to the practice limit.

22. Lessons Learnt • Not having target uncertainties reduces network governance and effectiveness • Redundancy & continuity are just as important as instrument calibration • A good working definition:High quality measurements are those that require minimal cost to demonstrate they are fit for purpose

23. Future Uses with NWP ACCESS-A Satellite MALAPS Second data NWP forecasts research