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1 . FY12-13 GIMPAP Project Proposal Title Page date: 7 August 2012

14. 1 . FY12-13 GIMPAP Project Proposal Title Page date: 7 August 2012. Title : Advancing GOES Cloud and Surface Irradiance Products for Applications to Short Term Solar Energy Forecasting Status : Renewal Duration : Total of 2 years Project Leads:

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1 . FY12-13 GIMPAP Project Proposal Title Page date: 7 August 2012

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  1. 14 1. FY12-13 GIMPAP Project Proposal Title Pagedate: 7 August 2012 Title: Advancing GOES Cloud and Surface Irradiance Products for Applications to Short Term Solar Energy Forecasting Status: Renewal Duration: Total of 2 years Project Leads: Dr. Steven Miller, CSU/CIRA, (Steven.Miller@colostate.edu) Dr. Matt Rogers, CSU/CIRA, (Matthew.Rogers@colostate.edu) Other Participants: Dr. Andrew Heidinger, NOAA/NESDIS-STAR Dr. Istvan Laszlo, NOAA/NESDIS-STAR Dr. Stan Benjamin, NOAA/OAR-ESRL Dr. Manajit Sengupta, NREL Prof. Jan Kleissl, UC San Diego Mr. J. Adam Kankiewicz, WindLogics

  2. 2. Project Summary Fusion of GOES-derived cloud and surface irradiance products and NOAA NWP to advance short-term (0-3 hr) solar energy prediction: Physically-based cloud property retrievals, project cloud shadows into the future, account for Sun/satellite geometry Predict time series of surface irradiance at selected locations Validate against nationally-distributed NOAA SURFaceRADiation (SURFRAD) network Evaluate performance against NWP-based short-term forecasts (emphasizing the High Resolution Rapid Refresh [HRRR] model)

  3. 3. Motivation / Justification • NOAA’s Next Generation Strategic Plan (Dec 2010) • ‘…renewable energy industries need more accurate resource assessments with better observations tailored to sources such as solar irradiance … and cloud cover measurement.’ • NOAA/DoE MoU on Renewable Energy (Jan 2011) • Advancement of solar energy across a wide range of spatial and temporal scales requires improvements to relevant observations and prediction schemes. • Opportunity—Harvesting the Low-Hanging Fruit • NOAA satellite observing systems are currently under-utilized for solar energy forecasting, particularly in terms of GOES-derived cloud parameters. • Partnerships–Convergent Interests Promote Leveraging • CSU/CIRA partnering with leaders from NOAA’s satellite remote sensing (NESDIS) and NWP communities (ESRL), the DoE (NREL), otheracademia (UCSD; Kleissl Solar Resource Assessment and Forecasting Lab), and industry (WindLogics).

  4. 4. Methodology • Apply NOAA PATMOS-x software to real-time GOES ingest for definition of cloud mask and retrieved cloud properties • Advect clouds using GFS, HRRR, or AMV winds, compute locations of shadows at each time step • Call surface irradiance model (Pinker and Laszlo, 1992) using advected parameters to determine global horizontal irradiance (GHI; a combination of direct and diffuse) • Compare predicted irradiance time series against observations (e.g., SURFRAD network) 4

  5. 5. Expected Outcomes • New methodologies and tools for forecasting solar irradiance • A critical assessment of HRRR forecast performance in comparison to satellite methods • - Skill transitions from satellite-based to model-based cloud prediction • - Analysis as a function of season, region, and meteorological regimes • Fulfills key elements of NOAA’s commitment to the DoE MoU • - Targets improved cloud prediction at time scales where models have difficulty with deterministic placement of cloud features • - Makes better use of NOAA/NESDIS satellite technology and infrastructure • - Improves coupling of cloud retrieval and surface irradiance model components for accurate long-term records (resource assessment)

  6. 6. First Year - Preliminary Results

  7. 6. First Year - Preliminary Results CSU Station Observations GOES-11: Predicted Cloud Movement 2100 UTC CSU Station WY Irradiance [W/m2] CO Sunset Sunrise NM Observed ‘ramp’ in solar energy at CSU Station occurred during a break between two convective clouds… High Clouds Mid-Troposphere Low Clouds 100 1000 Cloud Top Pressure (mb)  Cloud dissociation arises from speed and directional shear in the 3D model wind field. 7

  8. 6. First Year - Preliminary Results 19 Jan 2012 1800-1900 UTC Short-term forecast of clouds crossing Sioux Falls (cloud optical depth shown at GOES pixel centers) SURFAD observations at Sioux Falls, showing details of the associated ramp around 18 UTC  Automating this processing will allow for statistical analysis of forecast system performance.

  9. 6. First Year - Preliminary Results Cloud Grouping Logic: • Considers all PATMOS-x identified cloudy pixels • Associates groups of adjacent pixels based on thresholds in optical properties, cloud top height, and spatial/spectral based cloud classifier • 5 tests employed (identical type, related type, top height, effective radius, optical depth) • Weighted sum of tests compared against a similarity threshold to determine whether the pixel belongs to a pre-existing group or becomes 1st member of new group • ‘Hooks’ in place for testing other group logic (ISCCP, K-Means, etc.) 9

  10. 6. First Year - Preliminary Results Group Color Code  The new grouping logic will be tested against pixel-level advection at SURFRAD stations to gauge impact.

  11. 7. Possible Path to Operations • Through NOAA/DoEMoU paradigmof expanded use: • New pathways for demonstrations and operational transition under MoU, including non-governmental and international partners • DoE/NREL has numerous well-established partnerships with industry, and can serve as a transition partner here • Through direct connections to operational users: • WindLogics offers opportunities to link research directly to utilities for demonstrating GOES tools in the context of actual power production / scheduling • UCSD & CSU are partnering with industry to host on-site photovoltaic arrays • Through collaboration with modeling community: • NOAA’s HRRR model represents the future of operational mesoscaleforecasting, and includes a focus on renewable energy parameters • We will couple directly to HRRR via satellite-based cloud trajectories

  12. 8. FY13 Milestones • FY13 Milestones • Continue validation experiments at SURFRAD sites, as well as in coordination with NREL, CSU, UCSD, and WindLogics partners • Produce automated software for computing surface irradiance component (total/direct/diffuse) time series at arbitrary locations • Publish findings and package/document all software and datasets • Specific Tasks • Group definition logic: PATMOS-x, ISCCP, K-Means • Group advection: steering level optimization • Radiative transfer: pixel vs. neighborhood • NWP: comparisons to HRRR to assess transition of skill

  13. 9. Funding Request (K)

  14. 10. Spending Plan FY13 • FY13 $64,000 Total Project Budget • Grants to CIRA $ 64,000 • ~39% FTE - 60K • Travel - $2K • Publication charge - $2K • Federal Publication Charges - $0 • Federal Equipment - $0 • Transfers to Federal Travel - $0 • Other agencies - $0 • Other - $0

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