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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 fy12 13 gimpap project proposal title page date 7 august 2012


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 project summary
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 motivation justification
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 methodology
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)


5 expected outcomes
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 first year preliminary results1
6. First Year - Preliminary Results

CSU Station Observations

GOES-11: Predicted Cloud Movement

2100 UTC

CSU Station


Irradiance [W/m2]





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.


6 first year preliminary results2
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.

6 first year preliminary results3
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.)


6 first year preliminary results4
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.

7 possible path to operations
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
8 fy13 milestones
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
10 spending plan fy13
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