1. NOAA Capabilities Relevant to DOE Solar Forecasting FOA (DE-FOA-0000649) Webinar May 30, 2012

2. WEBINAR PURPOSE & OVERVIEW 2 The purpose of this webinar is to explain capabilities of NOAA that are relevant to the Forecasting FOA, and may be available to award recipients under the Forecasting FOA. The exact scope of assistance that may be available from NOAA for successful applicants will depend on the details of the approach in the applications selected by DOE for negotiation of award, and will be finalized through negotiations among DOE, NOAA and the selected applicant(s). The content presented in this webinar is for information only, and is not intended to dictate to applicants any specific approaches for improving the accuracy of solar forecasting. Applicants to the FOA are not limited to the enhancement areas / opportunities for collaboration presented in this webinar.

3. DOE and NOAA Collaboration MOU on Weather-dependent and Oceanic Renewable Energy Resources signed January 2011. Covers weather-dependent and oceanic renewable energy and new innovations that may develop in the future. Improvements in relevant atmospheric and oceanic observations, modeling, numerical weather prediction, and climate research are required. The Wind Forecast Improvement Project (WFIP), is one activity that falls under this MOU. 3

4. Outline DOE-NOAA Collaboration (Melinda Marquis) High Resolution Rapid Refresh model (Stan Benjamin) Local Analysis and Prediction System (Yuanfu Xie) Enhancement of Satellite Products (Andy Heidinger) Model Verification (Joe Michalsky) NOAA Cloud and Radiation Test Bed Measurements (Joe) Provision of SURFRAD Data in Real-Time (Joe) Leveraging Existing, Unconventional Data (Melinda) Technology-Specific Forecasting Evaluation (Melinda) 4

5. NOAA/ESRL High Resolution Rapid Refresh (HRRR) model 3km model, updated hourly Domain – CONUS, radar reflectivity assimilation, cloud assimilation from GOES/METAR with background Initialization – from ESRL Rapid Refresh Forecast duration – 15h Key experimental model for DOE Wind Forecast Improvement Project (WFIP) 5

6. HRRR model WRF community model - ARW core including digital filter radar reflectivity assimilation. Thompson cloud microphysics parameterization – 6 hydrometeor types explicitly resolved, used in both HRRR and RAP. RUC-Smirnova land-surface model for soil/snow/vegetation processes. Option for an interactive-chemistry version of HRRR with explicit forecasting of ~17 aerosol types including chemical sources and sinks including real-time fire information from GOES satellites. 6

7. Data assimilation for HRRR RAP version of NOAA community Gridpoint Statistical Interpolation (GSI), a 3-d variational assimilation system with an option for a hybrid ensemble/variational analysis. Cloud/hydrometeor analysis within GSI to update 3-d hydrometeor fields using GOES satellite, METAR aviation surface ceilometer observations, and radar reflectivity. Effective assimilation of near-surface observations, including updating of soil moisture and temperature. Radar reflectivity assimilation via radar-digital-filter- initialization technique 7

8. More on HRRR Probabilistic forecasts from HRRR probabilistic products from time-lagged HRRR forecasts (different forecasts initialized at different times valid at the same time). Validation of HRRR forecasts by the HRRR/RAP team interactive system against surface radiometer data, GOES/GSIP solar products raobs, surface (w/ ceiling, visibility) profiler, radar, and precipitation observations include operational NCEP models (NAM, GFS, RAP as well as experimental ESRL models (RAP, HRRR, including parallel cycles) 8

9. Enhancement areas planned by NOAA/ESRL for HRRR Improved cloud/hydrometeor analysis using GSI Improved shortwave radiation output products including DNI and DHI. Experiments with cloud-aerosol enhanced versions of WRF. Probabilistic solar/shortwave forecasts with time-lagged HRRR ensemble members. Application of improved cloud microphysics schemes in WRF for HRRR application. 9

10. More Enhancement areas for HRRR Testing of EnKF/hybrid data assimilation in RAP to better initialize HRRR. Evaluation of HRRR GHI/DHI/DNI forecasts. Application of real-time HRRR-chem and/or RAP-chem runs and assimilation cycles to provide real-time aerosol forecasts to complement HRRR GHI/DHI/DNI forecasts. 10

11. Cloud Modeling issues – another area of development Microphysics schemes in WRF do not provide for partial cloudiness Need for partial horizontal coverage will depend on horizontal grid spacing Cloud layers are often thin (say, 20-75m) so predicted optical thickness of clouds with typical vertical resolution (typically, 250m or more above 1- 2 km AGL) likely to be in error Will pdf-based cloud parameterizations (or optical depth parameterizations) be useful? Need for tightening the coupling between microphysics and radiation Not all WRF radiation-microphysics combinations are compatible Water clouds: optical thickness depends on cloud drop size for a given cloud water mixing ratio and depth of cloud—need coupling of aerosol (CCN) with microphysics Ice clouds (Cirrus): optical thickness depends on crystal habit as well as cloud-ice mixing ratio and depth of cloud Development of forward models and obs error characteristics in order to assimilate AOD measurements (need more of these, of course) into models. 11

12. 12 Local Analysis and Prediction System (LAPS) 12

13. Global Horizontal Irradiance Analysis + Observations 3-Frames at 15-minute interval Utilizes GOES imagery and 3-D cloud analysis allowing rapid 1-3km resolution update LAPS 13

14. Enhancement of Satellite Products NOAA Operations generates cloud and solar irradiance products hourly with a spatial resolution of 12.5 km (current) and 4km in 2013. These are available via the NESDIS/GSIP system which runs the PATMOS-x cloud and SASRAB irradiance algorithms. NOAA algorithms can be generated at higher spatial and temporal resolution to support this call. Products will be available with a 15 minutes latency and a spatial resolution of 4 km. Spatial resolutions of 1km will be attempted for the 15 minute CONUS scans. Cloud product suite includes cloud optical depth, transmission, phase, microphysics and height. Algorithms developed by Andrew Heidinger. Solar Irradiance product suite includes GHI and DHI from the SASRAB algorithm developed by Istvan Laszlo (NOAA). These products will be computed from the current operational algorithms and their validity at 1km will be assessed. 14

15. Model Verification 15

16. Model Verification SURFRAD ISIS Global horizontal irradiance Direct normal irradiance Diffuse horizontal irradiance Aerosol optical depth and spectral at five wavelengths Ultraviolet B All-sky imaging Thermal infrared Upwelling global Upwelling thermal infrared Wind, temperature, humidity, and pressure Global horizontal irradiance Diffuse horizontal irradiance Direct normal irradiance Ultraviolet B 16

17. First-Class Thermopile Instruments Used to Measure Direct Beam, Diffuse, and Global Solar Irradiance at All Fourteen SURFRAD and ISIS Sites Direct Beam Sensor (Thermopile Pyrheliometer) Thermopile Pyranometer for Diffuse and Global Irradiance Diffuse (sun blocked by ball) Total Direct Kipp & Zonen instruments displayed, but a variety of instruments used in SURFRAD and ISIS Sun tracker Infrared NOAA Cloud & Radiation Test Bed Measurements 17

18. Provision of SURFRAD Data in Real-Time For real-time data upgrade to all SURFRAD and ISIS communications is required 18

19. Leveraging Existing Data NOAA can act as an “honest broker” to protect business proprietary data for use in both research and model initialization NOAA has a long history of protecting business proprietary data. One example is ACARS, the Aircraft Communications Addressing and Reporting System ACARS began in late 1970s as a system for relaying messages between aircraft and ground stations As the system capabilities improved, both aircraft position and weather data were made available Recognizing the need for improved forecasts, one airline decided to share their proprietary data with NOAA. Other airlines soon followed, but they didn’t share with each other fearing that a competitor would gain advantage for fuel economy of smooth air. Advances were made in weather models and flight forecasts improved. The Wind Forecast Improvement Project (WFIP) required that data sharing agreements, via non-disclosure agreements, be put into place between private companies and NOAA to support research. 19

20. Technology-Specific Forecasting Evaluation 20

21. Closing Comments Forecasting FOA Letter of Intent (required) due: June 01, 2012, 5:00 PM Eastern Time. Forecasting FOA Full Applications due: June 28, 2012, 5:00 PM Eastern Time. This presentation and a recording of this webinar will be posted on EERE Exchange for your reference. Any questions related to this webinar or the Funding Opportunity Announcement in general should be submitted to the Solar Forecasting email box at solarforecasting@go.doe.gov. All questions and answers will be posted on EERE Exchange.solarforecasting@go.doe.gov 21

22. Acronyms ACARS = Aircraft Communications Addressing and Reporting System AGL = Above ground level AOD = Aerosol Optical Depth ARW = Advanced Research WRF (version of WRF model) CCN = Cloud condensation nuclei CONUS = Contiguous United States (lower 48) dBz = Decibels DHI = Direct horizontal irradiance DNI = Direct normal irradiance DOE = Department of Energy EnKF = Ensemble Kalman Filter ESRL = Earth System Research Laboratory (in NOAA) GHI = Global horizontal irradiance GOES = Geostationary Operational Environmental Satellite GSIP = GOES Surface and Insolation Products GSI = Gridpoint Statistical Interpolation (data assimilation) HRRR = High-Resolution Rapid Refresh (model) ISIS = Integrated Surface Irradiance Study (observing network) 22

23. Acronyms (cont) LAPS = Local Analysis and Prediction System METAR = Meteorological Aviation Report NAM = North American Mesoscale (NOAA model) NCEP = National Centers for Environmental Prediction NOAA = National Oceanic and Atmospheric Administration PATMOS-x = Pathfinder ATMOSpheres-extended PIREP = Pilot report RAP = Rapid Refresh (NOAA model) RUC = Rapid Update Cycle (NOAA model) SAO = Surface Aviation Observations SASRAB = Satellite Algorithm for Shortwave RAdiation Budget SURFRAD = Surface Radiation (observing network) WFIP = Wind Forecast Improvement Project WRF = Weather Research and Forecasting (model) 23