GOES-R Algorithm Working Group

1. GOES-R Algorithm Working Group • Mission: • To select, develop, test, validate, and demonstrate Level-2+ algorithms that meet the GOES-R F&PS requirements and provide them to the GOES-R Ground Segment. • Provide sustained life cycle validation and Level-2 product enhancements • End-to-End Capabilities • Instrument Trade Studies • Proxy Dataset Development • Algorithm Development and Testing • Product Demonstration Systems • Development of Cal/Val Tools • Integrated Cal/Val Enterprise System • Radiance and Product Validation • Algorithm and application improvements • User Readiness and Education

2. AWG Scope of Work • Development of Level-2 product algorithms (Baseline & *Option-2) • Support to Harris/AER’s Algorithm Implementation Activities • Development of Level-2 product validation tools needed post-launch for: • Routine monitoring of L2 product performance (accuracy, precision) • “Deep-dive” assessments and analysis of products (problem resolution) • Continued validation and characterization of product performance • Through pre-launchLevel-2 product demonstrations and validation studies • Using available ABI proxy data and reference/”ground truth” measurements • STAR/AWG will provide science support immediately after launch and on a continuous basis during the years after launch (in coordination with OSPO) • Support anomaly resolution • Perform L2 product algorithm updates • Development of new algorithms and application and support the transition of these into operational environment * - Now termed “Future Capabilities” Products

3. Baseline Products Future Capabilities GOES-R Products

4. AWG Teams

5. AWG Deliverables & Status • Algorithm Packages (APs) • Algorithm Theoretical Basis Documents (ATBD) • Instrument proxy datasets • Product output datasets (for reproducibilty) • Algorithm Interfaces and Ancillary Data Description (AIADD) document • Schedule of Deliveries to the GOES-R Program • September 2008: As-Is ATBDs • September 2009: 80% APs for Baseline Products • November 2010: 100% APs for Baseline Products 80% APs for Option 2 Products • September 2011: 100% APs for Option 2 Products; 80% APs for Visibility, Rainfall Potential & Rainfall Probability • September 2012: 100% APs for Visibility, Rainfall Potential, and Rainfall Probability; Routine Validation Tool Documentation

6. GOES-R vs JPSS IDPS Products • Aerosol Detection (including Smoke and Dust) • Aerosol Particle Size • Suspended Matter / Optical Depth • Volcanic Ash Detection and Height • Aircraft Icing Threat • Cloud & Moisture Imagery (KPPs) • Cloud Layers / Heights & Thickness • Cloud Ice Water Path • Cloud Liquid Water • Cloud Optical Depth • Cloud Particle Size Distribution • Cloud Top Phase • Cloud Top Height • Cloud Top Pressure • Cloud Top Temperature • Cloud Type • Clear Sky Masks • Fire / Hot Spot Characterization • Aerosol Optical Thickness • Aerosol Particle Size • Imagery • Cloud Base Height • Cloud Coverage/Layers • Cloud Effective Particle Size • Cloud Ice Water Path • Cloud Liquid Water • Cloud Mask (IP) • Cloud Optical Thickness • Cloud Top Height • Cloud Top Pressure • Cloud Top Temperature • Active Fires red = GOES-R baseline products

7. GOES-R vs JPSS Products • Probability of Rainfall • Rainfall Potential • Rainfall Rate / QPE • Legacy Atm. Vertical Moisture Profile • Legacy Atm. Vertical Temperature • Profile Derived Stability Indices (5) • Total Precipitable Water • Land Surface (Skin) Temperature • Surface Albedo • Surface Emissivity • Vegetation Fraction • Green Vegetation Index • Sea & Lake Ice / Age • Sea & Lake Ice / Concentration • Sea & Lake Ice / Extent • Sea & Lake Ice / Motion • Ice Cover / Landlocked: Hemispheric Snow Cover • Snow Depth (Over Plains) Sea Surface Temps • Atmospheric Vertical Moisture Profile • Atmospheric Vertical Temperature Profile • Ozone Total • ColumnOzoneProfile • Land Surface Temperature • Albedo (Surface) • Vegetation Index • Sea Ice Concentration • Ice Surface Temperature • Snow Cover • Sea Surface Temperature • Ocean Color • Suspended Matter red = GOES-R future capability products

8. Backup

9. Cloud-Top Pressure Product • Algorithm Highlights • Algorithm uses the 11, 12 and 13.3mm channels to estimate cloud temperature, cloud emissivity and a cloud microphysics. • Algorithm uses an optimal estimation approach that provides error estimates. • Cloud pressure and height are computed from NWP profiles. • For multi-layer clouds, lower cloud height estimates obtained from surrounding pixels. • Cloud heights in the presence of low level inversions are handled using similar logic that is employed in the MODIS algorithms. • Operational Applications • Used to assign heights to derived motion winds • Aviation Terminal Aerodrome Forecasts (TAFs) • Supplements Automated Surface Observing System (ASOS) with upper-level cloud information • Cloud initialization; Assimilation into NWP models • Climate prediction Andrew Heidinger, NOAA/NESDIS/STAR

10. Fire/Hot Spot Characterization Algorithm Highlights Heritage lies with the GOES operational Wildfire Automated Biomass Burning Algorithm (WF_ABBA) Dynamic, multi-spectral, thresholding contextual algorithm Utilizes the 0.64, 3.9, 11.2 and 12.3 mm channels Leverages ABI’s higher spatial and temporal resolution data Operational Applications Fire weather forecasting Air quality forecasting 10 10 10 10 Chris Schmidt, CIMSS

11. Snow Cover Algorithm Highlights Retrieves sub-pixel fractional snow cover and grain size estimates via computationally efficient spectral mixture modeling Heritage derived from MEMSCAG (Multiple Endmember Snow Covered Area and Grain size) algorithm for imaging spectrometers (AVIRIS, Hyperion, HYDICE, ARTEMIS) MODIS-based fractional snow cover and grain size (MODSCAG) algorithm Leverages ABI’s higher temporal resolution data and spectral coverage (VIS/near IR bands :0.47, 0.64, 0.86, 1.6, and 2.25 um) Operational Applications Assimilation into NOAA/NOHRC snow model Hydrologic forecasts and warnings, including river and flood forecasts Stream-flow forecasting/modeling Snowpack monitoring, analysis Water management Climate studies MODIS Color Composite (Colorado Rockies) 30 April 2007 11 11 11 11 11 Retrieved Fractional Snow Cover Tom Painter, JPL

12. GOES-R Algorithm Working Group

13. GOES-R Algorithm Development Executive Board (ADEB) • Primary objectives • Provide an independent assessment of processes followed by the AWG in the course of their algorithm development activities • Provide a thorough, independenttechnical assessment of the GOES-R AWG Level-2 algorithms • Report findings back to the GOES-R Program • ADEB Membership • Representatives from stakeholder organizations (NWS, DoD, University, private industry) • Supported by a team of Independent Peer Reviewers (IPR) • Subject matter experts not involved with or funded by GOES-R GOES-R Program

14. From the GOES-R Algorithm Development Executive Board (ADEB) • Summary Finding: Overall, the Board found the briefings were very professional, orderly, and complete. The Board commends the work of the STAR/AWG and recommends delivery of most of the 100% baseline algorithms and their ATBDs to the GOES-R Program Office. (ADEB Final Report, October 28, 2010) • Summary Finding: Overall, the Board found the briefings were very professional, orderly, and complete. The Board commends the work of the STAR/AWG and recommends delivery of most of the 100% option 2 algorithms and their ATBDs to the GOES-R Program Office. (ADEB Final Report, December 2, 2011) • The board recommends a special focus be given to the evaluation of future capability algorithms using a fused or integrated approach to deliver the best science. Integration and / or fusion of these algorithms with respect to multi-observational approaches (e.g. other satellites, ground based radar, surface and airborne in-situ observations, etc.) as well as assimilation into numerical models, could produce far more effective results. Such approaches seem particularly relevant for products such as Convective Initiation, Icing Threat, Visibility, Rainfall Potential, and Rainfall Probability / Rain Rate (ADEB Final Report, December 2, 2011)

15. From the Independent Advisory Committee (IAC)NOAA Science Week (Apr 30 - May 4, 2012)… • Successful delivery of Baseline Products to the GOES-R Office and the delivery of the Option 2 (Future Capability) Products has been the result of outstanding scientific, technical and managerial work. All involved deserve commendation. • The recognition of the synergistic processing of Leo and Geo-data is an important step and the use of common software and an Enterprise approach is to be commended. Together with the use of models and data assimilation, the Polar and Geo-satellite applications and products are entering a new era. • Opportunity for LEO supplement / complement to GEO measurements for product generation has been enhanced greatly by the establishment of the Enterprise processing system. More efforts should be encouraged for combined products where LEO benchmarks are temporally continued with GEO measurements. An example could be LEO day-night band observations of fog can be extended into early morning with GEO visible composites.

16. Looking Ahead • Future Capabilities Product Algorithms • Presents an opportunity to further improve a number of the algorithms and/or their application to meet existing and evolving user needs. • Coordinated activities between the GOES-R Program Office, AWG, GOES-R3, GOES-R Proving Ground, JCSDA, JPSS, and users will be critical for meeting these evolving needs • GOES-R • AWG GOES-R Proving Ground User Community GOES-R Risk Reduction JPSS Proving Ground Supporting User Readiness is a responsibility of all!

17. AWG Algorithm Integration Team Roles & Responsibilities… Ocean Dynamics Team Soundings Team Winds Team Imagery Team Aerosol Air Quality Team Clouds Team Algorithm Integration Team Hydrology Team Aviation Team Land Team Radiation Budget Team SST Team Lightning Team Cryosphere Team • Manage Processes • Initial Requirements Analysis • Algorithm Design Review • Critical Design Review • Test Readiness Review • Code Unit Test Review • Algorithm Readiness Review • Software CM Coordinate Algorithm Development & Integration Activities Develop and Maintain Algorithm Framework and Testbed Execute & Follow Integrates algorithm software from the product application teams • Enforce Standards • Algorithm Theoretical Basis • Document • Metadata (FGDC guidelines) • Interface Control • System Description • Users Manual • Fortran Programming • C/C++ Programming • Test Plan • Algorithm Implementation • Instructions • Latency Reports AWG Focal Point for interactions between AWG, GOES-R Ground Segment, and Harris/AER