Presented by:

1. Critical Design Review Full-Disk GOES Surface and Insolation Products (GSIP-fd)September 22, 2008 Presented by: Istvan Laszlo1, Hanjun Ding2, Andrew Heidinger1, Ronald Vogel3, William Straka4, Guang Guo3, Mark Eakin1, Kenneth Mitchell5 1 NOAA/NESDIS/STAR 2 NOAA/NESDIS/OSDPD 3 IMSG 4 Cooperative Institute for Meteorological Satellite Studies, UW 5 NOAA/NWS

2. Review Agenda 2

3. Outline • Introduction • PDR Report and Actions • Algorithm Theoretical Basis • System Design • Quality Assurance • Sample Products • Documentation • Archive • Practical Considerations • Operational Implementation • Examples of application – User Involvement • Risks and Actions • Summary 3

4. Introduction Presented by Istvan Laszlo IPT Team Lead NOAA/NESDIS/STAR

5. Contents • Project Objectives • Requirement • Users • Team • Plan of Operations • Project History • Entry and Exit Criteria • Project Timeline • Review Objectives 5

6. Project Objectives • Extend domain and spatial resolution of surface insolation products from GOES. • Develop algorithms for monitoring quality of insolation products. • Deliver algorithms and documentation to OSDPD. 6

7. Requirement • Requirement(s): • SPSRB 0409-5: “Full-disk GOES Surface and Insolation Products” • Summary: A high spatial resolution solar radiation product to be used in models for predicting coral bleaching and the hydrological cycle. • NOAA Mission Goal supported: • Ecosystems, Climate, Weather and Water. • Mission priority: • Mission Critical - Cannot meet operational mission objectives without this requirement. 7

8. Users • User community: • Coral reef managers • users of NOAA/HotSpot product suite • NCEP and the hydrology community. • Benefit to user: • The high spatial and temporal resolution surface insolation product is expected to vastly improve the accuracy of estimates of coral bleaching and improve the user’s ability to determine the possibility of mortality. • Extended spatial domain. 8

9. Integrated Product Team (IPT) • IPT Lead: Istvan Laszlo (STAR) • IPT Backup Lead: Hanjun Ding (OSDPD) • NESDIS team: • STAR: Andrew Heidinger, Istvan Laszlo, Dan Tarpley*, Xiangqian Wu • OSDPD: Hanjun Ding • OSD: Tom Schott • Data Center: CLASS (and Univ. of MD) • Others: Guang Guo and Ronald Vogel (IMSG), William Straka (UW/CIMSS), Liqun Ma • User team • Lead: Mark Eakin (STAR) • Others: Kenneth Mitchell (NWS/NCEP) • Oversight Panel (OP) leads: Earth Radiation Budget POP co-chairs (I. Laszlo and H. Ding). *retired 9

10. Project History • SPSRB Proposal for Product Development review (Project Plan) • Date: December 15, 2005 • Recommendation: prepare Product Development Decision Briefing • SPSRB Initial Technical Assessment review (Product/Service Development Decision Brief) • Date: March 16, 2006 (?). • Recommendation: add extensive QA/QC and prepare product development plan. • Preliminary Design Review • Date: April 30, 2007 • Guidance: discuss risks, OSDPD to address transition to operations • Annual Project Plan reviews and Executive Board guidance • Date: Nov 13, 2006; Aug 22, 2007, Aug 6, 2008 • Guidance: prepare Data Submission Agreement, update long term maintenance plan for OSDPD, update purchase item milestones, examine need for Linux hardware. 10

11. Plan of Operations (1) • Archive Plan • Need for archive: Model validation and impact studies require availability of “long-term” insolation data. (CONUS product is archived at NESDIS/STAR and UMD.) • Archive location: NCDC/CLASS and/or UMD; • Duration of archive: Forever or until quality is superseded by other data. • Deliverables: Documentation of data, ATBD; insolation and related data. • Long Term Maintenance Plan • STAR Radiation Budget Lead/Expert funded from STAR base will design fixes for uncovered problems; design improvements and seek funding for their implementation. • OSDPD covers periodic hardware maintenance and production monitoring; source of funding is base. 11

12. Plan of Operations (2) • Quality Monitoring Plan • Quality Control (QC) tools developed/used: Semi-automatic quality control of inputs, periodic calibration update; automated system for insolation QC. • QC tools are used by STAR Radiation Budget Lead; and OSPDP product maintenance personnel. • The automated QC system will acquire, match and statistically compare satellite and ground data and alert if comparison statistics exceed pre-defined thresholds on 24/7. It will also verify status of all input fields. 8/5 monitoring of system log files by OSDPD personnel is estimated. • Documentation/Metadata Plan • STAR contractor will prepare the algorithm/software/implementation documentation and metadata. • User Involvement Plan • User requirements were updated at the PDR; the current CDR is another opportunity for updates. • Periodical briefing to users (last three such briefings took place in Sep 2007 and Jan and Jun 2008) 12

13. Entry Criteria • PDR Risks and Actions • Review of CDR for Full-Disk GOES Surface and Insolation Products • Implementation Concept • Requirements • Algorithm Theoretical Basis • Software Architecture • Design Overview 13

14. Exit Criteria • Critical Design Review Report • The CDR Report (CDRR) will be compiled after the CDR • The report will contain: • CDR presentation • Actions • Comments 14

15. Project TimelineSPSRB Milestones and Key Tasks Product Delivery/Tracking Name: Full-Disk GOES Surface and Insolation Product • AUG 06 (AUG 06) (APR 06) (Mar 06) : Development Phase Begins • APR 06: IPT Branch Lead informed to begin product development • SEP 06: Initial Archive Requirements identified • NOV 06: Quality Monitoring Concept Defined • FEB 07: Long-term Maintenance Concept Defined • APR 07: Preliminary Design Review • MAY 07: Development processing system defined • MAY 07: Development Hardware Installed • OCT 07: Test case processed • FEB 08: Code is prepared for implementation • MAR 08: Final Archive requirements identified (in progress) • MAY 08: Operational and backup processing defined • JUL 08: Software code review • SEP 08 (AUG 08) (JAN 08): Critical Design Review (re-scheduled due to changes in code, etc.) • JUL 08 (JUN 08) (APR 08) (Nov 07) : Pre-operational Phase Begins • JUL 08: Operational and backup processing capabilities in place • JUL 08: Pre-operational product output evaluated & tested • AUG 08: Automated QC available to ensure product quality • AUG 08: Test data are ready for user evaluation • AUG 08: Code transitions to operations; all documentation is complete • SEP 08: Operational and backup capabilities reach ops status • OCT 08: Operational product is available for user • NOV 08: Brief SPSRB capability is ready to operational • DEC 08 (DEC 08) (OCT 08) (May 08): Operational Phase Begins • DEC 08: SPSRB declares product operational • SPSRB secretaries/manager updates the product metrics on the SPSRB web • OSD updates Satellite Products database • MAR 09: Old CONUS system shut down 15

16. Review Objectives • Review the • requirements • implementation concept • algorithm theoretical basis • system design and description • quality assurance procedures • documentation • archive • science and implementation risks 16

17. PDR Report and Actions Presented by Istvan Laszlo STAR

18. Risks and Actions Reported at PDR • This section serves as the Preliminary Design Review Report (PDRR) • The PDRR includes an assessment of two risks which have been closed • The PDRR includes the status of risk-associated actions, of which none remain open • Remaining or new risks and actions are addressed later in this CDR.

19. Risk Summary at PDR • Risk 1: At PDR, domains were defined by fixed values of latitude and longitude • Risk assessment: Medium. • Impact: Need data from GE and GW to cover CONUS • Mitigation: New domains are defined by satellite data availability • Status: Closed GOES West GOES East Full Disk Extended Northern Hemisphere

20. Risk Summary at PDR (2) • Risk 2: Detection of snow is inadequate under some cloudy conditions. • Risk assessment: High. • Impact: Insolation estimates are inaccurate under some snow conditions. • Mitigation: Use data from the Interactive Multisensor Snow and Ice Mapping System (IMS). • Status: Closed

21. Algorithm Theoretical BasisSurface and TOA SW radiation Presented by Istvan Laszlo IPT Lead STAR

22. SW Radiation Budget Algorithm Theoretical Basis • Purpose: Provide a physical and mathematical description of the GSIP-fd SW radiation budget retrieval algorithm for product developers, reviewers and users. • SW (shortwave) radiation budget is described by a set of upwelling and downwelling radiative fluxes at the surface and at the top of atmosphere (TOA) in the spectral interval of 0.2-4.0 μm. • ATB for only the SW radiation budget is reviewed here. 22

23. Algorithm Approach • A well established insolation algorithm is used; it has been • implemented in the NESDIS CONUS GOES Surface and Insolation Product (CONUS-GSIP); • selected and implemented as primary algorithm in the NASA GEWEX (Global Energy and Water Cycle Experiment) Surface Radiation Budget Project; • used to provide insolation in GEWEX Americas Prediction Project • Performance and application are documented in over ten peer-reviewed publications • Expected performance: • Bias of hourly data: (-40)-(+25) W/m2 depending on location • Root mean square error (RMSE) of hourly data: 70-120 W/m2 depending on location 23

24. Shortfalls of GSIP-CONUS • CONUS GSIP insolation has “low” spatial resolution, does not cover much ocean • Accuracy of current product over ocean has not been thoroughly established • GSIP-fd • increases spatial resolution, • Expands spatial coverage of oceanic regions, • thoroughly evaluates insolation over both ocean and land, • implements semi-automatic quality control of inputs, periodic calibration updates and quality-check of insolation output. • Benefits to user from GSIP-fd: • Improved accuracy and high-resolution insolation data for four domains covering portions of the Atlantic and Pacific Oceans that will lead to improved estimates of coral bleaching. • Provide data needed for validation and forcing of NWP surface physics. • Thoroughly characterized error statistics on expected data quality. 24

25. Capabilities Assessment 25

26. Algorithm Objectives • Meet the requirement specified for the surface insolation product. • Maintain heritage with GSIP-CONUS and ensure continuity/consistency of insolation. • Modular in design to support enhancements. • Simple to implement and robust for operational use. 26

27. Application of LUT for flux retrieval Physical Basis • Strong correlation exists between SW atmospheric transmittance and TOA albedo. • TOA flux is obtained by estimating the TOA albedo from narrowband ABI reflectances. • Surface flux is obtained by estimating atmospheric transmittance from TOA albedo. • All-sky flux is calculated as cloud-fraction weighted average of clear and cloudy fluxes. Albedo TOA albedo

28. The “theory” of SW ERB retrieval • TOA albedo, R and total transmittance, T are related. • Function h depends on the same quantities as R and T depend on. • The form of h is established by RT modeling. In GSIP, h is made available in the form of look up tables (LUT). • In GSIP, and are determined from GFS data. Satellite observations of TOA albedo are used to estimate

29. TOA Broadband Albedo • The insolation algorithm needs broadband (SW) reflectance • Two step approach is used: • Spectral transformation of narrowband radiance broadband radiance (based on RT simulations) • Angular transformation of broadband bidirectional reflectance to broadband albedo (uses ERBE angular models).

30. Composite Clear Reflectance (CCR) • Generated from a 28-day sequence of channel 1 images by selecting the darkest pixels in the sequence • Assumption: • Darkest pixel corresponds to the clearest (cloud-free) condition • Used for retrieving surface albedo assuming minimum amount of aerosol (currently, AOD=0.05 but there is an option to read in monthly climatology)

31. Concept of Operations • For each of four domains: • McIDAS area files are read, • counts are converted to radiances, • composite clear reflectances are determined, • ancillary data are read, • cloudy and clear pixels are identified and gridded, • insolation algorithm is applied to the gridded data, • results are compared to expected limits and quality flags are set, • output is converted to two formats and sent to ftp server for distribution; • images of products are displayed on Web in real-time. 31

32. Satellite Data Processing Calibration Narrowband reflectance Cloud detection Compositing Clear & cloudy reflectance Digital count Clear composite reflectance Spectral+angular correction Retrieval algorithm Clear & cloudy broadband albedo Surface irradiance

33. Flux Retrieval Water vapor and ozone amount Clear-sky composite albedo Clear-sky albedo Cloudy-sky albedo aerosol optical depth cloud optical depth Surface albedo Clear-sky flux Cloudy-sky flux Aerosol climatology All-sky flux

34. Ancillary Data Requirements • Dynamic data: • total column amounts of ozone and precipitable water from the Global Forecast System (GFS), • snow cover from the Interactive Multisensor Snow and Ice Mapping System (IMS). • Static data: • surface elevation • IGBP surface type maps. 34

35. Product Deliverable Details N=New E=Enhanced R=Replacement T=Tailored LW products are NOT included in table 35

36. Algorithm Theoretical BasisCloud Mask, Cloud Type, Surface Temperature, Cloud Top Temperature, Cloud Microphysics Presented by Andrew Heidinger STAR

37. Baseline Cloud Mask • The Baseline cloud mask is the Clouds from AVHRR Extended (CLAVR-x) cloud mask applied to GOES-R • CLAVR-x cloud mask documentation from OSDPD and http://cimss.ssec.wisc.edu/clavr • Contains several tests to determine cloud mask. • Thermal Uniformity Test • Reflectance Uniformity Test • Reflectance Gross Contrast Test • Thermal Gross Contrast Test • RTCT - Relative Thermal Contrast Test • RVCT - Relative Visible Contrast Test • Emissivity at Tropopause Test

38. Cloud Type • This module performs a cloud typing decision on pixel by pixel basis. The resulting cloud type codes are: • 0 - clear • 1 - partly cloud • 2 - liquid cloud • 3 - mixed phase cloud • 4 - glaciated • 5 - cirrus cloud • 6 - cirrus over lower • Typing done based off of brightness temperature differences and reflectance tests (daytime) • Primarily uses the 3.9, 11 micron channels with some 12 micron channel tests for non-GOES12 type satellites. • Concept adopted for NPOESS/VIIRS • Pavolonis, Michael J.; Heidinger, Andrew K. and Uttal, Taneil. Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons. Journal of Applied Meteorology, Volume 44, Issue 6, 2005, pp.804-826.

39. Baseline Surface Temperature • The function is to calculate surface temperature and radiative temperature. • Using the existing clear sky RTM to atmospherically correct the 11 micron radiance. This radiance and the surface emissivity are then used to derive a surface temperature via the Planck function

40. Cloud Top TemperatureSplit Window Technique • The split window retrieval estimates cloud top temperature, emissivity and microphysical index using the 11 and 12 micron radiances with the clear-sky forward model. • Assumptions: • Single layer clouds • Each cloud is comprised of one phase only • The cloud can be modeled as an isothermal layer. • Known multilayer clouds can be modeled by placing an opaque cloud beneath the higher cloud at 2 km above the surface. • Using the derived CTT, we estimate the cloud top pressure and height using the NWP temperature profile, which corresponds to each pressure level. Special logic in the presence of inversions is employed. • Adapted from the CLAVR-x split window technique. Methodology extended to GOES-R AWG algorithm. • Heidinger, Andrew K. and Pavolonis, Michael J. Gazing at cirrus clouds through a split-window Part I: Methodology. Journal of Applied Meteorology, submitted 10/2007.

41. Cloud Top Temperature CO2 Slicing Technique • The IR window technique is used as a first guess at the pressure. • This locates the closest two temperature levels to the observed 11 micron temperature, and interpolates between the two to get a interpolated pressure level. • This is a simplified version of the CO2 slicing method to determine cloud top pressure. • Uses PFAAST RTM model (same as used with GOES Sounder) • Only works with satellites that contain a 13 and 11 micron channels. (GOES-NOP) • Does not work with mixed phase and opaque ice clouds. In this case, the 11 mm temperature based estimate is used. • Schreiner, A.J. and Schmit, T.J.: Derived cloud products from the GOES-M imager. 11th Conference on Satellite Meteorology and Oceanography, Amer. Meteor. Soc., Madison, pp. 420-423,

42. Cloud Microphysical Properties • Used a 1d-var retrieval approach as outlined in Rodger (1976) to retrieve cloud microphysical properties. • Because the 0.64 micron channel is used, data can only be retrieved during the daytime. This means that liquid water path (lwp), ice water path (iwp), cloud optical depth and effective radius are only available during the day • For efficiency, the lookup tables dimensions use log10 tau and log10 of effective radius and this is what is estimated here. The optical depth and effective radius are converted from logarithms at the end. • There are a couple of assumptions: • The lower surface is Lambertian • The clouds are plane parallel • The clouds are single layer • The cloud-top phase applies throughout the entire cloud. • The particle size is uniform throughout the cloud. • This algorithm was adapted from the CLAVR-x cloud microphysics algorithm. • Heidinger, Andrew K.. Rapid daytime estimation of cloud properties over a large area from radiance distributions. Journal of Atmospheric and Oceanic Technology, Volume 20, Issue 9, 2003, pp.1237-1250.

43. Algorithm Theoretical BasisRoutine Validation and QC Presented by GuangGuo IMSG

44. GSIP ValidationAlgorithm Theoretical Basis • Purpose: Provide physical concept, approaches, and procedures for the quality control (QC) of ground radiative flux measurements and for validation of GSIP-fd surface shortwave radiation budget retrievals for product developers and users. • Surface shortwave radiation budget includes downwelling radiative fluxes (SW, 0.2-4.0 μm) and Photosynthetically Active Radiation (PAR, 0.4-0.7 μm) at the surface. • The ground radiative flux measurements that pass the QC tests are used in the GSIP validation. 44

45. Algorithm Objectives • Quality control the ground radiative flux measurements to ensure quality of ground data. • Collocate the satellite and ground data to facilitate comparisons. • Generate comparison statistics on different time scales to characterize quality of GSIP retrievals. • Provide quality summaries of ground data, satellite retrievals, and comparisons. 45

46. The “Theory” of Quality Control • Physical Approaches (QC) • Major approaches used in the GSIP-fd validation system • Developed primarily by Charles N. Long and Ellsworth G. Dutton, currently used in the World Climate Research Program (WCRP) Baseline Surface Radiation Network (BSRN), and modified for GSIP-fd validation. • Long and Dutton’s approach • Physically possible limits: ranges are setup for Global SWdn, Diffuse SW, Direct SW, SWup, LWdn, LWup ( total six measured variables) • Comparison limits: ratio of globalSW/sumSW, ratio of dirSW/GlobalSW, LWup to temp, LWdn to temp. • GSIP-fd QC procedures • Only Global SWdn and/or PAR data are available from most networks • QC flags are 0, 1, 9 if Global SWdn is in [0, 1368 W/m2] ([0, 0.6X1368 W/m2 for PAR]), beyond the range, or missing. • Ground data with QC flag of 0 are regarded as qualified data and are used in the validation. 46

47. The “Theory” of Quality Control (2) • Statistical approach (ST) • A supplement method for reference. • Developed by G.Guo and J. A. Coakley. • Evaluate the ground data based on their uniformity. • Guo and Coakley’s approach: • 20 days high resolution NSW and LW ground observations • Solar zenith angle less than 75° • NSW and LW means and standard deviations for every 30-min • Cloud-free condition: LW σ < 5 Wm-2, NSW σ< 10 Wm-2 • Cloudy condition: LW σ < 5 Wm-2, NSW σ < 100 Wm-2 • Cutoff line: (10 Wm-2, 5 percentile), (100 Wm-2, 95 percentile) • An example: 20 days 1-min NSW and LW observations in August, 2001 • GSIP ST procedures • Only SW data are available from most networks • ST flag is 0, 1, and 9 for statistically good, bad, and missing data. • The ground data with ST flag of 0 are regarded as good data. 47

48. The “Theory” of Quality Control (3)Illustration of the ST Approach NSWCloud-free σNSW < 10 W/m2 Cloudy σNSW <100 W/m2 LWcloud-free σLW < 5 W/m2 Cloudy σLW s<5 W/m2

49. The “Theory” of GSIP Validation • Satellite data are averaged within 50 km (or user determined area) centered on ground locations • “Hourly” average of satellite data is calculated by normalizing the instantaneous value to the 60-min average of the solar zenith angle • Ground data are averaged for 60-min around the time of GOES overpass

50. Validation Concept (1) • GSIP Real-Time Validation • Both GSIP satellite products and surface radiative flux measurements are on a real-time basis • Validation outputs are on a real-time basis • Surface radiative flux observation measured at five ground networks (about 20 stations ) are used in GSIP real-time validation • Historical Validation • Uses offline GSIP products or products from GSIP model • Uses offline ground data (more options) • Historical validation complements real-time validation • Validation Metrics for assessing performance: bias, root mean square error, histogram (cumulative). 50