Land Team Chair: Yunyue (Bob) Yu NOAA/NESDIS/STAR

1. GOES-R AWG Product Validation Tool DevelopmentLand Baseline ProductsLand Surface TemperatureandFire Detection and Characterization Land Team Chair: Yunyue (Bob) Yu NOAA/NESDIS/STAR

2. GOES-R AWG Product Validation Tool DevelopmentLand Surface Temperature Application Team Yunyue (Bob) Yu(STAR) Dan Tarpley (Short & Associates) Hui Xu, Xiao-long Wang (IMSG) Rob Hale (CIRA) Kostya Vinnikov (CICS)

3. LST Products • The ABI Land Surface Temperature (LST) algorithm generates the baseline products of land surface skin temperatures in three ABI scan modes: Full Disk, CONUS, Mesoscale.; • Meets the GOES-R mission requirements specified for the LST product; • Has a good heritage, will add to the LST climate data record; • Simplicity for implementation/ease of maintenance, operational robustness, and potential for improvement. Full Disk CONUS

4. Products Specifications *Requirement Change Requested: to be 2 km. Qualifiers

5. Validation Strategies • Utilize existing ground station data • Stations under GOES-R Imager coverage • Stations under MSG/SEVIRI coverage • Ground site characterization • Stringent cloud filtering • Multiple comparisons: satellite vs satellite, satellite vs ground station. • Direct and indirect comparisons • International cooperation SURFRAD Sites

6. Validation Strategies • Development for routine validation tools • Characterizations of SURFRAD and CRN ground sites • Routinely acquired matchup data sets of satellite and ground Data • Ground LST estimation • Procedures for converting point ground LST to “pixel” ground LST • Direct comparisons and statistics for each ground LST vs satellite LST for last x months • Time series plots of selected coincident LST and ground LST for last x months • Development for deep dive validation tools • All of the routine validation tools + • Data sets consisting of multiple years of clear radiances coincident with ground LSTs • Indirect comparison and statistics for each ground LST vs ground LST climatology for last x years • Comparisons and statistics for GOES-R LST vs other satellite LST • Routines for calibrating LST algorithm coeffs using the validation results

7. Validation Tools Components of Validation Tools Satellite Data Satellite Data Reader Geolocation Match-up Ground Data Reader Time Match-up Ground Data Match-up Datasets Satellite Cloud Mask Satellite LST Calculation/Extraction Ground Data Mask Ground LST Estimation/Extraction Manual Cloud Control Outputs (Plots, Tables, etc.) Direct Comparison Synthetic Analysis and Correction Indirect Comparison Statistical Analysis

8. Routine Validation Tools-- SURFRAD data results Comparison results of GOES-8 LST using six SURFRAD ground station data, in 2001.. Numbers (Table, left) and scatter plots (right) of the match-up LSTs derived from GOES-8 Imager data vs. LSTs estimated from SURFRAD stations in year 2001. Data sets in plots are stratified for daytime (red) and night time (blue) atmospheric conditions

9. Routine Validation Tools -- A visualization interface

10. T(x,y,t) T(x0,y0,t0) ”Deep-Dive” Validation Tools The Synthetic pixel/sub-pixel model Site characterization analysis using ASTER data— an integrated approach for understanding site representativeness and for site-to-pixel model development • Quantitatively characterize the sub-pixel heterogeneity and evaluate whether a ground site is adequately representative for the satellite pixel. The sub-pixels may be generated from pixels of a higher-resolution satellite. • For pixel that is relatively homogeneous, analyze statistical relationship of the ground-site sub-pixel with the surrounding sub-pixels: • {T(x,y) } ~ T(x0,y0) • Establish relationship between the objective pixel and its sub-pixels (i.e., up-scaling model), e.g., Tpixel = T(x,y) + DT (time dependent?) ASTER pixel MODIS pixel The site pixel Surface heterogeneity is shown in a 4km x 4km Google map (1km x 1km, in the center box) around the Bondville station area Site-to-Pixel Statistical Relationship for 5 SURFRAD sites

11. ”Deep-Dive” Validation ToolsComparison of SEVIRI-Retrieved LST and station LST at Evora • Apparent diurnal patterns are shown in the 10 days’ LST comparison profiles for selected months. • Comparison of LST diurnal profiles revealed higher station LST than SEVIRI LST around mid-days (i.e. maximum daily LST) and slightly higher SEVIRI LST than station LST at night (with low LST). • The diurnal differences are larger in warm months. Others ? We need to understand and fix this problem

12. ”Deep-Dive” Validation Tools-- Directional effect study Due to the satellite LST directional properties (surface components, topography, shadowing etc.), the satellite LST can be significantly different from different view angles. Deep dive validation tools may be used for case studies and improved algorithms. Goodwin Creek, MS, observation pairs are about 510. View Zenith of GOES-8/-10: 42.680/61.890

13. Complementary Posters Validation of Land Surface Temperature Algorithm for U.S. GOES-R Mission Y.Yu, H. Xu, X-L. Wang, D. Tarpley, R. Hale Development of a Multi-Satellite Validation System for NOAA/GOES-R ABI Land Surface Products K. Gallo, G. Stensaas,G. Chander, Y. Yu, M. Goldberg,R. Hale, and D. Tarpley Impacts of Emissivities in the Retrieval of SEVIRI LST and the Calculation of LST from Surface Measurements H. Xuand Y. Yu Developing Tools for LST Validation and Deep-Dive Analysis X-L. Wang, Y. Yu, H. Xu, D. Tarpley, R. Hale Land Surface and Air Temperature (LST & SAT) at Clear and Overcast skies K. Y. Vinnikov, Y. Yu, M. D. Goldberg, D. Tarpley, M. Chen, C. N. Long

14. GOES-R AWG Product Validation Tool DevelopmentFire Detection and Characterization Application Team Christopher Schmidt (CIMSS) Ivan Csiszar (STAR) Wilfrid Schroeder (CICS/UMD)

15. Products Fire detection and characterization algorithm properties: • Refresh rate: 5 minute CONUS, 15 minute full disk • Resolution: 2 km • Coverage: CONUS, full disk • ABI version of the current GOES Wildfire Automated Biomass Burning Algorithm (WF_ABBA) • Product outputs: • Fire location • Fire instantaneous size, temperature, and radiative power • Metadata mask including information about opaque clouds, solar reflection block-out zones, unusable ecosystem types.

16. Products

17. Validation Strategies FDCA Routine Validation Current practice for GOES WF_ABBA: No automated realtime method is available. Ground-based fire reports are incomplete and typically not available in realtime. At the Hazard Mapping System Human operators look at fire detections from various satellites and at satellite imagery to remove potential false alarms. This method is labor intensive and actual fire pixels are often removed.

18. Validation Strategies • FDCA Routine Validation • ABI near realtime validation: • Co-locate ABI fire pixels with other satellite data • Ground-based datasets tend to be incomplete and not available in realtime • Fire detections from other satellites (polar orbiting) can be used in near realtime • Perfect agreement is not expected. Due to resolution, viewing angle, and sensor property differences a substantial number of valid fires will be seen by only one platform • Other fire properties (instantaneous fire size, temperature, and radiative power) have no available near realtime validation source (see Deep-Dive tools) • Important note: the product requirement does not align with user expectations. The requirement states: • “2.0 K brightness temperature within dynamic range (275 K to 400 K)” • This applies to a pixel brightness temperature, and the algorithm achieves it for 100% of the fires where fire characteristics are calculated. When used to recalculate the input brightness temperature the fire characteristics match the input data to better than 0.0001 K.

19. Validation Strategies • FDCA Validation Tools • Routine validation tools: • Perform co-locations for individual fires and for clusters of fires • Provide statistics on matches • Table on following slide shows example of routine statistics from model- generated proxy data cases. 75 MW of fire radiative power is the estimated threshold for fire detectability. • Deep-Dive validation tools: • Allow for validation of fire location and properties • Utilize high-resolution data from satellite or aircraft to provide fire locations and enable estimates of fire size, temperature, and radiative power • Can be partially automated, availability of high resolution data is limiting factor

20. Validation Strategies 20

21. ”Deep-Dive” Validation Tools • Deep-dive fire detection and characterization validation tool builds on methods originally developed for MODIS and GOES Imager • Use of near-coincident (<15min) Landsat-class and airborne data to generate sub-pixel summary statistics of fire activity • Landsat-class data are used to assess fire detection performance • History of successful applications using ASTER, Landsat TM and ETM+ to estimate MODIS and GOES fire detection probabilities and commission error rates (false alarms). Methods published in seven peer reviewed journal articles • Limited fire characterization assessment (approximate fire size only). Frequent pixel saturation and lack of middle infrared band prevent assessment of ABI’s fire characterization parameters • Airborne sensors are used to assess fire characterization accuracy • High quality middle-infrared bands provide fine resolution data (<10m) with minimum saturation allowing full assessment of ABI’s fire characterization parameters (size, temperature, Fire Radiative Power) • Sampling is limited compared to Landsat-class data • Regional × hemispheric/global coverage • Targeting case-study analyses

22. Several national and international assets will be used to support ABI fire validation USGS Landsat Data Continuity Mission (2013) ESA Sentinel-2 (2013) DLR BIROS (2013) NASA HysPIRI (TBD ~2020) Airborne platforms (NASA/Ames Autonomous Modular Sensor-Wildfire; USFS FireMapper) Will perform continuous assimilation, processing and archival of reference fire data sets Daily alerts targeting false alarms, omission of large fires Main output: Quick looks (PNG) for visual inspection of problem areas showing ABI pixels overlaid on high resolution reference imagery Probability of detection curves and commission error rates derived from several weeks/months of accumulated validation data Main output: Tabular (ASCII) data containing pixel-based validation summary (graphic output optional) ”Deep-Dive” Validation Tools 22

23. ”Deep-Dive” Validation Tools Using Landsat-class imagery to validate ABI fire detection data Sample visual output of simulated ABI fire product (grid  2km ABI pixel footprints) overlaid on ASTER 30m resolution RGB (bands 8-3-1). Red grid cells indicate ABI fire detection pixels; green on background image corresponds to vegetation; bright red is indicative of surface fire ASTER binary (fire – no fire) active fire mask indicating 494 (30m resolution) active fire pixels coincident with GOES-R ABI simulated fire product 23

24. ”Deep-Dive” Validation Tools • For more visit: • Deep-Dive Validation of GOES-R Active Fire Detection and Characterization Product • at the poster session 24