1. 11 Ice Cover and Sea and Lake Ice Concentration with GOES-R ABI Presented by Yinghui Liu Presented by Yinghui Liu 1 Team Members: Yinghui Liu, Jeffrey R Key, Team Members: Yinghui Liu 1, Jeffrey R Key 2, and Xuanji Wang and Xuanji Wang 1 UW-Madison CIMSS 1 UW-Madison CIMSS NOAA/NESDIS/STAR 2 NOAA/NESDIS/STAR GOES-R AWG Cryosphere Team 2011 GOES-R AWG Annual Meeting, June 13-16, Fort Collins, CO

2. 22 Outline  Executive Summary  Algorithm Description  ADEB and IV&V Response Summary  Requirements Specification Evolution  Validation Strategy  Validation Results  Summary

3. 3 Executive Summary  The GOES-R Ice Cover and Sea and Lake Ice Concentration are Option 2 products.  Software Version 5 was delivered in May 2011. ATBD (100%) is on track for a August delivery.  The algorithm has been developed and tested using MODIS, and SEVIRI data as proxy, and validated with the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) products.  Validation studies indicate spec compliance

4. 4 MODIS true color image over Great Lakes on February 24 2008. Ice Cover: A binary yes/no detection of ice cover over ocean, inland lakes, and rivers. Ice Concentration: The fraction (in tenths) of the sea or lake surface covered by ice. Algorithm Description

5. 55 Reflectances of ice, clouds, and water (Riggs et al. 1999). 8% NDSI=(R vis -R swir ) /(R vis +R swir ) NDSI value: Water: ~ 0.5 New ice: ~ 1.0 Snow ice: ~ 1.0 Cloud: ~ 0.0 Ice cover Detection: Ice or snow covered ice show high NSDI value and high reflectance at visible and near-infrared bands. Algorithm Description Ice Cover

6. 666 Ice/snow surface temperature is retrieved by the following equation (Key et al. 1997). Ts = a + b T11 + cT12 + d [(T11-T12)(sec  -1)] Ts = the estimated surface temperature (K) T11 = the brightness temperatures (K) at 11 um T12 = the brightness temperatures (K) at 12 um  = sensor scan angle a, b, c, d = coefficients, derived for the following temperature ranges: T11 260K. The Algorithm Description Ice Cover

7. 77 In daytime, pixels with both NDSI and reflectance larger than set thresholds, with surface temperature lower than set thresholds are ice. NDSI in this algorithm is defined as NDSI=(R 1 -R 2 )/(R 1 +R 2 ) R 1 = reflectance at GOES-R ABI ch 3, 0.865  m R 2 = reflectance at GOES-R ABI ch 5, 1.61  m Threshold : 0.6 Reflectance at near infrared channel,Reflectance at near infrared channel, 0.865  m Threshold 0.08 At nighttime, pixels with surface temperature lower than set thresholds are ice. Algorithm Description Ice Cover

8. 8 Temperature Reflectance Pure water Pure ice Ice Concentration: Ice reflectance (temperature) change linearly with ice concentration Algorithm Description Ice Concentration

9. 99 Fractional ice concentration for each pixel (F p ) in a search window is then calculated as F p = (B p - B water ) / (B ice – B water ) B water = the reflectance/temperature (K) of a pure water pixel B ice = the reflectance/temperature (K) of a pure ice pixel B p = the observed reflectance/temperature (K) of the pixel.  In this algorithm, reflectance at GOES-R ABI channel 2 (0.64  m) is used during the day, and surface temperature is used at night.  The spatial resolution is 0.5 km at 0.64  m channel, and 2.0 km for surface temperature at sub-satellite FOV  B ice is determined using tie-point algorithm; B water is parameterized. Algorithm Description Ice Concentration

10. 10 step1 step2 step3 High Level Flowchart of the ice concentration and extent algorithm Ice cover and concentration algorithm begin Ice cover and concentration algorithm end Group-criteria detection Ice Tie point algorithm Ice concentration Ice cover ABI channel radiance, satellite viewing angles, cloud mask, land/water mask Algorithm Description

11. 11 Algorithm Changes from 80% to 100%  A new test, using the ice surface temperature in the daytime for ice detection is added in the version 5 code.  Pure water surface reflectance is parameterized as a function of water salinity and solar zenith angle. In the 80% version, it is a constant value.  Metadata output added  Quality flag added

12. 12  All comments on ATBD clarification have been addressed.  Suggestions on algorithm improvement have been considered and implemented.  Suggestions on validation work are taken and more extensive validation work is undergoing.  Feedback: I would recommend to explain this clearly in the beginning and possibly modify the title of ATBD to reflect “the ice over water” application, i.e. no land ice applications are included.  Response: In the beginning of the ATBD, we point out that the ice concentration is only retrieved in ABI pixels over water surfaces covered with ice. ADEB and IV&V Response Summary

13. 13  Feedback: While ATBD contains interesting comparisons between retrievals from MODIS, SEVIRI and AMSR-E, it requires more systematic analysis and quantitative evaluation of its performance. For 80% ATBD level, the material is likely adequate.  Response: More systematic analysis and quantitative validation of the products are undergoing using AMSR-E, ice charts, and Landsat data.  Feedback: Explain why temperature thresholds are not used during daytime ice detection. This would make algorithm more consistent between daytime and night time scenes.  Response: We added a new test, using the temperature in the daytime ice detection, in the version 5 code and updated this in the ATBD. ADEB and IV&V Response Summary

14. 14 NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementRangeMeasurementAccuracyProductRefreshRate/CoverageTimeVendorAllocatedGroundLatencyProductMeasurementPrecision Ice CoverGOES- R FDN/A2 km1 kmBinary yes/no detection 85% correct detection 180 min 77,756 sec N/A FD – Full Disk Requirements Specification Evolution February 2009 Ice Cover / Landlocked: Change the accuracy from Binary yes/no detection to 85% correct detection. Change the name to 'Ice Cover'. Sea & Lake Ice: Extent: Delete product.

15. 15 NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementRangeMeasurementAccuracyProduct RefreshRate/CoverageTimeVendor AllocatedGround LatencyProductMeasurementPrecision Sea & Lake Ice: Concentration GOES-R C: Regional & Great Lakes and US costal waters containing sea ice hazards to navigation FD: Sea ice covered waters in N & S Hemisphere s Ice Surface 3 km 10 km < = 1.5 km < = 5 km Ice concentration – 1/10 to 10/10 Ice concentratio n – 10% 180 min 6 hr 3236 sec 9716 sec 30% Requirements Specification Evolution No changes in specification in ice concentration

16. 16 Ice Cover and Sea and Lake Ice Concentration retrieved from MODIS data as proxy were validated with ice cover and concentration from the AMSR-E product as truth.  AMSR-E Level 3 product provides ice concentration over the Great Lakes and the Arctic Ocean. Ice cover is assigned when ice concentration is larger than 15%.  Ice cover and sea and lake ice concentration retrievals uses the GOES-R ABI algorithm, and MODIS data as proxy.  Two datasets are matched daily by averaging the retrieved product in the footprint of AMSR-E product.  Validation statistics are computed based on 1,576,298 matched pairs covering different seasons. Validation Strategy

17. 17 Validate of ice cover and concentration retrievals using MODIS data as proxy with ice cover and concentration results from Landsat, which has much higher spatial resolution, and ice charts, which have better spatial resolution than AMSR-E, are undergoing. Meanwhile, validation with AMSR-E are continuing. Validation Strategy

18. 18 Lake ice concentration (%) with MODIS Aqua data (left), MODIS true color image (middle), and from AMSR-E (right) over Great Lakes on February 24 2008. Ice concentration over Great Lakes Validation Strategy

19. 19 Case number Total pairs: 1576298 Sea/Lake ice cover determined from AMSR-E Water determined from AMSR-E Sea/Lake ice cover 1075124 Water305872 Correct detection ratio = (1075124+305872)/1576298 = 87.6% The product measurement accuracy is higher than required 85% correct detection. Performance of Ice Cover product Validation Results Ice cover

20. 20 Ice concentration difference of AMSRE product and MODIS Mean bias (%) (%) Standard deviation (%) Over Arctic Ocean 4.015.7 Over Great Lakes -4.025.6 Required measurement accuracy 10 Required measurement precision 30 Performance of Ice Concentration product The product measurement accuracy and precision meet the requirement. Validation Results Ice concentration

21. 21 Performance of ice concentration product Frequency distribution of ice concentration differences between the AMSR-E product and retrievals using the ABI algorithm based on selected 41 clear day MODIS data in four seasons in 2007 over the Arctic Ocean. Validation Results Ice concentration

22. 22 ABI allows us to monitor ice conditions at high temporal and spatial resolution. The ice cover product is generated using group threshold tests. Comparison of ice cover products using MODIS as proxy with AMSR-E shows that products exceed the required detection accuracy of 85%. The ice concentration product is produced using a tie-point algorithm. Validation of this product with the AMSR-E product shows that products meet the required measurement accuracy and precision. Further tuning of the algorithm, including test thresholds in the group threshold test, and parameters in tie-point algorithm are needed. More quantitative validation of the products with AMSRE and other available products, including ice chart, and data from LANDSAT are undergoing. Summary