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2011 GOES-R AWG Annual Meeting, June 13-16, Fort Collins, CO

ABI Ice Thickness and Age Algorithm (AITA) June 15, 2011 Presented by: Xuanji Wang 1 Other team members: Jeff Key 2 , Yinghui Liu 1 1 UW-Madison/CIMSS 2 NOAA/NESDIS/STAR. GOES-R AWG Cryosphere Team. 1. 2011 GOES-R AWG Annual Meeting, June 13-16, Fort Collins, CO. Outline.

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2011 GOES-R AWG Annual Meeting, June 13-16, Fort Collins, CO

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  1. ABI Ice Thickness and Age Algorithm (AITA) June 15, 2011Presented by: Xuanji Wang1Other team members: Jeff Key2, Yinghui Liu11UW-Madison/CIMSS2NOAA/NESDIS/STAR GOES-R AWG Cryosphere Team 1 2011 GOES-R AWG Annual Meeting, June 13-16, Fort Collins, CO

  2. Outline • Executive Summary • Algorithm Description • Algorithm changes from 80% to 100% • Example AITA output • ADEB and IV&V Responses Summary • Requirements Specification Evolution • Validation Strategy • Validation Results • Summary

  3. Executive Summary • The GOES-R Sea and Lake Ice Thickness and Age is an Option 2 product. • Software Version 5 was delivered in May 2011. ATBD (100%) is on track for a August delivery. • The algorithm has been developed, tested, and validated using AVHRR, MODIS, SEVIRI data and in situ observed data from submarine, mooring sites, and stations as well as numerical model simulations. • Validation studies indicate spec compliance for the product. 3

  4. One-dimensional Thermodynamic Ice Model (OTIM) Based on the thermodynamic energy balance thoery Designed for easy portability and adaptation Solid physics and computationally economic For both day and night For both clear and cloudy sky Journal article published. (Wang, X., J. R. Key, and Y. Liu, 2010, A thermodynamic model for estimating sea and lake ice thickness with optical satellite data, J. Geophys. Res.-Oceans, doi:10.1029/2009JC005857) Algorithm DescriptionOTIM – Physics and Applicability 4

  5. One-dimensional Thermodynamic Ice Model (OTIM) Based on the surface energy budget at thermo-equilibrium state, the fundamental equation is (1-αs)(1-i0)Fr – Flup + Fldn + Fs + Fe + Fc = Fa(αs, Ts, U, hi, C, hs, …) After parameterizations of thermal radiation (Fr, Flup,Fldn) and turbulent (sensible & latent) heat (Fs, Fe), ice thickness hibecomes a function of11 model controlling variables: hi = f(αs, i0, Sz, Ts, Ti, Ta, Pa, hw, U, C, hs, Fa) Cloud C U Ta αsFr Fr(Sz) Pa Fa Flup hw Fldn Fc Fe Fs Ts Snow layer hs T0 Fcs = Fci Ice layer Z Ti (1-αs)(1-i0)Fr hi Tf i0(1-αs)Fr Algorithm DescriptionOTIM – Structure and Components

  6. OTIM and Ice age classification begin ABI Channel Used: Radiances and retrieved products, e.g., cloud mask. Ancillary Data: NWP/NCEP/ECMWF, etc. Products Generated: Ice thickness, ice age, and their associated quality flags. Surface air temperature, pressure, wind, and moisture Cloud mask/amount, ice and snow physical properties, snow cover/depth, sea/lake mask For each pixel Surface albedo All-sky sea/lake ice thickness Surface Temperature Sea/lake ice age classification Surface radiative fluxes All-sky sea/lake ice age Surface heat fluxes OTIM and Ice age Classification end One-dimensional Thermodynamic Ice Model - OTIM Algorithm DescriptionOTIM - High Level Flowchart 6

  7. Algorithm DescriptionOTIM - Ice Age Classification • Ice Age Categories: • Ice free: Directly from ice identification/concentration algorithm when ice concentration is less than 15%. • First-year ice: Ice thickness < 1.80m. First-year ice includes New Ice (<5cm), Nilas Ice (5~10cm), Grey Ice (10~15cm), Grey-white Ice (15~30cm), Thin First-year Ice (30~70cm), Medium First-year Ice (70~120cm), Thick First-year Ice (120~180cm). • Older ice: Ice thickness >= 1.80m.

  8. Algorithm Changes from 80% to 100% • A new lookup table for estimating residual heat flux is added in the version 5 code. • Two independent subroutines have been added to the version 5 code for the model variable initialization and metadata analysis in order to avoid machine type issue. • Metadata output added. • Quality flag added. 8

  9. Example AITA Output NASA MODIS Data: Great Lakes Ice Age Classification: 1: Free of ice (white) 2: New ice 3: Grey ice 4: Grey-white ice 5: Thin first-year ice 6: Median first-year ice 7: Thick first-year ice 8: Old ice Ice Thickness (m) over Great Lakes area, February 24, 2008. Ice Age derived from Ice Thickness over Great Lakes area, February 24, 2008. Clear sky condition 9 9

  10. Example AITA Output MSG SEVIRI Data: Caspian Sea Ice Age Classification: 1: Free of ice (white) 2: New ice 3: Grey ice 4: Grey-white ice 5: Thin first-year ice 6: Median first-year ice 7: Thick first-year ice 8: Old ice Ice Thickness (m) over Caspian Sea, February 24, 2008. Ice Age derived from Ice Thickness over Caspian Sea, February 24, 2008. Clear sky condition 10 10

  11. ADEB and IV&V Response Summary • 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. Critical Recommendations and Responses • Recommendations: Continue to pursue more complete data sets. Measurement validation must be conducted with thoroughness and completeness within a sustained validation/verification framework and should consider using “human in the loop.” This recommendation was made previously but has rarely been adopted. Response: Ice age/thickness “deep-dive” validation uses all types of available data: in situ measurements, submarine and moored sonar, numerical model simulations, and estimates from other sensors such as ICESat. We believe that there is little more that can be done for this products. We also have plans to use data from current and upcoming field campaigns, such as NASA’s IceBridge flights.

  12. ADEB and IV&V Response Summary Critical Recommendations and Responses • Recommendations: Recommend teams continue work on “stretch goals” that approach the state-of-the-art. Response: The current requirements are based on the original Threshold values rather than the Goal. Nevertheless, we have tightened the Threshold requirements somewhat, and continue to strive for meeting the Goal requirements. For example, our Sea and Lake Ice Age product far exceeds the Threshold requirement of two ice classes by providing an ice thickness estimate. The thickness estimate can be used to classify ice into the Goal categories, even though it is not a current requirement to do so. • Recommendations: plans to reach 100% should be clearly stated. Response: Tasks for moving ice thickness/age products from 80% to 100% maturity include: improving the daytime ice age algorithm with an approach that parameterizes the residual heat fluxes, improving the parameterized relationship between ice thickness and ice age, performing additional deep-dive validation, adding metadata to the products, and updating the ATBDs. • Recommendations: OK for advance to 100%. The board also recommends further validation including in-situ sonar and RadarSat. Team should consider future implementation that uses GOES-R produced fluxes vs. independently calculating those fluxes within this algorithm. Response: Additional sonar data has been acquired and used for validation. RADARSAT data are being investigated. If GOES-R flux products are of sufficiently high quality, and we expect that they will be, they will be used in the ice age algorithm.

  13. Requirements Specification Evolution

  14. Requirements Specification Evolution

  15. Validation ‘Truth’ Data Station ice thickness measurements since 2002 from Canadian Ice Service (CIS). Submarine cruise measurements of ice draft data using Upward Looking Sonar (ULS) from National Snow and Ice Data Center (NSIDC) and Unified Sea Ice Thickness climate Data Record (USITCDR) at UW-Seattle. Moored Upward Looking Sonar (ULS) measured ice draft/thickness data from NSIDC and Beaufort Gyre Exploration Program (BGEP). Numerical model simulatedice thickness from a Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) at UW-Seattle. Ice thickness from past, current, and future field campaigns from RADARSAT-1, ICESat, and IceBridge. Ice Age derived from SMMR and SSM/I passive microwavedata with NASA team algorithm since 1978. Validation Strategy 15

  16. Validation Test Data Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP) Extended (APP-x) Data over the Arctic and Great Lakes. MODIS data over the Arctic and the Great Lakes. Meteosat SEVIRI data over the Caspian Sea. Validation Strategy 16

  17. Validation Method Direct match-up and comparison in ice thickness between OTIM retrievals and ‘truth’ data. Compare Ice Age derived from Ice Thickness (this algorithm) with independent Ice Age estimation from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data (NASA teamalgorithm) Validation Performance Metrics Cumulative frequency, bias mean, and absolute bias mean, accuracy, precision. Validation Strategy 17

  18. Ice thickness values retrieved by OTIM with APP-x data, submarine sonar data, and simulated thickness from the PIOMAS model along the submarine track segments. Submarine ice draft (mean and median only) was converted to ice thickness by a factor of 1.11. Submarine trajectory, 1999 Ice thickness cumulative distribution retrieved by OTIM with APP-x data, submarine sonar data, and simulated thickness from the PIOMAS model. Submarine ice draft (mean and median only) was converted to ice thickness by a factor of 1.11. Validation Results Comparison of AVHRR Ice Thickness with submarine ULS measurements and numerical model simulations 18

  19. Validation Results Comparison of AVHRR Ice Thickness with station measurements and numerical model simulations Comparisons of ice thickness cumulative distribution retrieved by OTIM with APP-x data, simulated thickness from the PIOMAS model and station measurements at Alert. Comparisons of ice thickness values retrieved by OTIM with APP-x data, measurements at Alert, and simulated thickness from the PIOMAS model. Station location map. Totally 11 Canadian stations participate the New Arctic Program starting from 2002 for ice thickness measurements. 19

  20. Validation Results Comparison of AVHRR Ice Thickness with Moored ULS measurements and numerical model simulations Comparisons of ice thickness values retrieved by OTIM with APP-x data, simulated thickness from the PIOMAS model and ULS measurements at the mooring site A. The location information of the three mooring sites during the Beaufort Gyre Exploration Project starting from 2003. Comparisons of ice thickness cumulative distribution retrieved by OTIM with APP-x data, simulated thickness from the PIOMAS model, and ULS measurements at the mooring site A. 20

  21. Validation Results Comparison of OTIM derived Ice Thickness with Submarine and Moored ULS measurements, and station measurements 21

  22. OTIM Ice Age from MODIS data vs Microwave (NASA) Ice Age MODIS TERRA & AQUA (2412 swaths, Day & Night, March, 2006) Cloud Contamination (Big Issue) Composite Ice Thickness SMMR and SSM/I vs AITA (2006) Validation Results 0:Ice free 1: First-year ice 2: Older ice 22

  23. Validation Results OTIM Ice Age with MODIS data vs Microwave (NASA) Ice Age D=day; N=night 23

  24. Validation Results OTIM Ice Age with MODIS data vs microwave (NASA) Ice Age 24 24 D=day; N=night

  25. The ABI Ice Thickness and Age Algorithm provides a unique solution that utilizes the new capabilities offered by the ABI and its derived products. ABI allows us to monitor ice conditions at high temporal and spatial resolution. The Sea & Lake Ice Age Product has been run offline and within the framework and the results are exactly the same. This product meets the specifications and is proving useful to downstream applications. The Sea & Lake Ice Age Product uses MODIS and AVHRR data as proxy and is validated against Upward Looking Sonar (ULS) measurements from submarine and mooring sites, stations observations, and passive microwave data to demonstrate that the Ice Age algorithm meets product requirements of 80% correct accuracy and less than one category precision. Summary 25

  26. Version 5 100% code is delivered and the 100% ATBD is coming soon. This algorithm will be further modified to improve the accuracy. Additional validation data will be employed once they become available. Summary 26

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