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Presented by : Shunlin Liang Dongdong Wang, Tao He University of Maryland, College Park

GOES-R AWG Land Team: ABI Land Surface Albedo (LSA) and Surface Reflectance Algorithm June 14, 2011. Presented by : Shunlin Liang Dongdong Wang, Tao He University of Maryland, College Park Contributors: Bob Yu 1 , Hui Xu 2 1 NOAA/NESDIS/STAR 2 IMSG. Outline. Executive Summary

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Presented by : Shunlin Liang Dongdong Wang, Tao He University of Maryland, College Park

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  1. GOES-R AWG Land Team: ABI Land Surface Albedo (LSA) and Surface Reflectance AlgorithmJune 14, 2011 Presented by: Shunlin Liang Dongdong Wang, Tao He University of Maryland, College Park Contributors: Bob Yu1, Hui Xu2 1NOAA/NESDIS/STAR 2IMSG

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

  3. Executive Summary • This ABI Land Surface Albedo Algorithm (LSA) generates the Option 2 products of surface albedo and surface reflectance. • The Version 4 code and 80% ATBD have been delivered. • The delivery of Version 5 code and 100% ATBD are on track. • An improved optimization algorithm was developed to estimate the surface properties and atmospheric condition simultaneously. • The algorithm of direct estimation of albedo was added as back-up to handle cases where the routine algorithm fails. • Background information of albedo climatology and gap-filling technology are used to reduce the frequency of missing and filling values. • Both albedo and reflectance products have been validated. • Validation shows the LSA algorithm can satisfy the requirements.

  4. Algorithm Description

  5. Algorithm Summary • This ABI LSA Algorithm generates the Option 2 products of Surface Albedo and Surface Reflectance. • Combination of atmospheric correction and surface BRDF modeling, producing both surface directional reflectance and shortwave albedo simultaneously . • The unique advantages of GOES-R ABI(multi-channel and frequent refreshing rate) are fully utilized in the cooperative retrieval of surface BRDF and atmospheric parameters. • The operational codes are run in two modes. The time-consuming process of retrieving BRDF is handled in offline mode. • Back-up algorithm is designed to handle rapidly-changing surfaces. • The albedo climatology is incorporated as background information effectively in the inversion algorithms. • Use of background information and gap-filling technique greatly reduces the frequency of data gaps.

  6. Introduction to the Offline Mode • The calculation of albedo needs a long time series. To improve efficiency, an “offline” computation is carried out at the end of each day to derive BRDF parameters. These parameters will be stored to calculate albedo and surface reflectance for the next day. • The online mode is run in real-time to generate instantaneous full-disk albedo and surface reflectance products. • The offline and online modes share similar mathematical and physical theories and have slightly different input/output

  7. Flowchart of offline Mode

  8. Flowchart of online mode

  9. Back-up Algorithm • A back-up algorithm, which directly estimates albedo from TOA radiances through extensive radiative transfer simulation, has been developed to handle the failure of the LSA routine algorithm. The two figures show the error budgets (Left: R2, Right: RMSE) of the LSA back-up algorithm under various view and relative azimuth angles. The slightly reduced accuracy at hot-spot is evident due to the larger uncertainties in that geometry.

  10. Examples of Albedo Algorithm Output Retrieved broadband blacksky albedo map using MODIS as the proxy data May 1st, 2005 Around 48˚N, 102˚W 10

  11. Algorithm Changes from 80% to 100% • Back-up algorithm was added. • Gap-filling technique was added to reduce missing values • Metadata output added. • Quality flags standardized.

  12. ADEB and IV&V Response Summary • All ADEB comments and recommendations have been addressed. • We have paid special attention to the scaling problems of validating albedo and submitted a GOES-R val/cal proposal to further address this issue. • With regard to the problem that “graceful degradation was rarely addressed”, we proposed the combination of back-up algorithm and gap-filling technique to handle the failure of the LSA routine algorithm.

  13. Requirements and Product Qualifiers Surface Albedo and Surface Reflectance 13

  14. Proposal to change some requirement parameters 14

  15. Validation Approach and Results 15

  16. Validation Approach • Albedo Validation • Direct comparison of retrieved albedo with field measurement. • Proxy data: MODIS, SEVIRI • Surface types: “bright”, ”dark” surfaces • Intercomparison with existing albedo products • MODIS albedo product: 16 days composite • Surface Reflectance Validation • Direct validation • No routine field measurements of surface reflectance • Calculate “true” surface reflectance from TOA reflectance using in situ measurements of atmospheric parameters • Intercomparison with other products 16

  17. Validation Approach: datasets • Satellite proxy data • MODIS: multi-channels, but polar orbiting • SEVIRI: geostationary, but limited channels (three) • In situ measurement of surface albedo • AmeriFlux • SURFRAD • GC-Net • IMECC • Surrogate of surface reflectance data • ASRVN: AERONET-based Surface Reflectance Validation Network 17

  18. Validation Results: Vegetated Surface Example of time series shortwave albedo from MODIS observations in 2005 over six SURFRAD sites Example of time series total visible albedo from MODIS observations in 2005 over four AmeriFlux sites

  19. Validation Results: Snow Surface • Greenland sites (GC-Net) • 2003 • Comparison with MODIS albedo products and ground measurements

  20. . Validation Results: Surface Reflectance Example of time series instantaneous reflectance from MODIS observations in 2005 over AERONET sites Comparison of estimated and MODASRVN instantaneous bidirectional reflectance for MODIS band1&2 over 16 AERONET sites during 2005

  21. Summary of Validation Results 21

  22. Next Steps to Reach 100% Refine the subroutine (back-up algorithm) to handle snow/non-snow transitional periods and other cases of main algorithm failure; Continue to improve the algorithms in terms of accuracy and computational efficiency; Carefully design strategy of graceful degradation; Conduct more validation activities to further evaluate the algorithm and quantify the accuracy and precision of the data; Revise the code by following the coding regulation. 22

  23. Summary The ABI LSA algorithm takes full advantage of the ABI unique new capabilities of frequent multichannel observations. This algorithm works on both bright and dark surfaces. The algorithm of direct estimation of albedo was incoporated as back-up to handle cases where the routine algorithm fails. Background information of albedo climatology and gap-filling technology are used to reduce the frequency of missing and filling values. Validation shows the LSA algorithm can satisfy the requirements. The delivery of Version 5 code and 100% ATBD are on track. 23

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