1. 1 GOES-R AWG Land Surface Emissivity Team: Retrieval of Land Surface Emissivity using Time Continuity June 15, 2011 Presented By: Zhenglong Li, CIMSS Jun Li, CIMSS Timothy J. Schmit, NOAA/NESDIS/STAR

2. 2 Outline  Executive Summary (1 slide)  Algorithm Description (3 slides)  ADEB and IV&V Response Summary (3 slides)  Requirements Specification Evolution (2 slides)  Validation Strategy (1 slide)  Validation Results (4 slides)  Summary (1 slide)

3. 3 Executive Summary  This ABI Land Surface Emissivity (LSE) retrieval algorithm generates the Option 2 product of LSE  Version 4 was delivered in January. Version 5 and ATBD (100%) are scheduled to be delivered in June.  A physical approach that uniquely takes advantage of time continuity of geostationary satellite, has been developed to generate ABI spectral LSE in window channels; the LSE is assumed to be invariable during a short period of time while LST is variable.  Sensitivity study: the algorithm has been applied to the simulated SEVIRI measurements to test the LSE retrieval sensitivity on the first guess, the local zenith angle (LZA), the radiance bias and instrument noise.  Validation: the LSE retrieval from the SEVIRI measurements are compared with other LSE products such as MODIS, AIRS and IASI.

4. 4 Algorithm Description

5. 5 Algorithm Summary  Time continuity: LSE is temporally invariable while LST is variable during a short period of time  Inverse scheme: The quasi-nonlinear iteration  Three IR window channels (8.7, 10.8 and 12 micron) for SEVIRI, and four IR winddow channels (8.5, 10.35, 11.2 and 12.3 micron) for ABI  Three time steps with time difference of 3 hours between two consecutive time steps (e.g., 00 UTC, 03 UTC and 06 UTC)

6. 6 The Linearization of the Radiative Transfer Equation yields: This equation needs to be simplified for accurate and stable LSE retrievals The atmospheric correction is achieved by using NWP forecast profiles plus Greatly simplifies the linearization equation by reducing the unknowns of T/Q profiles to just one hybrid variable of The simplified linearized RT Equation: where Linearization of RT Equation

7. 7 Time continuity of LSE: the LSE is assumed to be invariable during a short period of time while LST is variable. On SEVIRI, there are three window channels (8.7, 10.8 and 12 micron), and three time steps are recommended. The linearized RT equation looks: or the number in superscript denotes time steps, and the number in subscript denotes channel index Time continuity

8. 8 A general form of the variational solution for LSE is to minimize the cost function: Where X: state vector Y m : observed brightness temperature vector Y: radiative transfer model (function of X) E: observation error covariance matrix (diagonal) X 0 : first guess (initial state) of LSE H: the inverse of the first guess error covariance matrix Apply Newtonian iteration The quasi-nonlinear iterative form of the solution is The optimal solution

9. 9 ADEB and IV&V Response Summary  ADEB: Land Surface Emissivity -- OK for advance to 100%. The board commends the team for its use of temporal resolution of ABI. The board recommends further validation including alternative sources of data and comparison against SeeBor and MODIS emissivities (already done).  Recommend #1 -- 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: Surface emissivity will not only be compared to MODIS and SeeBor, but other datasets, such as from AIRS/IASI/CrIS (when available). Again, there is the 'direct' comparison to other LSE products and also the 'indirect' method of using the LSE in the forward calculations to then compare to observations. Another option to indirectly validate LSE is to investigate its impacts on sounding retrievals. One unique method is to use satellite radiance measurements to objectively quantify LSE precision. More information about this method can be found in Li, Zhenglong; Li, Jun; Jin, Xin; Schmit, Timothy J.; Borbas, Eva E. and Goldberg, Mitchell D. An objective methodology for infrared land surface emissivity evaluation. Journal of Geophysical Research, Volume 115, 2010, Doi:10.1029/2010JD014249.

10. 10 ADEB and IV&V Response Summary-cont  Recommend #3 -- In most cases the requirements are conservative. Most of the products, especially the legacy ones, have requirements that do not extend the capabilities. Response: As far as a 'stretch' goal for LSE, there is a limit to what can be done with low spectral resolution data. That said, the LSE current requirement is ONLY for the CONUS region. At some point, this should be extended to cover the Full Disk regions (within the LZA cut-off). This will help other products, such as OLR and LST.  Recommend #4 -- The many advantages of geostationary orbit are rarely exploited. Response: As mentioned by the ADEB, the LSE already uses the temporal resolution. There may be the other product, other that winds, that uses the temporal component. The implementation of time continuity in LSE algorithm improves LSE products quality.

11. 11 ADEB and IV&V Response Summary-cont  Recommend #5 -- The board recommends this issue be addressed thoroughly before launch. The IPR team and the ADEB were asked to consider graceful degradation, however the teams rarely discussed it and then only with regard to ancillary data. Impacts due to sensor/channel degradation or loss were not addressed, and there was no quantitative assessment of impact to product quality as input data degrade. Response: wrt graceful degradation, how much is needed to be done by the teams, as opposed to the AIT running more 'system' level tests? For LSE products, no discussion is made WRT graceful degradation. However, the discussion of radiance biases (which could comes from cloud/dust contamination, forward calculation or instrument degradation) should apply here. Our study indicates the LSE products are sensitive to radiance biases in a complex way. Therefore, radiance bias adjustment is needed if there is any.  Recommend #6 – plans to reach 100% should be clearly stated. Response: Compared with 80 %, the 100 % will include more quality control, which makes it easier for users to understand the quality of the products.

12. 12 Requirements Surface Emissivity M – Mesoscale FD – Full Disk NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementsRangeMeasurementsAccuracyR efresh Rate/Coverage TimeOption (Mode 3)Refresh Rate Option(Mode 4)Vendor AllocatedGroundLatency (Mode 3)Vendor AllocatedGround Latency(Mode 4)ProductMeasurementPrecision Surface Emissivity GOES-RCN/A10 km5 km0.6 to 1.0 (unitless) 0.05 (unitless) 60 min 3236 sec 0.005* *Pending to relax to 0.05, due to lack of HES.

13. 13 Surface Emissivity Product Qualifiers FD – CONUS M - Mesoscale NameUser &PriorityGeographicCoverage(G, H, C, M)TemporalCoverageQualifiersProductExtentQualifierCloud CoverConditionsQualifierProductStatisticsQualifier Surface EmissivityGOES-RCSun at less than 67 degree daytime solar zenith angle * Quantitative out to at least 70 degrees LZA and qualitative at larger LZA ** Clear conditions associated with threshold accuracy Over specified geographic area * This IR technique is independent of the solar illumination. ** Quantitative out to at least 67 degrees LZA and qualitative at larger LZA (out to soundings limit).

14. 14 Validation Strategy

15. 15 Validation Approach  Direct validation of LSE retrievals using laboratory measurements are difficult due to »Measurement scale inconsistency (point versus area); very few areas are homogeneous enough for this comparison. »Naturally temporal variation of LSE »Expensive  Practical way to evaluate LSE retrievals include »Simulation study: validate the LSE retrieval algorithm in simplified conditions »Inter-comparison: compare with other LSE databases, such as MODIS, SeeBor, AIRS and IASI »Indirect: apply the LSE database to see if they draw positive impacts, i.e. on sounding retrievals and radiative transfer calculations as compared to observations »Indirect: apply the objective method to quantitatively evaluate LSE precisions using satellite observations »Indirect: apply the algorithm to GOES Sounder over ocean to see if the retrieved LSE and LST are temporally consistent

16. 16 Validation Results

17. 17 The retrievals with LZA of 0 degree are compared with the true Validation of the retrievals using simulated SEVIRI radiances The algorithm successfully brings the retrieval parameters closer to the true values, especially for LSE of 8.7 micrometer and LST.

18. 18  The algorithm is applied to SEVIRI observations on August 1 2006. The retrievals are compared with MODIS (collection 4.1), AIRS and IASI monthly LSE databases. Inter-comparison: SEVIRI LSE products---- 8.7 um 8.7 um

19. 19  Using MODIS monthly LSE database as reference, other three databases are quantitatively evaluated Inter-comparison: Compared with MODIS ---- 8.7 um 8.7 um Using MODIS as a reference, SEVIRI > IASI > operational AIRS for 8.7 µm in this particular case

20. 20  The retrieved SSE/SST from GOES Sounder (coarser spatial resolution) with GOES-R LSE algorithm is temporally consistent, although there seems to be cloud contamination. Indirect: Apply to GOES Sounder April 29, 2010 12.66 um SSE 11.03 um SSE 12.02 um SSE SST

21. 21 Summary  The ABI land surface emissivity algorithm provides LSE products that uniquely utilizes the time continuity offered by the ABI  Version 4 is delivered, version 5 and the 100% ATBD is coming.  These products are being validated, and will be useful for other products.  The validation strategy includes objective method, direct comparison and indirect evaluation

22. References  Li, J., Z. Li, X. Jin, T. Schmit, L. Zhou, and M. Goldberg, 2011: Land surface emissivity from high temporal resolution geostationary infrared imager radiances: Methodology and simulation studies, Journal of Geophysical Research - Atmospheres, 116, D01304, doi:10.1029/2010JD014637.  Li, Z., J. Li, X. Jin, T. J. Schmit, E. Borbas, and M. D. Goldberg, 2010: An objective methodology for infrared land surface emissivity evaluation, Journal of Geophysical Research - Atmospheres, 115, D22308, doi:10.1029/2010JD014249. 22