1. Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison Tiejun Chang and Xiangqian Wu GSICS Joint Research and data Working Groups Meeting March, 6, 2012 Beijing

2. Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison  Introduction  Methodology and data processing - Co-registered radiance and collocated radiance - Homogeneous scene selection  AVHRR IR channel calibration - Radiance-based nonlinear correction currently used for L1b radiance: - Count-based quadratic calibration for testing  Calibration radiance error effects - Calibration radiance error effect model - Evaluation of the error  Calibration coefficients error analysis for re-processed radiance - Bias regressive analysis model - Error evaluations - Calibration coefficient correction for quadratic calibration algorithm  Bias analysis and radiance correction for L1b radiance - Regressive analysis of the bias - Radiance correction  Summary 1

3. Introduction AVHRR IASI:  Accurate and stable spectral radiance measurement  High spectral resolution  a good reference for inter-comparison MetOp-A: Launched on October 19, 2006 (images and tables from EUMETSAT MetOp website) AVHRR-IASI collocation measurements over ~5 years, on various Earth scenes, and in different seasons  a good tool for AVHRR bias analysis 2

4. Introduction Motivation:  Increasing need for accuracy in AVHRR radiance products require a precise calibration of the instrument response  AVHRR IR channel bias has been observed  Correction of Earth scene radiance for AVHRR IR channels is needed Focus of this work:  Analytical modeling of the calibration error effect  Regression analysis for calibration error evaluations  Correction for both processed radiance and calibration coefficients  AVHRR IR channel calibration algorithm improvement References: [1] L. Wang, and C. Cao, IEEE Trans. Geosci. Remote Sens., 46, 4005–4013, (2008) [2] J. Mittaz and A. Harris, Journal of Atmospheric And Oceanic Technology, Vol, 28 Issue: 9, 1072-1087 (2011) Earth scene brightness temperature dependent bias for AVHRR IR channels. (from ref. 2) 3

5. Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison  Introduction  Methodology and data processing - Co-registered radiance and collocated radiance - Homogeneous scene selection  AVHRR IR channel calibration - Radiance-based nonlinear correction currently used for L1b radiance: - Count-based quadratic calibration for testing  Calibration radiance error effects - Calibration radiance error effect model - Evaluation of the error  Calibration coefficients error analysis for re-processed radiance - Bias regressive analysis model - Error evaluations - Calibration coefficient correction for quadratic calibration algorithm  Bias analysis and radiance correction for L1b radiance - Regressive analysis of the bias - Radiance correction  Summary

6. Methodology and data processing Collocated L1b radiance and re-processed radiance IASI L1c AVHR R L1b AVHRR radiance AVHRR raw counts & cal. Info. collocated pixels Collocated AVHRR radiance Re-processed collocated AVHRR radiance Test calibration algorithm IASI radiance with AVHRR SRF 5  IASI spectral coverage  Channel 4 and 5  Focus on radiometric calibration issue  Nadir only  One day (1 st day) each month in year 2011 processed.

7. Methodology and data processing Collocated Nadir pixels/Homogeneous scene selection  2 IASI FORs (8 FOVs) at nadir selected  IASI FOV footprint 12x12 km (blue circles)  AVHRR pixel footprint 1.1x1.1 km (red dots)  ~ 130 AVHRR pixels for each IASI FOV.  AVHRR radiance is the average of the enclosed AVHRR pixels  Scene homogeneity becomes a concern Dec., 1 st, 2011 data 6

8. Methodology and data processing Homogeneous scene consideration and selection 10000 1000 Number of data points Earth scene radiance range - Ch 4 - Ch 5  Scene homogeneity impacts regression analysis - random noise level affects analysis accuracy - bias due to a limited data size (such as 1-day) - error due to inhomogeneous + registration error  Homogeneous scene selection considerations  Homogeneous scene selection criterion: After testing, the standard deviation is used as selection reference and the threshold is set to 0.75 % selection criteriontighterlooser number of data pointslessmore earth radiance rangenarrowbroader error and noise levellowerhigher 0.75% (12/01/211 data) 7

9. Methodology and data processing AVHRR-IASI difference on homogeneous scenes Collocated radiance Ch 4 Ch 5 Re-processed radiance Ch 5 Ch 4 Relative radiance error = (AVHRR-IASI)/IASI  Relative radiance error is convenient for calibration error analysis  Radiance unit used  The corrections are derived from analysis of 12 days data (1/month in year 2011)  Dec., 1,2011 data is shown as an example in this talk 8

10. Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison  Introduction  Methodology and data processing - Co-registered radiance and collocated radiance - Homogeneous scene selection  AVHRR IR channel calibration - Radiance-based nonlinear correction currently used for L1b radiance: - Count-based quadratic calibration for testing  Calibration radiance error effects - Calibration radiance error effect model - Evaluation of the error  Calibration coefficients error analysis for re-processed radiance - Bias regressive analysis model - Error evaluations - Calibration coefficient correction for quadratic calibration algorithm  Bias analysis and radiance correction for L1b radiance - Regressive analysis of the bias - Radiance correction  Summary

11. AVHRR IR channel calibration for L1b radiance  The Channel 4 and 5 use Hg-Cd-Te detector operated in photoconductive (PC) mode and exhibit nonlinear responses.  A radiance based nonlinear correction is applied for generating L1b product. Earth scene radiance retrieval Linear radiance estimation Nonlinear correction References: [1] C. Walton, J. Sullivan, C. Rao, and M. Weinreb, J. Geophys. Res., 103, 3323–3337 (1998); [2] J. Sullivan, Int. J. Remote Sensing, Vol 20, No. 18, 3493-3501 (1999) AVHRR IR channel Radiance based nonlinear correction. (from ref. 2) C target: count value when viewing a target (ICT, deep space, or Earth scene) b 0, b 1,b 2, and R earth: calibration coefficients from pre-launch characterization. 10

12. Alternative nonlinear correction algorithm  The count based quadratic nonlinear correction algorithms are used for some IR channel radiometric calibration  The instrument response drifts with instrument degradation and instrument temperature fluctuations are considered, for example linear gain is calculated scan by scan [1]. References: [1] X. Xiong, K. Chiang, J. Esposito, B. Guenther, and W.L. Barnes, “MODIS On-orbit Calibration and Characterization,” Metrologia, vol. 40, pp. 89-92, 2003 C target : count value when viewing a target (ICT, deep space, or Earth scene) a 0 and a 2 : calibration coefficients (offset, linear gain, nonlinear coefficient) from pre-launch characterization.  Direct count-radiance conversion & 2 independent coefficients  simple calibration  easier analysis of error effect on the radiance a1a1 11

13. Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison  Introduction  Methodology and data processing - Co-registered radiance and collocated radiance - Homogeneous scene selection  AVHRR IR channel calibration - Radiance-based nonlinear correction currently used for L1b radiance: - Count-based quadratic calibration for testing  Calibration radiance error effects - Calibration radiance error effect model - Evaluation of the error  Calibration coefficients error analysis for re-processed radiance - Bias regressive analysis model - Error evaluations - Calibration coefficient correction for quadratic calibration algorithm  Bias analysis and radiance correction for L1b radiance - Regressive analysis of the bias - Radiance correction  Summary 12

14. Calibration radiance error analysis f EV  : emissivity B(T ): BB radiance at temperature T f inst and f EV : configuration factors (portion of ICT hemisphere covered)  On-board calibration radiance: f inst f space f inst +f EV + f space =1  Calibration radiance used : instrument dependent (Constant) ICT temperature & error dependentEarth & ICT radiance dependent  On-board calibration radiance error: Effective emissivity error 13

15. Calibration error effect analysis Error in Earth radiance retrieval - perturbations in coefficients - 1 st order effect only  Count based quadratic algorithm A B C B A C 14

16. Step 1: Evaluation of calibration radiance error Analytical model Calibration radiance error Select Earth scenes with BT close to ICT BT 15

17. Step 2: Regression for calibration coefficient error Errors in offset and nonlinearity Calibration radiance error From step 1 Analytical model Remove calibration radiance error effect 16 Regression analysis

18. Evaluation of calibration radiance error  Using re-processed radiance with count based quadratic algorithm Ch 4 12/01/2011 one-day data Non-negligible calibration error (ideally should be 0. However 0.0011 (ch4) and 0.00056 (ch5) on average)  L1b radiance with radiance based nonlinear correction algorithm 17

19. Evaluation of calibration radiance error. Channel 4Channel 5 Number of data points Collocated radiance (Re-processed) Number of data points Collocated radiance (Re-processed) January3440.002960.003004800.003360.00337 February540.002800.00282850.003200.00323 March4290.002890.002955350.003370.00343 April5070.003020.003076530.003370.00343 May5630.002950.003009000.003390.00345 June560.003120.00319800.003080.00311 July680.002610.002729510.002890.00300 August1070.002990.003061080.003420.00351 September7530.003090.0031211500.003400.00343 October8280.003060.0031110990.003350.00336 November6610.002740.002829380.003130.00316 December8140.002830.0029010730.003290.00338 Weighted average 0.002940.003000.003270.00333 Standard deviation 0.000130.000110.000170.00015 One-day (1 st day) data in each month analyzed The value in the table is the relative radiance error (effective emissivity error) 18

20. Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison  Introduction  Methodology and data processing - Co-registered radiance and collocated radiance - Homogeneous scene selection  AVHRR IR channel calibration - Radiance-based nonlinear correction currently used for L1b radiance: - Count-based quadratic calibration for testing  Calibration radiance error effects - Calibration radiance error effect model - Evaluation of the error  Calibration coefficients error analysis for re-processed radiance - Bias regressive analysis model - Error evaluations - Calibration coefficient correction for quadratic calibration algorithm  Bias analysis and radiance correction for L1b radiance - Regressive analysis of the bias - Radiance correction  Summary 19

21. Step 2: Regression for calibration coefficient error Calibration radiance error 0.297% for Ch4 0.330% for Ch5 Analytical model Remove calibration radiance error effect 20 Errors in offset and nonlinearity Regression analysis for

22. Weighting function in regression analysis Regression model Ch 4 A B C B A C 21

23. Weighting function in regression analysis = number of data points in unit radiance range centered Weighting function for least-square fit 22

24. Weighting function in regression analysis = number of data points in unit radiance range centered Weighting function for least-square fit Regression model --- Weight applied --- Weight not applied 23

25. Regressive analysis for re-processed radiance Correction of calibration coefficients Bias regression model Ch 4 Ch 5 Corrected Channel 40.972-0.3531.3251.84x10 -5 0.17x10 -5 1.67x10 -5 Channel 50.636-0.5321.1681.18x10 -5 0.08x10 -5 1.10x10 -5 Calibration coefficient improvement 24

26. Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison  Introduction  Methodology and data processing - Co-registered radiance and collocated radiance - Homogeneous scene selection  AVHRR IR channel calibration - Radiance-based nonlinear correction currently used for L1b radiance: - Count-based quadratic calibration for testing  Calibration radiance error effects - Calibration radiance error effect model - Evaluation of the error  Calibration coefficients error analysis for re-processed radiance - Bias regressive analysis model - Error evaluations - Calibration coefficient correction for quadratic calibration algorithm  Bias analysis and radiance correction for L1b radiance - Regressive analysis of the bias - Radiance correction  Summary

27. Bias regressive model for L1b radiance Bias regression model Collocated radiance (L1b) Re-processed radiance  It is difficult to derive a model for bias regressive analysis  Similar error effect  Use the model for count-based quadratic calibration Ch 4 Re-processed Ch 4 L1b 26

28. Regressive analysis and radiance correction algorithm for L1b radiance Radiance correction for L1b radiance Bias regression model Ch 4 Ch 5 27

29. Radiance correction for L1b radiance Ch 4 Ch 5 before after before after Radiance correction for L1b radiance 28

30. Radiance correction for L1b radiance Ch 4 Ch 5 before after Gaussian fit Center =0.0056% Width =0.089% Center =0.0042% Width =0.073% 29  Gaussian error distribution  remaining error is mostly random noise indicated by the width  Center close to 0  AVHRR bias has been mostly removed  The Gaussian coefficients  uncertainty of the correction

31. Summary Regression analysis and radiance correction Analytical model Bias form AVHRR-IASI Comparison Calibration coefficient correction Radiance correction Two-step regressions Weight function Error effects

32. Summary  Inter-comparison of AVHRR and IASI for both L1b radiance and re-processed radiance using a test calibration algorithm.  Analytical study for calibration error effect for both calibration algorithms.  Two-step regression analysis for evaluation of the calibration error.  Radiance correction for L1b radiance.  Calibration coefficient correction for the test calibration algorithm.  Demonstration of the importance of using analytical model to investigate inter- comparison results.  Development of a tool for test AVHRR calibration algorithm and calibration improvement Submitted to IEEE TGARS inter-comparison special issue.