1. Inter-calibration of Operational IR Sounders using CLARREO Bob Holz, Dave Tobin, Fred Nagle, Bob Knuteson, Fred Best, Hank Revercomb Space Science and Engineering Center, University of Wisconsin-Madison 2009 CLARREO Workshop Langley, VA May 11-15, 2009

2. Question: Given a CLARREO mission optimized for producing the climate benchmark products (e.g. consistent with the DS mission consisting of three 90 degree polar orbits, 100 km footprints, 0.1K radiometric accuracy, broad and continuous spectral coverage at high spectral resolution), how well can we meet the secondary objective of CLARREO to serve as an inter-calibration reference for the operational IR sounders ? Answer: Yes, the CLARREO calibration can be transferred to operational IR sounders with an uncertainty comparable to the CLARREO radiometric accuracy.

3. Approach: A simulation study using real MODIS data. Same basic approach as presented at the first CLARREO workshop, the CLARREO science team meeting, and recent AGU and SPIE meetings. Find Simultaneous Nadir Overpasses (SNOs) of CLARREO and EOS Aqua, and for each SNO use MODIS radiances to estimate the spatial and temporal sampling differences between CLARREO and CrIS/AIRS or IASI. Opposed to actual inter-comparisons studies involving two sensors, this approach removes the unknown sensor biases and allows spatial and temporal inter- calibration differences to be examined.

4. CLARREO/Aqua SNOs in 2006 Three 90-degree CLARREO orbits with right ascension separated by 120 degrees are “launched” on January 1st, and the SNOs for CLARREO and EOS Aqua are identified for the year of 2006.

5. Estimating brightness temperature differences due to spatial and temporal sampling differences Spatial: Compute mean of 1km MODIS observations within the CLARREO footprints and also within the CrIS/AIRS or IASI footprints. (Also record the standard deviation of the MODIS observations within the CLARREO footprints.) Temporal: simple Lagrangian approach; compute the mean of MODIS observations within a displaced CLARREO footprint using known CLARREO/Aqua time difference and 13 m/s wind.

6. Spatial Sampling Differences CrISIASI 100 km CLARREO 100km ~55% coverage~18% coverage 11  m MODIS BT (K)

7. Time Sampling Differences 11  m MODIS BT (K) Displaced FOV

8. We use all sky cases for inter-calibration The approach presented is consistent with the GSICS ATBD (in development), and as has been practiced for Geo/Geo and Geo/Leo intercal for many years. GSICS ATBD Uses All Sky Inter-calibration

9. Uncertainty in Monthly Means MODIS Band 31@11  m; 100km CLARREO FOVs every 14s; CrIS/AIRS Uncertainty including Space/Time errors only Uncertainty including Space/Time errors and CLARREO (1K NEDT) and Sounder single channel Noise Mean # points per month Conclusions: - CLARREO will be capable of inter-calibration uncertainties of less then 0.1 K 3σ - Inter-calibration uncertainties are improved when non-uniform FOV are included

10. Threshold method and Weighted means MODIS Band 31@11  m; 100km CLARREO FOVs every 14s; CrIS/AIRS 168 pts464 696 866 976 1163 Conclusion: A CLARREO single channel noise of less then 1.0 K will allow for a monthly inter-calibration accuracy of 0.1 3σ

11. FOV Size Dependence on Monthly Uncertainties 100 km, 50 km, and 25 km diameter footprints MODIS Band 31@11  m ; CrIS/AIRS 100km (14s), 1276 pts, STDEV = 0.214 K 50 km (8s), 2286 pts, STDEV = 0.460 K 25 km (4s), 4474 pts, STDEV = 0.918 K Solid = threshold method, BTSTD ≤ 10K Dashed = uncertainty in weighted mean Conclusion: The inter-calibration goal of 0.1 3σ can be accomplished for a range of CLARREO FOV sizes including 100 km.

12. “ Nonlinearity Curve” from Annual data MODIS Band 31@11  m; 100km CLARREO FOVs every 14s; CrIS/AIRS 10K bin Uncertainties Blue = space/time only Cyan = plus CLARREO (1K NEDT) and Sounder Noise Yellow = plus CLARREO (0.5K NEDT) and Sounder Noise Red = CLARREO (1K NEDT) and Sounder Noise, averaging 4 adjacent spectral channels Conclusion: Instrument nonlinearities can be investigated using yearly averages for single channels or monthly using spectral averaging.

13. Aircraft (S-HIS) Sounder (AIRS/IASI/CrIS) CLARREO Aircraft Inter-Calibration 0.5 3σ0.1 3σ Conclusion: Aircraft to Sounder inter-calibration is the limiting link in evaluating CLARREO using Aircraft This uncertainty limits the evaluation of CLARREO

14. (K) 21 November 2002, Gulf of Mexico Aircraft is key approach for direct radiance validation of EOS & NPOESS Fantastic Agreement, but 3-sigma uncertainty in validation is at least 0.5 K** **Contributions from Sampling, Representativeness, Noise, Double differences, as well as S-HIS Accuracy (70% chance error <0.16 K) Aircraft Inter-Calibration

15. Non-uniform Spatial Response UniformLinear, ±10%Linear, ±25% Cone~1/r 2 (~4km FWHM)

16. Uncertainty in Monthly Means Uniform, Linear 20%, Linear 50%, ~1/r 2 MODIS Band 31@11  m; 100km CLARREO FOVs every 14s; CrIS/AIRS Tophat, STDEV = 0.214 K 20% linear: STDEV = 0.220 K 50% linear: STDEV = 0.290 K ~1/r 2 : STDEV = 0.803 K Solid = threshold method, BTSTD ≤ 10K Dashed = uncertainty in weighted mean Conclusion: Non-uniform spatial response has minimal impact on inter-calibration uncertainty.

17. Summary, Conclusions For a nominal CLARREO DS mission and reasonable SNO thresholds (three 90 deg polar orbits, 100km nadir footprints every 14s, 10 deg max sounder scan angle, 15 min max time difference), the monthly intercal uncertainty budget is dominated by the assumed CLARREO radiometric noise level. With CLARREO single channel radiometric noise of 1K NEDT, the 1-sigma uncertainty in monthly intercal is less than 0.03K. Lower uncertainties are obtained with smaller (and more numerous) footprints and/or lower radiometric noise. Non-uniform spatial response has negligible impact on these results. For annual ensembles, a wide range of intercal BTs and low uncertainties in single channel 10K bins are obtained. When multiple channels are averaged, monthly 10k bins can be used. Need to remember spectral considerations for intercal objectives.

18. 1 ≤ STDEV ≤ 2 (5074 pts, stdev=0.08) 3 ≤ STDEV ≤ 4 (2783 pts, stdev=0.18) 5 ≤ STDEV ≤ 6 (1179 pts, stdev=0.27) 7 ≤ STDEV ≤ 8 ( 563 pts, stdev=0.34) 9 ≤ STDEV ≤ 10 ( 211 pts, stdev=0.47) Spatial Sampling Differences MODIS Band 31@11  m; 100km CLARREO FOVs every 14s; CrIS/AIRS Conclusion: The spatial sampling differences are Gaussian when separated by STDEV