This document discusses efforts to create a consensus calibration between sensors to make their radiances physically consistent. It describes using multiple methods to generate recalibrations between sensors like TMI and Windsat. The current consensus calibration is based on TMI and Windsat, using TMI as the transfer standard with Windsat given 3 times the weight. This summary calibration is applied to other sensors like AMSR-E and SSM/I. The document also discusses plans to refine the consensus calibration further.

1. A Consensus Calibration Based on TMI and WindsatTom Wilheit1,Wes Berg2, Linwood Jones3, Rachael Kroodsma4, Darren McKague4, Chris Ruf4, Matt Sapiano2Texas A&M University, (2) Colorado State University, (3) University of Central Florida, (4) University of Michigan

2. GPM Intersatellite Working Group (aka X-CAL)Make Radiances from Constellation Radiometers Physically Consistent Differences of Frequencies/Incidence AnglesClean Up Other ProblemsVariety of Methods To Generate Two Point Recalibrations Unified set of DeltasTbnew = A* Tbold + BCould Be More Complex Where Necessary.Currently Have Consensus Calibration based on Windsat and TMIUse TMI as Transfer Standard. (CC_1.1) (75% Windsat—25% TMI)In Process of Applying to AMSR-E and SSM/I (F13,14,15)Working on CC_1.2 Not Very Different but More Defensible

3. X-CAL Status OverviewAccuracy: Relative/ To What Degree can We Make the Sensors Alike? Looks Like We are Meeting our 0.1K Goal We Continue to Refine for Sake of Credibility Absolute Difficult to Estimate/ Standards are Inadequate NIST is Working on Standards/ May Impact Satellites in FutureDouble Differences Where Possible We Use Double Differences to Minimize Model Sensitivity Corresponding Channels e.g. TMI 19.35V/ Windsat 18.7VS = Source Sensor T= Target SensorDD = (TBTobs – TBTcalc) - (TBSobs – TBScalc) = (TBTobs – TBSobs) - (TBTcalc– TBScalc)

4. DATA SETJuly 2005-June 2006TMI Version 7 (Includes reflector temperature correction)WindsatTAMU, UCF, CSU methods use coincident(±1h) observations binned in 1° boxesTAMU solves for SST, WS, PW and CLW using source sensor then computes targetCSU similar to TAMU but external SST and covariancesUCF computes both sets of TBs from GDAS analysesUniv. Michigan cold end method uses global histograms of TBs and finds limiting values.Warm End : U. Michigan uses method analogous to TAMU and CSU over Amazon Rain Forest. JPL and UCF use same method but less mature implementations

6. Unified Deltas Common TBs 163. 85. 188. 109. 200. 206. 135. Extrapolate method 1 &2 deltas to common TBsTAMU, UCF, CSU MethodsAll Data 0.31 -1.66 -0.61 -3.20-1.50 -3.24 -2.41Lowest 0.18 -1.71 -0.76 -3.08 -1.89-3.25 -2.42 Warm End (U. Mich Method) 281. 280. 285. 284. 284. 281. 281.K -0.76 -0.92 -1.20 -1.43 -3.37 -3.17 -3.16K

7. UNIFIED DELTASStandard Deviations (K) Uncertainties in the Means (K) ALL LOW ALL LOW10V 0.061 0.032 0.035 0.01910H 0.066 0.078 0.038 0.04519V 0.151 0.221 0.087 0.12819H 0.179 0.189 0.103 0.10921V 0.329 0.241 0.190 0.13937V 0.010 0.059 0.006 0.03437H 0.020 0.101 0.012 0.058

9. Courtesy of Darren McKague

10. Consensus CalibrationWarm End Variance TMI a little more than twice as large as WS (K**2)Cold End Variance TMI a little more than three times as large as WSKeep the numbers simple and round Windsat Gets 3 times the weight of TMI (i.e. 75%WS/25%TMI)Consensus Calibration 1.175% of Unified DeltasTMI_CC_1.1 10V 10H 19V 19H 21V 37V 37H0.23K -1.25 -0.46 -2.40 -1.42 -2.43 -1.81 @ 163K 85 188 109 200 206 135-0.57K -0.69 -0.90 -1.07 -2.53 -2.38 2.37 @ 281K 280 285 284 284 281 281Negative #’s indicate TMI cold relative to Windsat

11. Plans for CC_1.2Weights for Unified Deltas based on Error ModelsCommon Partitioning of Cold End Values (Quartiles)Examine Updated Radiative Transfer ModelsEarth Incidence Angle Issue

12. From Steve Bilanow’s Presentationat March 2011 X-CAL meetingTMI Pointing Uncertainty Effects“Prelaunch measure of TMI 10 V and10H boresight alignment offsets from a 49 degrees scan cone were reported at 0.555 and 0.185 degrees respectively*. * Memorandum from Jim Shiue, 12/11/97”This corresponds to ~ 1.3 K and -0.2 K bias shifts.When you do the trigonometry, this translates to increases of the Earth Incidence Angle for the two 10.7 GHz channels of 0.649° and 0.216°. (OK, a few too many significant figures)Is it real?Does it matter?

13. Use TAMU Model to Calculate TMI-WS DifferencesWarm End Differences < 0.05K /IgnoreDeltas Computed with TAMU Model 10V 10H 19V 19H 21V 37V 37H 0.34 -1.71 -0.86 -2.98 -1.73 -3.20 -2.49 -1.20 -1.48 Including Jim Shiue’s Angles@ 171K 89 202 137 200 216 156 -0.76 -0.92 -1.20 -1.43 -3.37 -3.17 -3.16@ 281 280 285 284 284 281 281Deltas are more self-consistent using Jim Shiue’s angles.“TMI_CC_1.1” based on TAMU model only @ Cold End, U. Mich at Warm End.75% Weight for Windsat, 25% TMI

14. New angles result in slightly more consistent set of deltas.

16. Conclusions/QuestionsWe have a Consensus Calibration that allows us to move forwardWill be updatedTMI 10 GHz Angle Issue is RealIt Matters (a little) Needs to be Accounted for Where TMI is being used as a StandardSimilar Problems will recur throughout the ConstellationAccounted for when known Won’t always be Known Recalibration Will Absorb this Sort of Problem.Goal of X-CAL is 0.1K consistency. Not all Sensors Will be That Good Still Not Good Enough For Climate Purposes Algorithm Team Needs to Think about Another Bias Removal Layer Average Precipitation in Washington DC is of order 0.1mm/hAbsolute Accuracy is Another Story Hesitate to state an error bar

17. Spare Slides

18. TAMU ALGORITHMCOMPONENTS OF UNCERTAINTY(All Cold End Data) 10V 10H 19V 19H 21V 37V 37H @TB 172 90 206 143 231 219 160 EOF1 -.02 .02 -.04 -.26 EOF2 .02 -.09 EOF3 .16EOF4 .10EOF5 .05EOF6 all < 0.02RH .03 .02 .05 .34 CLHT .02 .03LR .10NET .03 .04 .06 .36NOTES: ALL VALUES IN KELVINSVALUES LESS THAN 0.02K LEFT BLANK BUT INCLUDED IN SUMS

19. TAMU ALGORITHM UNCERTAINTYAll Cold End Data 10V 10H 19V 19H 21V 37V 37H @TB 172 90 206 143 231 219 160 Mod .03 .04 .06 .36Stat/M .02 .03 .06 .05 .04 .06 .03Net/M .04 .03 .07 .08 .36 .06 .03Stat/QR all below 0.02Net/QR .03 .02* .04 .06 .36 .02* .02*NOTES: ALL VALUES IN KELVINSVALUES LESS THAN 0.02K LEFT BLANKUse Red Values, if < 0.02 use 0.02

20. TAMU ALGORITHMCOMPONENTS OF UNCERTAINTY(Lowest Quartile Only) 10V 10H 19V 19H 21V 37V 37H @TB 170 88 198 131 219 213 151 EOF1 -.02 .02 -.16 EOF2 -.04 EOF3 .09EOF4 -.06EOF5 .03EOF6 all < 0.02RH .02 .03 .20 CLHT -.02 .05 .06LR -.06NET .03 .06 .06 .20NOTES: ALL VALUES IN KELVINS/VALUES LESS THAN 0.02K LEFT BLANK BUT INCLUDED IN SUMS

21. TAMU MODEL UNCERTAINTYLowest Quartile Only 10V 10H 19V 19H 21V 37V 37H @TB 170 88 198 131 219 213 151 Mod .03 .06 .06 .20Stat/M .07 .05 .04 .04 .05 .06 .03Net/M .08 .05 .07 .07 .21 .06 .03Stat/QR all < 0.02Net/QR .03 .06 .07 .20 NOTES: ALL VALUES IN KELVINSVALUES LESS THAN 0.02K LEFT BLANKUse Red Value

24. Relationship to PPSWhen tasks become routine we pass them over to PPSMatchup/Monitoring softwareData Set support (1C and “Base” files)Consensus Calibration EffortIn general, can we get a better calibration using multiple sensors?Try with TMI/Windsat combinationSoundersWe are in the process of organizing a methods comparison for soundersAMSR-EWe have a preliminary analysis of JAXA AMSR-E data setUpdated data set availableNeed additional data that PPS can provideSSM/INext up/ CSU “Base” files available soon

25. CC_1.1 Improves Self-Consistency of TMI DrasticallyCC_1.X Improves Self-Consistency of WS Significantly

26. Updates to Consensus CalibrationCC_1.2McKague suggested more rigorous method of unifying teams’ results. Requires uncertainty estimates for each team’s numbers We now have a second warm end estimate (UCF)Updates can come fairly often before launch of Core. Improved techniques/Additional Instruments We’re still figuring out the details of what we’re doingGMI should have significant weight in Post Launch CCSoon after Core launch, stability in CC will become much more important. Update approval mechanism will be needed.

27. Basics of the TAMU Model3 Reasons to Present: Collaboration/ Example/Tool Used HereSource Sensor (e.g. WS)  Surface & AtmosphereTarget (e.g. TMI)Adjust Surface & Atmosphere for best fit to Source RadiancesIterate until Adjustments are < 0.01KFor corresponding channels DTb is the double difference DTb = (Tsource – Ttarget)obs - (Tsource – Ttarget)calcSurface = Elsaesser Model (Wilheit/Kohn model has been used for comparison)Modified to allow for negative windspeedsAdjust SST & WSAtmosphere RT Models from RosenkranzModified to allow for Negative Cloud Liquid Water Cloud @ 4-5kmFixed RH Profile/Lapse RateAdjust CLW and Temperature at bottom of Atmosphere (PW follows)

28. Uncertainties in the TAMU ModelKey Assumptions in the TAMU ModelCloud Location 4 to 5km Insignificant Contributor to UncertaintyLapse Rate From Co-located GDAS: 6.26 ± 0.30 K/km Minor Contribution to UncertaintyRelative Humidity Profile Mean and Covariance Matrix from Co-located GDAS Compute EOFs for uncertainty Primary Source of Uncertainty