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A Consensus Calibration Based on TMI and Windsat

A Consensus Calibration Based on TMI and Windsat. Tom Wilheit 1 ,Wes Berg 2 , Linwood Jones 3 , Rachael Kroodsma 4 , Darren McKague 4 , Chris Ruf 4 , Matt Sapiano 2 Texas A&M University, (2) Colorado State University, (3) University of Central Florida, (4) University of Michigan .

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A Consensus Calibration Based on TMI and Windsat

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  1. A Consensus Calibration Based on TMI and Windsat Tom Wilheit1,Wes Berg2, Linwood Jones3, Rachael Kroodsma4, Darren McKague4, Chris Ruf4, Matt Sapiano2 Texas 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 Angles Clean Up Other Problems Variety of Methods To Generate Two Point Recalibrations Unified set of Deltas Tbnew = A* Tbold + B Could Be More Complex Where Necessary. Currently Have Consensus Calibration based on Windsat and TMI Use 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 Overview Accuracy: 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 Future Double Differences Where Possible We Use Double Differences to Minimize Model Sensitivity Corresponding Channels e.g. TMI 19.35V/ Windsat 18.7V S = Source Sensor T= Target Sensor DD = (TBTobs – TBTcalc) - (TBSobs – TBScalc) = (TBTobs – TBSobs) - (TBTcalc– TBScalc)

  4. DATA SET July 2005-June 2006 TMI Version 7 (Includes reflector temperature correction) Windsat TAMU, UCF, CSU methods use coincident(±1h) observations binned in 1° boxes TAMU solves for SST, WS, PW and CLW using source sensor then computes target CSU similar to TAMU but external SST and covariances UCF computes both sets of TBs from GDAS analyses Univ. 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

  5. Unified Deltas Common TBs 163. 85. 188. 109. 200. 206. 135. Extrapolate method 1 &2 deltas to common TBs TAMU, UCF, CSU Methods All Data 0.31 -1.66 -0.61 -3.20-1.50 -3.24 -2.41 Lowest 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

  6. UNIFIED DELTAS Standard Deviations (K) Uncertainties in the Means (K) ALL LOW ALL LOW 10V 0.061 0.032 0.035 0.019 10H 0.066 0.078 0.038 0.045 19V 0.151 0.221 0.087 0.128 19H 0.179 0.189 0.103 0.109 21V 0.329 0.241 0.190 0.139 37V 0.010 0.059 0.006 0.034 37H 0.020 0.101 0.012 0.058

  7. Courtesy of Darren McKague

  8. Consensus Calibration Warm 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 WS Keep the numbers simple and round Windsat Gets 3 times the weight of TMI (i.e. 75%WS/25%TMI) Consensus Calibration 1.1 75% of Unified Deltas TMI_CC_1.1 10V 10H 19V 19H 21V 37V 37H 0.23K -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 280 285 284 284 281 281 Negative #’s indicate TMI cold relative to Windsat

  9. Plans for CC_1.2 Weights for Unified Deltas based on Error Models Common Partitioning of Cold End Values (Quartiles) Examine Updated Radiative Transfer Models Earth Incidence Angle Issue

  10. 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?

  11. Use TAMU Model to Calculate TMI-WS Differences Warm End Differences < 0.05K /Ignore Deltas 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 @ 281 280 285 284 284 281 281 Deltas 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

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

  13. Conclusions/Questions We have a Consensus Calibration that allows us to move forward Will be updated TMI 10 GHz Angle Issue is Real It Matters (a little) Needs to be Accounted for Where TMI is being used as a Standard Similar Problems will recur throughout the Constellation Accounted 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/h Absolute Accuracy is Another Story Hesitate to state an error bar

  14. Spare Slides

  15. TAMU ALGORITHM COMPONENTS 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 .16 EOF4 .10 EOF5 .05 EOF6 all < 0.02 RH .03 .02 .05 .34 CLHT .02 .03 LR .10 NET .03 .04 .06 .36 NOTES: ALL VALUES IN KELVINS VALUES LESS THAN 0.02K LEFT BLANK BUT INCLUDED IN SUMS

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

  17. TAMU ALGORITHM COMPONENTS 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 .09 EOF4 -.06 EOF5 .03 EOF6 all < 0.02 RH .02 .03 .20 CLHT -.02 .05 .06 LR -.06 NET .03 .06 .06 .20 NOTES: ALL VALUES IN KELVINS/ VALUES LESS THAN 0.02K LEFT BLANK BUT INCLUDED IN SUMS

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

  19. Relationship to PPS When tasks become routine we pass them over to PPS Matchup/Monitoring software Data Set support (1C and “Base” files) Consensus Calibration Effort In general, can we get a better calibration using multiple sensors? Try with TMI/Windsat combination Sounders We are in the process of organizing a methods comparison for sounders AMSR-E We have a preliminary analysis of JAXA AMSR-E data set Updated data set available Need additional data that PPS can provide SSM/I Next up/ CSU “Base” files available soon

  20. CC_1.1 Improves Self-Consistency of TMI Drastically CC_1.X Improves Self-Consistency of WS Significantly

  21. Updates to Consensus Calibration CC_1.2 McKague 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 doing GMI should have significant weight in Post Launch CC Soon after Core launch, stability in CC will become much more important. Update approval mechanism will be needed.

  22. Basics of the TAMU Model 3 Reasons to Present: Collaboration/ Example/Tool Used Here Source Sensor (e.g. WS)  Surface & AtmosphereTarget (e.g. TMI) Adjust Surface & Atmosphere for best fit to Source Radiances Iterate until Adjustments are < 0.01K For corresponding channels DTb is the double difference DTb = (Tsource – Ttarget)obs - (Tsource – Ttarget)calc Surface = Elsaesser Model (Wilheit/Kohn model has been used for comparison) Modified to allow for negative windspeeds Adjust SST & WS Atmosphere RT Models from Rosenkranz Modified to allow for Negative Cloud Liquid Water Cloud @ 4-5km Fixed RH Profile/Lapse Rate Adjust CLW and Temperature at bottom of Atmosphere (PW follows)

  23. Uncertainties in the TAMU Model Key Assumptions in the TAMU Model Cloud Location 4 to 5km Insignificant Contributor to Uncertainty Lapse Rate From Co-located GDAS: 6.26 ± 0.30 K/km Minor Contribution to Uncertainty Relative Humidity Profile Mean and Covariance Matrix from Co-located GDAS Compute EOFs for uncertainty Primary Source of Uncertainty

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