Chris Barnet NOAA/NESDIS/STAR (the office formally known as ORA)

1. Vertical Weighting Functions & Validation of Satellite Retrievals Chris Barnet NOAA/NESDIS/STAR (the office formally known as ORA) University of Maryland, Baltimore County (Adjunct Professor) AIRS Science Team Member NPOESS Sounder Operational Algorithm Team Member GOES-R Algorithm Working Group – Chair of Sounder Team NOAA/NESDIS representative to IGCO July 27, 2006 MSRI-NCAR Summer Workshop on Data Assimilation for the Carbon Cycle

2. Outline for Todays’s Lecture • A question from yesterday. • What are they. • How are they used. • Validation Techniques. • Discussion of observation types (mostly references). • Comparisons of CH4 and CO2 product with in-situ. • Summary of some papers on using AIRS CO2 products in carbon assimilation systems. • Trace gas product correlations – A better way to use AIRS data?

3. A Question From Jeremy • What does the K matrix look like for temperature and moisture? • Why is there more vertical information about temperature than CO2?

4. Example of T(p) & q(p) Channel Kernel Functions AIRS 15 µm (650-800 cm-1) band K = dR/dT AIRS 6.7 µm (1200-1600 cm-1) band K = dR/dq

5. Weak Lines (Water & CO2) in Window Region Sound Boundary Layer Temperature Texas Spikes down - Cooling with height (No inversion) Brightness Temperature (K) Spikes up - Heating with height Ontario (low-level inversion)

6. Example of 15 µm spectrum with “in-between” the lines marked with blue dots Lines up (in emission): T(z) increases with altitude Lines down (in absorption): T(z) decreases with altitude

7. From Yesterday: K = dR/dCO2 Polar TPW = 0.5 cm Mid-Latitude TPW = 1.4 cm Tropical TPW = 2.5 cm moisture optical depth pushes peak sensitivity upwards Isothermal vertical structure weakens sensitivity

8. Why are CO2 channel functions broad & all at the same altitude while T(p) functions have profile information? • Spectroscopy: The CO2 lines are strong narrow lines. Temperature affects the width (and hence the channel transmittance) while # of CO2 molecules affects the strength. Once the line is saturated (near the surface, where p is large) we loose sensitivity. • Radiative transfer: The temperature enters both in the absorption coefficient and in the Planck function.

9. Why is CH4 considered to be 25x more powerful as a greenhouse gas than CO2?

10. Why is CH4 considered to be 25x more powerful as a greenhouse gas than CO2? • At 380 ppm the CO2 lines are saturated and as CO2 increases the absorption of energy changes as the log(N). • CH4 is 1.8 ppm and the lines are not saturated. As the amount of CH4 changes the absorption is linear w.r.t # of molecules.

11. Retrieval Averaging Functions • See • Rogers 2000, pg. 43-44 & pg. 83-85 • Rodgers and Conner 2003 • My notes – section 8.12.1

12. Retrieval Vertical Weighting Functions • Our Retrieval Equation Can Be Written As • Note that this equation is really a weighting average of the state determined via radiances and the a-priori. • The radiance covariance can be written as KTN-1K, in geophysical units, and • The product covariance is given by [KTN-1K + C-1]-1

13. We can derive the Averaging Function our minimization equation • We can linearize the retrieval about the “truth” state • And simplify by replacing the region highlighted in green above with the variable G

14. Computing the Vertical Averaging Function • The vertical averaging function is the amount of the answer that came from the radiances • And I-A is the amount that came from the prior Retrieval covariance Inverse of a-priori covariance

15. Value of the Vertical Averaging Function • A is the retrieval weighing of the channel kernel functions (think of a retrieval as an integrator of data) • A tells you how much the observations were believed. • I-A tells you how much of the a-priori was believed. • When comparing other measurements (such as high vertical resolution sondes or aircraft) the validation measurements • Must have similar vertical smoothing • Should be “degraded” by the fraction of the prior that entered the solution (i.e., we know we can’t measure 100%) • When using AIRS products the A maxtrix • Tells you the vertical correlation between parameters • Tells you how much to believe the product and where to believe the product. • You can remove our a-priori assumptions and substitute your own.

16. We can use averaging function to optimally smooth the truth for comparisons Aj,j retrieval “convolved truth” a-priori “truth” This retrieval is only believed at the 50% level

17. Application of Vertical Averaging Functions with modeled CO2 • Comparison of CO2 product and Kawa 2004 model for April 2005 • At first glance it looks like the retrieval and model do not agree. • But if we “degrade” the model with the retrieval a-priori they agree quite well. • To use AIRS products to nudge an assimilation, the vertical weighting function tells the model when and where to believe the AIRS products.

18. Or We Can compute a Averaging Function via Brute Force • Start with the retrieval state, X0 • Perturb X0 in some atmosphere layer by Xk • Compute change in radiance, R(X0+Xk)-R(X0) • Compute a new retrieval, Xk, using the perturbed radiance. • Xk-X0 is the jth column of Akj • Goto Step 1 and compute another row of A This method has the advantage that the entire system, including cloud correction and multiple-interacting and non-linear retrieval steps can be analysed.

19. The “Brute Force” Averaging provided a Sanity test for Internal Averaging Functions A = G*K Ajk & trace{A} via Brute Force for T(p)

20. Validation

21. “what is truth” • Compare to ECMWF & NCEP Models (e.g., See Susskind, 2005) • Can compare complete global dataset • Differences can be model or retrieval errors • Implicitly validating against all other instruments (space-borne, sondes, buoy’s etc.) used in analysis • Compare to Radiosondes, Ozonesondes (e.g., See Tobin, 2006, Divakarla, 2006) • Only a couple hundred “dedicated” sondes are flown per year. Usually we fly 2 sondes so we can see lower and upper air at overpass time. • Sondes can take 1-2 hours to ascend • Sondes can drift up to 100’s of km’s • A few hundred sondes are launched globally per day that are within 300 km and +/- 1 hour of our overpass. • Different sonde instruments, quality of launches, etc. • In-situ intensive experiments with sondes, aircraft and LIDAR. • Have participated in INTEX-NA6 AEROSE, START, MILAGRO, INTEX-B, AMMA

22. An example of things that go wrong with “truth” These 2 sondes were launched 1.5 hours apart from same location.

23. Example of Validation of Methane Products from AIRS with ERSL/GMD surface flasks and aircraft

24. Also providing the vertical information content to understand CH4 product CH4 total column f/ transport model (Sander Houweling, SRON) AIRS mid-trop measurement column Peak Pressure of AIRS Sensitivity Fraction Determined from AIRS Radiances

25. Comparison of CH4 product & ESRL/GMD Continuous Ground Site Barrow Alaska 3deg. x 3deg. gridded retrieval averaged over 60-70 lat, & -165 to -90 lng

26. Comparisons of AIRS product to ESRL/GMD Aircraft Observations ESRL/GMD aircraft profiles are the best validation for thermal sounders since they measure a thick atmospheric layer.

27. ESRL/GMD Flask Data from Poker Flats, Alaska: Seasonal cycle is a function of altitude 7.5 km 385 mb 5.5 km 500 mb 1.5 km 850 mb Surface Flasks (Barrow)

28. We need to determine how much of our CH4 signal is from stratospheric air Dobson-Brewer circulation UT/LS region in high latitudes has “older” air. We will explore tracer correlations to unravel surface vs stratospheric sources. (working w/ L. Pan, NCAR) Can depress high latitude, high altitude methane signals in winter/spring time-frame.

29. Example of Validation of Carbon Dioxide Products from AIRS with ESRL/GMD Marine Boundary Layer and Aircraft Products and Japanese Commercial Aircraft

30. Also providing the vertical information content & comparing CO2 product with models CO2 Transport Model Randy Kawa (GSFC) AIRS mid-trop measurement column Fraction Determined from AIRS Radiances Averaging Function Peak Pressure

31. Preliminary Comparisons to ESRL/GMD aircraft • Comparison of AIRS & ESRL/GMD observations.. • Usually  5 hour time difference • Limit retrievals within 200 km of aircraft. • Spot vs. regional sampling • Retrieval is average of “good” retrievals • 3 – 50 ret’s are used in each dot. •  = ± 3.1 ppmv, correlation = 0.83 • Investigation of outliers is in-work.

32. Comparisons to JAL aircraft observations & ESRL/GMD MBL model ESRL/GMD Marine Boundary Layer Model (surface measurement) AIRS CO2 retrieval from GRIDDED dataset – mid-trop thick layer measurement Matsueda et al. 2002 aircraft – single level measurement JAL Aircraft data provided by H. Matsueda

33. Same as before, but in-situ CO2 adjusted by a-priori in retrieval In-situ data is adjusted by the % of a-priori in our regularized retrieval. This depresses the seasonal amplitude of the in-situ data and is a gauge of our retrieval performance. Differences should exist between single level (surface or altitude) observations and AIRS thick layer observations. JAL Aircraft data provided by H. Matsueda

34. Again, to what extent does stratospheric age of air play a role? Surface measurements (dashed line) and model of 500 mb concentration (solid line) can differ by 5 ppm (NOTE: Vert. Scale is 350-385 ppm) Brewer-Dobson effect has altitude, latitude, and seasonal variation with a maximum in northern winter/spring. 2004 2000 2002 Model runs of Brewer-Dobson circulation effect are courtesy of Run-Lie Shia, Mao-Chang Liang, Charles E. Miller, and Yuk L. Yung, California Inst. Of Technology & NASA Jet Propulsion Lab.

35. Example of Validation of CO2 measurements with Models

36. Chevallier, Engelen & Peylin,GRL, 2005 • Compared CO2 derived from ECMWF 4DVAR analysis of 18 AIRS channels (R. Engelen’s CO2 product) vs Laboratoire de Météorologie Dynamique (LMDZ) GCM driven by surface flux climatology • Large systematic differences at high latitudes • Significant sea/land contrast • Use of a constant averaging kernel may contribute to biases.

37. Chevallier (continued)

38. Tiwari, et al., JGR 2006 (in press) • CO2 flux estimates of Rodenbeck 2003 used as boundary conditions for TM3 (4x5 deg, 19 , NCEP winds) & LMDZ (2.5x3.75 deg, 19 , ECMWF winds) • Averaged monthly fluxes for 1993-2001 and used as driver of transport for 2000-2003 • Spun up model from Jan. 2000-Dec. 2002 • Compared to R. Engelen’s product to explore two potential pathways for transport of CO2 • North hemisphere mid-lat air laden w/ fossil fuel via northern high latitudes along upward sloping constant pot. Temp surfaces. • Dispersal of CO2 laden air in the PBL toward the tropics and subsequent upwards via deep convection. • Conclusions • Models agree with each other more than with retrievals. • Hovmoeller diagrams (time vs latitude) show that models transport the CO2 via the northern pathways whereas in the retrievals the CO2 shows up instantaneously. • More work is needed.

39. Tiwari: Comparison of Models and R.Engelen’s AIRS CO2 Product

40. Tiwari: Comparison of Models and R.Engelen’s AIRS CO2 Product

41. Both of These Studies Have Caveats • Used a constant averaging function that is probably too high. • Noted land/sea boundaries were evident in product. • Used the ECMWF derived CO2 that • Only uses 18 channels • Uses ECMWF 4DVAR analysis that already has used AIRS radiance data. • Uses internal clear flag that appears to select too many cases as clear.

42. Can we “Validate” our Product Using Atmospheric Correlations Or Can AIRS product correlations have scientific value

43. Suntharalingam, Jacob, Palmer, Logan, Yantosca, Xiao and Evans 2004 Biofuel & biomass would be low numbers  10-12 Biosphere would be very large numbers  800-1000 Fossil fuel would be  25 w/o CO controls,  100 with

44. Pan, Wei, Kinnison, Garcia, Wuebbles, and Brasseur (submitted to JGR) • Using aircraft measurements of O3, CO, and H2O in the upper troposphere/lower stratosphere to validate MOZART-3 & WACCM3 model. • Chemical discontinuity at tropopause caused by changes in thermal and dynamic fields (Brewer-Dobson circulation)

45. Pan et al, 2006: Example of ER-2 Observations Profiles from 38 ER-2 flights from approximately 5-18 km near Fairbanks Alaska (65N, 147W)

46. O3,CO correlations Green cases: below thermal tropopause Red cases: above thermal tropopause Case.1 MOZART-3 w/ ECMWF Op winds Case.2 MOZART-3 w/ ECMWF Exp471 winds Case.3 WACCM3

47. O3,H2O Correlations Green cases: below thermal tropopause Red cases: above thermal tropopause Case.1 MOZART-3 w/ ECMWF Op winds Case.2 MOZART-3 w/ ECMWF Exp471 winds Case.3 WACCM3

48. So How Does This Relate to AIRS measurements? • We are collaborating with Laura Pan (NCAR) to compare AIRS products with Aircraft Products • We are computing correlations of AIRS products to see if we can distinguish air mass types. • Can we identify cases affected by Brewer-Dobson circulation and improve our ESRL/GMD & Matsueda comparisons? • Can we identify biomass regions and chemical conversion of CO?

49. Preliminary Analysis Looks Promising

50. Example of pollution eventsDomes of CO2 over cities? Balling et al. , 2001 Idso et al. 2001