Chris BarnetNOAA/NESDIS/STAR

(the office formally known as ORA)

University of Maryland, Baltimore County (Adjunct Professor)AIRS Science Team Member

NPOESS Sounder Operational Algorithm Team MemberGOES-R Algorithm Working Group – Chair of Sounder Team

NOAA/NESDIS representative to IGCO

July 27, 2006MSRI-NCAR Summer Workshop on

Data Assimilation for the Carbon Cycle

Vertical Weighting Functions & Validation of Satellite Retrievals

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

1. What does the K matrix look like for temperature and moisture?

2. 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

Spikes down - Cooling with height

Spikes up -Heating with height

Texas

Ontario

Bri

ghtn

ess

Tem

pera

ture

(K

)

(low-level inversion)

(No 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

Mid-Latitude

TPW = 1.4 cm

Polar

TPW = 0.5 cm

Tropical

TPW = 2.5 cm

Isothermal vertical structure weakens

sensitivity

moisture optical depth pushes peak sensitivity upwards

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

1)Rogers 2000, pg. 43-44 & pg. 83-85

2)Rodgers and Conner 2003

3) 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

Aj,j

“truth”

a-priori

retrieval

“convolved truth”

We can use averaging function to optimally smooth the truth for comparisons

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

1. Start with the retrieval state, X0

2. Perturb X0 in some atmosphere layer by Xk

3. Compute change in radiance, R(X0+Xk)-R(X0)4. Compute a new retrieval, Xk, using the perturbed

radiance.5. Xk-X0 is the jth column of Akj

6. 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

Ajk & trace{A} via Brute Force for T(p) A = G*K

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

AIRS mid-trop measurement column

CH4 total column f/ transport model (Sander Houweling, SRON)

Fraction Determined from AIRS Radiances

Peak Pressure of AIRS Sensitivity

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

Surface Flasks (Barrow)

1.5 km 850 mb

5.5 km 500 mb

7.5 km 385 mb

28

We need to determine how much of our CH4 signal is from stratospheric air

Dobson-Brewer circulation

Can depress high latitude, high altitude methane signals in winter/spring time-frame.

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)

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

AIRS mid-trop measurement column

CO2 Transport ModelRandy Kawa (GSFC)

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

Matsueda et al. 2002 aircraft – single level measurement

ESRL/GMD Marine Boundary Layer Model (surface measurement)

AIRS CO2 retrieval from GRIDDED dataset – mid-trop thick layer measurement

JAL Aircraft data provided by H. Matsueda

33

Same as before, but in-situ CO2 adjusted by a-priori in retrieval

JAL Aircraft data provided by H. Matsueda

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.

34

Again, to what extent does stratospheric age of air play a role?

Model runs of Brewer-Dobson circulation effect are courtesy of Run-Lie Shia, Mao-Chang Liang, Charles E. Miller, andYuk L. Yung, California Inst. Of Technology & NASA Jet Propulsion Lab.

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.20022000 2004

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

51

So maybe the utility of thermal sounders has yet to be exploited

• AIRS has measured global tropospheric CO2 & CH4 for the first time.

• AIRS has a unique capability to intercompare tropospheric products of temperature, water, O3, CO, CH4, and CO2.

• We expect to learn new things from this dataset.• We are exploring diurnal signals and hope to identify large

pollution or biosphere events over the lifetime of these instruments.

• We are exploring the use of trace gas correlations within the AIRS products.

• We want to work with transport modelers to compare our product to a realistic emission scenario for CO2 with proper vertical weighing functions.

• Other ideas?

52

ReferencesAIRS Instrument & Retrieval Algorithms

• Aumann, H.H., M.T. Chahine, C. Gautier, M.D. Goldberg, E. Kalnay, L.M. McMillin, H. Revercomb, P.W. Rosenkranz, W.L. Smith, D.H. Staelin, L.L. Strow and J. Susskind 2003. AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens. v.41 p.253- 264. {PDF FILE = Aumann_IEEE8big15aug02.pdf}

• Susskind, J., C.D. Barnet, J.M. Blaisdell, L. Iredell, F. Keita, L. Kouvaris, G. Molnar and M.T. Chahine 2006. Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder Advanced Microwave Sounding Unit as a function of fractional cloud cover. J. Geophys. Res. v.111 doi:10.1029/2005JD006272, 19 pgs. {PDF FILE = 2005JD006272_susskind.pdf}

• Susskind, J., C.D. Barnet and J.M. Blaisdell 2003. Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens. v.41 p.390-409. {PDF FILE = susskind_IEEE23apr02sub.pdf}

• Rodgers, C.D. 2000. Inverse methods for atmospheric sounding: Theory and practice. World Scientific Publishing 238 pgs.

53

ReferencesAnalysis of AIRS C products

• Barkley, M.P., P.S. Monks and R.J. Engelen 2006. Comparison of SCIAMACHY and AIRS CO2 measurements over North America during the summer and autumn of 2003. Geophys. Res. Lett. (in press). {PDF FILE = grl_fsi_v1.pdf}

• Chevallier, F., R.J. Engelen and P. Peylin 2005. The contribution of AIRS data in the estimation of CO2 sources and sinks. Geophys. Res. Lett. v.32 L23801 doi:10.1029/2005GL024229, 4 pgs. {PDF FILE = 05chevallier_publ.pdf}

• Tiwari,Y.K., M. Gloor, R.J. Engelen, F. Chevallier, C. Rodenbeck, S. Korner, P. Peylin, B.H. Braswll and M. Heimann 2006. Comparing CO2 retrieved from AIRS with model predictions: implications for constraining surface fluxes and lower to upper tropospheric transport. J. Geophys. Res. (in press).

– Rodenbeck, C., S. Houweling, M. Gloor and M. Heimann 2003. CO2 flux history 1982-2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmos. Chem. Phys. v.3 p.1919-1964. {PDF FILE = acp-3-1919.pdf}

54

ReferencesAIRS Trace Gas Algorithms

• Haskins, R.D. and L.D. Kaplan 1993. Remote sensing of trace gases using the atmospheric infrared sounder. Intl Rad. Symp. (QC912.3 I57 1992, Deepak Publ.) p.278-281. {PDF FILE = haskins_kaplan_1992.pdf}

• Crevoisier, C., S. Heilliette, A. Chedin, S. Serrar, R. Armante and N.A. Scott 2004. Midtropospheric CO2 concentration retrieval from AIRS observations in the tropics. Geophys. Res. Lett. v.31 p.17106-17110. doi:10.1029/2004GL020141 {PDF FILE = 2004GL020141.pdf}

• Crevoisier, C., A. Chedin and N.A. Scott 2003. AIRS channels selection for CO2 and other trace gases retrieval. Quart. J. Roy. Meteor. Soc. v.128 p.2719-2740. doi:10.1256/qj.02.180 {PDF FILE = 03qjrms_airs_chl.pdf}

• Engelen, R.J. and A.P. McNally 2005. Estimating atmospheric CO2 from advanced IR satellite radiances within an operational 4D-Var data assim. system: Results and Validation. J. Geophys. Res. v.110 D18305 doi:10.1029/2005JD005982 {PDF FILE = 2005JD005982_engelen.pdf}

• Engelen, R.J. and G.L. Stephens 2004. Information content of infrared satellite sounding measurements with respect to CO2. J. Appl. Meteor. v.43 p.373-378. {PDF FILE = Engelen_jap2004.pdf}

• Engelen, R.J., G.L. Stephens and A.S. Denning 2001. The effect of CO2 variability on the retrieval of atmospheric temperatures. Geophys. Res. Lett. v.28 p.3259-3263. {PDF FILE =2001GL013496_engelen.pdf}

55

ReferencesAveraging Functions & Validation

• Divakarla, M.G., C.D. Barnet, M.D. Goldberg, L.M. McMillin, E. Maddy, W.W. Wolf, L. Zhou and X. Liu 2006. Validation of Atmospheric Infrared Sounder temperature and water vapor retrievals with matched radiosonde measurements and forecasts. J. Geophys. Res. v.111 D09S15 doi:10.1029/2005JD006116, 20 pgs. {PDF FILE = 2005JD006116.pdf}

• Gettelman, A., E.M. Weinstock, E.J. Fetzer, F.W. Irion, A. Eldering, E.C. Richard, K.H. Rosenlof, T.L. Thompson, J.V. Pittman, C.R. Webster and R.L. Herman 2004. Validation of Aqua satellite data in the upper troposphere and lower stratosphere with in situ aircraft. Geophys. Res. Lett. v.31 L22107 doi:10.1029/2004GL020730 {PDF FILE = Gettelman2004GL020730.pdf}

• Houweling, S., F. Dentener, J. Lelieveld, B.P. Walter and E.J. Dlugokencky 2000. The modeling of tropospheric methane: How well can point measurements be reproduced by a global model. J. Geophys. Res. v.105 p.8981-9002. {PDF FILE = 1999JD901149.pdf}

• Houweling, S. 2000. Global modeling of atmospheric methane sources and sinks. PhD Dissertation 177 pgs. {PDF FILE = houweling_thesis.pdf}

• Kawa, S.R, D.J. Erickson III, S. Pawson and Z. Shu 2004. Global CO2 transport simulations using meteorological data from the

NASA data assimilation system. J. Geophys. Res. v.109 D18312 doi:10.1029/2004J004 554, 17 pgs. {PDF FILE = 2004JD004554_kawa.pdf}

• Rodgers, C.D. and B.J. Conner 2003. Inter-comparison of remote sounding instruments. J. Geophys. Res. v.108 p.1-14. doi:10.1029/2002JD002299 {PDF FILE = 03JGR_Rodgers.pdf}

• Tobin, D.C., H.E. Revercomb, C.C. Moeller and T.S. Pagano 2006. Use of Atmospheric Infrared Sounder high spectral resolution spectra to assess the calibration of Moderate resolution Imaging Spectroradiometer on EOS Aqua. J. Geophys. Res. v.111 D09S05 doi:10.1029/2005JD006095, 15 pgs. {PDF FILE = 2005JD006095.pdf}

• Tobin, D.C., H.E. Revercomb, R.O. Knuteson, B.L. Lesht, L.L. Strow, S.E. Hannon, W.F. Feltz, L.A. Moy, E.J. Fetzer and T.S. Cress 2006. Atmospheric Radiation Measurement site atmospheric state best estimates for Atmospheric Infrared Sounder temperature and water retrieval validation. J. Geophys. Res. v.111 D09S14 doi:10.1029/2005JD006103, 18 pgs. {PDF FILE = 2005JD006103.pdf}

56

ReferencesCirculation & Gas Correlations

• Shia, R.L., M.C. Liang, C.E. Miller and Y.L. Yung 2006. CO2 in the upper troposphere: influence of stratosphere-troposphere exchange. Geophys. Res. Lett. v.33 L14814 doi:10.1029/2006GL026141, 4 pgs. {PDF FILE = 2006GL026141_shia.pdf}

• Palmer, P.I., P. Suntharalingam, D.B.A. Jones, D.J. Jacob, D.G. Streets, Q. Fu, S.A. Vay and G.W. Sachse 2006. Using CO2:CO correlations to improve inverse analyses of carbon fluxes. J. Geophys. Res. v.111 D12318 doi:10.1029/2005JD006697, 11 pgs. {PDF FILE = 06jgr_palmer_co_co2.pdf}

• Suntharalingam, P., D.J. Jacob, P.I. Palmer, J.A. Logan, R.M. Yantosca, Y. Xiao and M.J. Evans 2004. Improved quantification of Chinese carbon fluxes using CO2/CO correlations in Asian outflow. J. Geophys. Res. v.109 D18S18 doi:10.1029/2003JD004362, 13 pgs. {PDF FILE = 2003JD004362_suntha.pdf}

57

ReferencesCO2 Domes, Fossil Fuel Use

• Balling Jr., R.C., R.S. Cerveny and C.D. Idso 2001. Does the urban CO2 dome of Phoenix, Arizona contribute to its heat island? Geophys. Res. Lett. v.28 p.4599-4601. {PDF FILE = 2000GL012632.pdf}

• Idso, C.D., S.B. Idso and R.C. Balling Jr. 2001. Intensive two-week study of an urban CO2 dome. Atmospheric Environment v.35 p.995-1000. {PDF FILE = 01_idso_domes.pdf}

• Deffeyes, Kenneth 2005. Beyond oil: The view from Hubbert's peak. Hill and Wang, (div of Farrar, Straus and Giroux) NYC 202 pgs.

• Deffeyes, Kenneth 2001. Hubbert's peak: the impending world oil shortage. Princeton University Press 208 pgs.

• Eshel, G. and P.A. Martin 2006. Diet, energy, and global warming. Earth Interactions v.10 p.1-17. {PDF FILE = 06ei_eshel.pdf}

• Eshel, G. and P.A. Martin 2006. Diet, energy, and global warming. BAMS v.5 p.562-562. {PDF FILE = 0605bams_diet_energy.pdf}