1. Jennifer Wei 1,2, Antonia Gambacorta 1,2, Eric Maddy 1,2, Xiaozhen Xiong 1,2, Fengyin Sun 1,2, Xingpin Liu 1,2, Murty Divakarla 2,3 ….. Chris Barnet 2, Mitch Goldberg 2 START08/pre-HIPPO Workshop Jan. 09, 2008 AIRS/IASI Trace Gas Products (Level 2) 1 Perot Systems Government Services 2 NOAA/NESDIS/STAR 3 IMSG

2. Trace Gas Product Potential from Operational Thermal Sounders Haskins, R.D. and L.D. Kaplan 1993 GasRange (cm -1 )Precisiond.o.f.Interfering GasesAIRSIASI H2OH2O1200-160015%4-6CH4, HNO3NASA DAACApr 2008 O3O3 1025-105010%1.25H2O,emissivityNASA DAACApr 2008 CO2080-220015%  1 1 H2O,N2ONASA DAACApr 2008 CH 4 1250-13701.5%  1 1 H2O,HNO3,N2ONASA DAACApr 2008 CO 2 680-795 2375-2395 0.5%  1 1 H2O,O3 T(p) NOAA NESDIS Apr 2008 Volcanic SO 2 1340-138050% ??< 1H2O,HNO3TBD HNO 3 860-920 1320-1330 50% ??< 1emissivity H2O,CH4,N2O NOAA NESDIS Apr 2008 N2ON2O1250-1315 2180-2250 2520-2600 5% ??< 1H2O H2O,CO NOAA NESDISApr 2008 CFCl 3 (F11)830-86020%-emissivityNo plans CF 2 Cl (F12)900-94020%-emissivityNo plans CCl 4 790-80550%-emissivityNo plans

3. Example of Ozone from AIRS What has been learned so far….. High degree of consistency with dynamical variability of UTLS Realistically map chemical transitions between stratosphere and troposphere Show reasonable agreement with aircraft data over a large dynamical range of ozone Comparisons with ozonesonde show good agreement between 400-50 mb range Bian J., A. Gettelman, H. Chen, L. L. Pan (2007), Validation of satellite ozone profile retrievals using Beijing ozonesonde data, J. Geophys. Res., 112, D06305, doi:10.1029/2006JD007502. Monahan K. P., L. L. Pan, A. J. McDonald, G. E. Bodeker, J. Wei, S. E. George, C. D. Barnet, E. Maddy (2007), Validation of AIRS v4 ozone profiles in the UTLS using ozonesondes from Lauder, NZ and Boulder, USA, J. Geophys. Res., 112, D17304, doi:10.1029/2006JD008181. Divakarla et al. (2008), JGR-A, submitted.

4. X

5. Ozone A priori for Version 6 retrieval Consideration of a tropopause referenced climatology Altitude Tropopause referenced Relative Alt. Pan et al, 2004

6. Example of Carbon Monoxide from AIRS What has been learned so far…. CO can be used to estimate horizontal and vertical transport during combustion events. CO can be used to help us distinguish combustion sources (fossil or biomass) from other sources/sinks in our methane and carbon dioxide products. CO and Ozone may help us improve atmospheric vertical transport models in the mid-troposphere. Warner, J., M. M. Comer, C. D. Barnet, W. W. McMillan, W. Wolf, E. Maddy, and G. Sachse (2007), A comparison of satellite tropospheric carbon monoxide measurements from AIRS and MOPITT during INTEX-A, J. Geophys. Res., 112, D12S17, doi:10.1029/2006JD007925.

7. 500 mb 700 mb 850 mb UMBC Courtesy of W. McMillan (mcmillan@umbc.edu) AIRS CO and Trajectories CO from northern Alaskan fires was transported to the lower atmosphere in SE of US CO from southern Alaska Fires was transported to Europe at high altitudes (5 km)

8. Example of Methane from AIRS (Xiaozhen.Xiong@noaa.gov) What has been learned so far… The accuracy is about 0.5-1.5% depending on different altitudes, and sensitive region is at 200-300mb in the tropics and 300-500 mb in the high northern hemisphere (HNH). Observed significant summer enhancement of CH 4 in HNH (possibly due to wetland emissions/thawing permafrost, paper submitted to GRL by Xiong et al.) Observed significant plume of CH 4 over the Tibetan Plateau (collaborate with S. Houweling, paper in preparation) Use AIRS CH4 in conjunction with model simulations to better quantify the source region

9. Comparison of CH 4 product & ESRL/GMD Continuous Ground Site Barrow Alaska 3deg. x 3deg. gridded retrieval averaged over 60-70 lat, & -165 to -90 long.

10. AIRS CH4 comparison to ESRL Aircraft Xiong, X., C. Barnet, C. Sweeney, E. S. Maddy, X. Liu, L. Zhou, and M. D. Goldberg (2008), Characterization and Validation of Methane Products from the Atmospheric Infrared Sounder (AIRS), J. Geophys. Res., doi:10.1029/2007JG000500, in press.

11. CH 4 plume over Tibetan Plateau Paper is in preparation

12. AIRS CH 4 at 300 mb Courtesy of L. Pan Xiong et al., Satellite Observed Increase of Tropospheric Summer Methane Concentration: Is it due to Wetland Emission over the High Northern Hemisphere?, GRL, 2008 (submitted)

13. Example of Carbon Dioxide from AIRS (Eric.Maddy@noaa.gov) What has been learned so far…. Maximum measurement sensitivity from AIRS in the middle to upper troposphere – broadly weighted column measurement. Retrievals require significant spatial and temporal averaging (~5 day / 400 km) to improve S/N. Total uncertainty in middle-to-upper troposphere: 1 ppmv in tropics vs. high altitude aircraft (JAL Matsueda) 2 ppmv in middle/high latitudes vs. ESRL/GMD aircraft. CO2 Retrievals from the Atmospheric Infrared Sounder: Methodology and Validation, Maddy, E. S. and Barnet, C. D. and Goldberg, M. D. and Sweeney, C. and Liu, X., Accepted to JGR-A

14. Validation: AIRS CO 2 (6km – 8km) vs ESRL/GMD Aircraft (2.5km – 8km) Right: Taylor diagram [Taylor, JGR, 2001] for 2005 aircraft matchups illustrates retrieval skill (end of arrow) relative to a priori (beginning of arrow). Left: AIRS retrieval and ESRL aircraft timeseries at Poker Flat, Alaska shows good agreement in placement of seasonal cycle and year-to-year variability.

15. ESRL/GMD Aircraft vs. AIRS Retrieval CO 2

16. NOAA AIRS CO 2 Product is Still in Development Product is CO2(p) profile with associated averaging kernels. Measuring a product to 0.5% is inherently difficult –Cloud clearing error (also error estimates) strongly impacts the CO2 product. –Errors in moisture of ±10% is equivalent to ±0.7 ppmv errors in CO2. –Errors in surface pressure of ±5 mb induce ±1.8 ppmv errors in CO2. –AMSU side-lobe errors corrupt the ability to use the 57 GHZ O2 band as a T(p) reference point. Reduction of Core product retrieval errors is critical for CO 2. Currently, we can characterize seasonal and latitudinal mid- tropospheric variability to test product reasonableness. The real questions is whether thermal sounders can contribute to the source/sink questions. –Requires accurate vertical & horizontal transport models –Having simultaneous O 3, CO, CH 4, and CO 2 products is a unique contribution that thermal sounders can make to improve the understanding of transport.

17. IASI & AIRS Global Measurements 4 Times/Day AQUA METOP Initial Joint Polar System: an agreement between NOAA & EUMETSAT to exchange data and products. –NASA/Aqua in 1:30 pm orbit (May 2002) –EUMETSAT/IASI in 9:30 am orbit (October 2006)

18. The NOAA Unique Level 2 Processing System The NOAA level 2 processing is a unique system to compute atmospheric core and trace gas products. The whole architecture is a file-driven system compatible with multiple instruments. This system has been developed during the Aqua mission, using AIRS/AMSU/MODIS Instruments. Although the system was built for AIRS, it was designed to be expandable for both IASI and CrIS. This system has been thoroughly validated using several in-situ measurement campaigns (e.g., ESRL/GMD Aircraft, JAL, INTEX, etc.) This system is a reliable, well tested and fast package that we are migrating into operations for IASI.

19. IASI & AIRS Carbon Monoxide ( October 22nd 2007) 12 34

20. Spectral Coverage Comparison: AIRS, IASI, & CrIS CO 2 O3O3 CO CH 4 AIRS, 2378 chs CrIS, 1305 chs IASI, 8461 chs

21. Preliminary selection of IASI channels for physical retrieval. (NOTE: All channels except non-LTE are used in regression) Ignored – non- LTE CC69 T152 Q 87 O3O3 53 CO33 CH 4 59 CO 2 79 HNO 3 14 N2ON2O58

22. Instrument Noise, NEΔT at 250 K CO 2 CH 4 CO

23. RMS Simulation Inter-Comparison

24. Preliminary validation results: Temperature, water vapor, ozone (focus day October 19 th, 2007)

25. Selected IASI Nighttime Ascending Granules

26. Near AIRS Nighttime Descending Granules (~4 hour difference)

27. Standard deviation of retrievals-ECMWF IASI (blue), IASI CLEAR (red) AIRS (cyan), AIRS CLEAR (green) Standard Deviation w.r.t. ECMWF Dashed lines are NOAA Cloudy Regression and Solid lines are Physical Retrieval Using Physical QA Temperature, T(p)Water, q(p) Ozone, O3(p)

28. Bias of retrievals – ECMWF IASI (blue), IASI CLEAR (red) AIRS (cyan), AIRS CLEAR (green) BIAS w.r.t. ECMWF Dashed lines are NOAA Cloudy Regression and Solid lines are Physical Retrieval Using Physical QA

29. Towards Operational Status (April 08) Have not installed the regression derived from cloud cleared radiances. Will be installed in Jan. 2008. Have not computed tuning for AMSU & MHS (used Aqua AMSU tuning). Will be installed in Jan. 2008. Have not installed the local angle correction (needed for cloud clearing). Will be installed Jan. 2008. No attempt has been made to perform sub-pixel ILS correction. –There is an advantage to cloud clearing in that FOV’s are averaged with clearest having highest weight. –This will be studied and installed in version 2. Only quick optimization has been done. –Need to derive optimal functions & regularization parameters, Jan/Feb. 2008. –Preliminary list of channels looks good, minor changes to channel list. Pre-launch Radiative Transfer Algorithm (RTA). Post-launch RTA from UMBC, expected any time soon Empirical bias corrections, empirical noise term (to compensate for sub-pixel ILS), etc. are still very crude.

30. Our interest in participating to the START 08/pre-HIPPO campaign: Compare with in situ co-located trace gas measurements to validate and assess the performance of IASI L2 products Exchange data and products chris.barnet@noaa.gov antonia.gambacorta@noaa.gov