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Uses of AIRS Data for Weather, Climate and Atmospheric Composition

Uses of AIRS Data for Weather, Climate and Atmospheric Composition. Eric Fetzer Jet Propulsion Laboratory / California Institute of Technology Satellite Hyperspectral Sensor Workshop, University of Miami March 29 -31, 2011. In Memory of Dr. Mous Chahine AIRS Science Team Leader and Friend.

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Uses of AIRS Data for Weather, Climate and Atmospheric Composition

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  1. Uses of AIRS Data for Weather, Climate and Atmospheric Composition Eric Fetzer Jet Propulsion Laboratory / California Institute of Technology Satellite Hyperspectral Sensor Workshop, University of Miami March 29 -31, 2011

  2. In Memory of Dr. Mous ChahineAIRS Science Team Leader and Friend

  3. AIRS Project Overview Salient Features Spacecraft – Instruments: EOS Aqua – AIRS/AMSU/HSB Launch Date: May 4, 2002 Launch Vehicle: Boeing Delta II, Intermediate ELVMission Life: 6 years Category: 2 Risk Class: A Instruments Operational: August 2002 Team Leader: Moustafa T. Chahine • Science • Improve Weather Prediction • AIRS data are assimilated into the operational forecast system at NWP centers worldwide • AIRS Improves Forecast and Tropical Cyclone Prediction • Improve Climate Prediction • Measure the Water Cycle, Temperature Trends, Dust and Cloud Properties • Measure Important Trace Gases: CO, O3, CO2, CH4, SO2

  4. AIRS Key Products and Science Areas Cloud and Water Vapor Processes CO Cloud Properties Atmospheric Temperature Greenhouse Gas Forcing Atmospheric Water Vapor Dust Ozone Methane CO2 SO2 Emissivity

  5. ~400 AIRS Peer-Reviewed Publications

  6. Question 1 • How are current hyperspectral IR sounders such as the NASA JPL AIRS and CNES & EUMETSAT IASI used? • What are the deficiencies? • What improved information is needed by the user?

  7. Polar 1 Polar 2 Irene 10 15 SAL 1 SAL 3 SAL 2 20 SAL 4 5 1 AEW 1 AIRS Improves Weather Operations and Research AIRS Research Validates Models NCEP Operational Improvement 6 hrs on 6 day forecast J. LeMarshall, JCSDA J. Fu, U of Hawaii Regional Forecast Improvement NOAA Hurricane Center Saharan Air Layer Hurricane Suppression Rainfall Pressure J. Dunion, NOAA B. Zavodsky, NASA SPoRT

  8. AIRS Finds Biases in Climate Model Moisture & Temperature • AIRS finds major climate models are too dry below 800 mb in the tropics, and too moist between 300 mb and 600 mb especially in the extra-tropics. (Pierce, John, Gettleman); too cold above. • Radiance biases of opposite signs in different spectral regions suggests that the apparent good agreement of a climate model's broadband longwave flux and total water with observations may be due to a fortuitous cancellation of spectral errors (Huang). • Pierce D. W., T. P. Barnett, E. J. Fetzer, P. J. Gleckler (2006), Three-dimensional tropospheric water vapor in coupled climate models compared with observations from the AIRS satellite system, Geophys. Res. Lett., 33, L21701, doi:10.1029/2006GL027060. • John, V.O. and Soden, B. J., Temperature and humidity biases in global climate models and their impact on climate feedbacks, Geophys.Res. Lett., 34, L18704, doi:10.1029/2007GL030429 • Gettleman, Collins, Fetzer, Eldering, Irion (2006), “Climatology of Upper-Tropospheric Relative Humidity from the Atmospheric Infrared Sounder and Implications for Climate”, J. Climate, 19, 6104-6121. DOI: 10.1175/JCLI3956.1 • Huang, Y., Ramaswamy, V., Huang, X.L., Fu, Q., Bardeen, C., A strict test in climate modeling with spectrally resolved radiances: GCM simulation versus AIRS observations, Geophys. Res. Lett., 2007, 34, 24, L24707 Water Vapor Vertical Climatology(Pierce, Scripps) Outgoing Longwave Radiation (Huang, Univ. of Michigan)

  9. AIRS H2O Data used as “Truth” toImprove Parameterizations in Climate Models • Tim Barnett: Scripps, UCSD • Coupled Climate Models show >50% bias errors in H2O vapor. Models worst at mid altitude and mid latitude. • Andrew Gettleman: NCAR • AIRS can provide insight on climate forcings • Variability not well reproduced in GCM/CAMS • Greenhouse effect appears to increase with SST • Water vapor feedback positive: but not as positive as constant RH would assume • Andrew Dessler: Texas A&M • Simple trajectory model with fixed RH limit does a good job of reproducing AIRS annual average water vapor • Model shows that dehydration of mid-troposphere air occurs in three latitude bands

  10. Atmospheric Composition: Influence of Madden-Julian Oscillation on AIRS CO2 Contour line: TRMM Rain AIRS CO2 data are modulated by the Madden-Julian Oscillation. The peak-to-peak amplitude of the MJO signal is ~ 1 ppm. Li et al. [PNAS, 2010]

  11. Current AIRS Status-- What are the deficiencies?-- What improved information is needed by the user? • Full utilization of existing data. Specifically: • Cloudy scenes (and cloud information) into forecasts. • Constituents • Sulfur dioxide (volcanoes). • Dust (aerosols). • HDO (hydrologic cycle) • Ammonia (nitrogen cycle; aerosols). • Etc… • Improved spatial resolution • Improved spectral coverage

  12. Information from AIRS Retrievals in Cloudy RegionsImproves Tropical Cyclone Forecast Major Impact to Tropical Cyclone Nargis Hindcast 5 of the 7 forecasts’ error at landfall is less than 50km AIRS Vis Image: Nargis, May 1, 2008 Reale, O., W. K. Lau, J. Susskind, E. Brin, E. Liu, L. P. Riishojgaard, M. Fuentes, and R. Rosenberg (2009), AIRS impact on the analysis and forecast track of tropical cyclone Nargis in a global data assimilation and forecasting system, Geophys. Res. Lett., 36, L06812, doi:10.1029/2008GL037122.http://www.agu.org/journals/gl/gl0906/2008GL037122/ Good landfall location Good landfall timing No Cyclone

  13. Current AIRS Status-- What are the deficiencies?-- What improved information is needed by the user? • Full utilization of existing data. Specifically: • Constituents • Sulfur dioxide (volcanoes). • Dust (aerosols). • HDO (hydrologic cycle) • Ammonia (nitrogen cycle; aerosols). • Etc… Gangale, G., A. J. Prata, and L. Clarisse (2009), The infrared spectral signature of volcanic ash determined from high-spectral resolution satellite measurements, Remote Sensing of Environment, 114(2), 414-425.

  14. Current AIRS Status-- What are the deficiencies?-- What improved information is needed by the user? • Improved horizontal resolution.

  15. Horizontal resolution needs driven by rapid improvements in global models.

  16. Higher Spatial Resolution will Improve Process Studies of Clouds and Water Vapor Water Vapor Sub Gridscale Resolution Needed to Constrain Cloud Physics Parameterizations Current (AIRS) 50 km 15 km Bretherton et al (2004) 16

  17. Expect Improvement in Boundary Layer Accuracy and Yield over Land with Higher Spatial Resolution AIRS Yield and Accuracy Degrade Near Land Surface AIRS (Hyperspectral) One Month August 2005 Cloud-cleared 50x50 km ν = 1095 cm-1 T. Pagano (JPL) MODIS (Broadband) One Month August August 2003 Cloud-Free Scenes 5x5 km ν = 1205 cm-1 Land cases limited by inadequate surface emissivity knowledge. S. Hook (JPL) Joel Susskind, 2008

  18. Fine Scale Structure Cumulus Boundary Layers Needed for Improved Models Small values of cloud cover ~ 5-30% Stevens et al (2006) Low cloud cover, deeper boundary layers and smoother vertical structures   More detailed information from IR/MW sounding

  19. IR sounding and cumulus boundary layer vertical structure: AIRS and RICO experiment AIRS Support Product provides realistic info on vertical structure

  20. Current AIRS Status-- What are the deficiencies?-- What improved information is needed by the user? • Improved spectral coverage • Carbon monoxide • Methane 3.33 micron band.

  21. Conclusions • Much work has been done with AIRS • Reflected in ~400 publications in • Weather. • Climate. • Atmospheric Composition. • More to do • Exploit cloudy scenes in forecasts. • Extract more information about structure and composition. • The Future • Current and planned sounders will not keep up with rapid improvements in model resolution.

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