uses of airs data for weather, climate and atmospheric composition

National Aeronautics and Space Administration

Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadena, California

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Uses of AIRS Data for Weather, Climate and Atmospheric Composition

Eric FetzerJet 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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AIRS Project Overview

Salient Features

Spacecraft – Instruments:EOS Aqua – AIRS/AMSU/HSBLaunch Date: May 4, 2002Launch Vehicle: Boeing Delta II, Intermediate ELV

Mission Life: 6 yearsCategory: 2Risk Class: AInstruments Operational: August 2002Team 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



AIRS Key Products and Science Areas

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Atmospheric Water VaporOzone

Cloud Properties

Dust

CO

EmissivityMethane

Atmospheric Temperature

CO2

Greenhouse Gas Forcing

Cloud and Water Vapor Processes

SO2



~400 AIRS Peer-Reviewed Publications

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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?

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AIRS Improves Weather Operations and Research

SAL 4SAL 3SAL 2

AEW 1

Polar 1

SAL 1Irene

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Polar 2

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Mixing Ratio (g kg-1)

Pre

ssu

re (

hP

a)

GPS Sonde

Jordan

AIRS

NCEP Operational Improvement

Regional Forecast Improvement

Pressure RainfallNOAA Hurricane Center

Saharan Air Layer Hurricane Suppression

AIRS Research Validates Models

6 hrs on 6 day forecast

J. Fu, U of Hawaii

J. Dunion, NOAAB. Zavodsky, NASA SPoRT

J. LeMarshall, JCSDA



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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).

1. 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.

2. 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

3. 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

4. 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)



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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

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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]



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

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Information from AIRS Retrievals in Cloudy RegionsImproves Tropical Cyclone Forecast

5 of the 7 forecasts’error at landfall is

less than 50km

No CycloneGood landfall locationGood landfall timing

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/

Major Impact to Tropical Cyclone Nargis Hindcast



• Full utilization of existing data. Specifically:– Constituents

• Sulfur dioxide (volcanoes).• Dust (aerosols).• HDO (hydrologic cycle)• Ammonia (nitrogen cycle; aerosols).• Etc…

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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.




• Improved horizontal resolution.

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Horizontal resolution needs driven by rapid improvements in global models.

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Higher Spatial Resolution will Improve Process Studies of Clouds and

Water Vapor

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Current (AIRS)

Water Vapor

15 km

50 km

Bretherton et al (2004)

Sub Gridscale Resolution Needed to Constrain Cloud Physics

Parameterizations



Expect Improvement in Boundary Layer Accuracy and Yield over

Land with Higher Spatial Resolution

Joel Susskind, 2008

Land cases limited by inadequate surface emissivity knowledge.

One Month August 2005 Cloud-

cleared

AIRS(Hyperspectra

l)

One Month August August 2003

Cloud-Free Scenes

MODIS (Broadband)

50x50 km

ν = 1095 cm-1

5x5 km

ν = 1205 cm-1

S. Hook (JPL)

T. Pagano (JPL)

AIRS Yield and Accuracy Degrade Near Land

Surface



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



IR sounding and cumulus boundary layer vertical structure: AIRS and

RICO experiment

19AIRS Support Product provides realistic info on vertical structure



• Improved spectral coverage– Carbon monoxide– Methane 3.33 micron band.

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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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uses of airs data for weather, climate and atmospheric composition

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