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AGU Fall Meeting, Wednesday, December 12, 2007 GC34A-02 D.R. Feldman (Caltech);

Determination of atmospheric temperature, water vapor, and heating rates from mid- and far- infrared hyperspectral measurements. AGU Fall Meeting, Wednesday, December 12, 2007 GC34A-02 D.R. Feldman (Caltech); K.N. Liou (UCLA); Y.L. Yung (Caltech);

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AGU Fall Meeting, Wednesday, December 12, 2007 GC34A-02 D.R. Feldman (Caltech);

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  1. Determination of atmospheric temperature, water vapor, and heating rates from mid- and far- infrared hyperspectral measurements AGU Fall Meeting, Wednesday, December 12, 2007 GC34A-02 D.R. Feldman (Caltech); K.N. Liou (UCLA); Y.L. Yung (Caltech); D. G. Johnson (LaRC); M. L. Mlynczak (LaRC) http://www.gps.caltech.edu/~drf/misc/agu2007

  2. Presentation Outline • Motivation for studying the far-infrared • FIRST instrument description • Sensitivity tests of mid-IR vs far-IR capabilities • Clear-sky • Cloudy-sky • Multi-instrument data comparison • Climate model considerations • Conclusions • Outline

  3. Current EOS A-Train spectrometers measure 3.4 to 15 μm, don’t measure 15-100 μm Far-IR, through H2O rotational band, affects OLR, tropospheric cooling rates Far-IR processes inferred from other spectral regions Mid-IR, Microwave, Vis/NIR Interaction between UT H2O and cirrus clouds requires knowledge of both Currently inferred from measurements in other spectral regions The Far-Infrared Frontier No spectral measurements to the right of line Figures derived from Mlynczak et al, SPIE, 2002 • Motivation

  4. FIRST: Far Infrared Spectroscopy of the Troposphere AIRS AIRS • NASA IIP FTS w/ 0.6 cm-1unapodized resolution, ±0.8 cm scan length • Multilayer beamsplitter • Germanium on polypropylene • Good performance over broad spectral ranges in the far-infrared • 5-200 μm (2000 – 50 cm-1) spectral range • NeDT goal ~0.2 K (10-60 μm), ~0.5 K (60-100 μm) • 10 km IFOV, 10 multiplexed detectors • Balloon-borne & ground-based observations FIRST • FIRST instrument

  5. Retrieval Sensitivity TestFlow Chart Random Perturbations Model Atmosphere A priori Atmospheric State) RTM RTM + Noise A priori uncertainty A priori spectrum Synthetic Measurement Retrieval algorithm T(z) H2O(z) O3(z) CWC(z) CER(z) Analyze retrieved state, spectra, and associated statistics From Rodgers, 2000 • Sensitivity tests

  6. Clear-Sky Retrieval Test • AIRS and FIRST T(z) retrievals comparable. • FIRST better than AIRS in H2O(z) retrievals 200-300 mbar. • Residual signal in far IR seen 100-200 cm-1 → low NeDT critical • Sensitivity tests

  7. Clear-Sky Heating Rates • Spectra provide information about fluxes/heating rates • Error propagation (Taylor et al, 1994; Feldman et al, In Review) can be used to determine heating rate uncertainty. • Heating rate error for scenes with clouds is generally higher. A priori σ(T(z)) = 3 K σ(H2O(z)) = 20% σ(O3(z)) = 20% A posteriori σ(T(z)) ≈ 1 K σ(H2O(z)) ≈ 10% σ(O3(z)) ≈ 10% • Heating Rates

  8. Extrapolating Far-IR with Clouds • Retrieval of T(z), H2O(z), CWC(z), CER(z) difficult with AIRS spectra • AIRS H2O channels correlate with far-IR channels • Low BT channels from 6.3 μm band ≈ low BT channels in far-IR • High BT channels scale from mid- to far-IR • For tropics, channels with BT 250-270 K (emitting ~ 5-8 km) are complicated • Broadband IR radiance can be computed from mid-IR channels • Clouds

  9. Test Flight on September 18, 2006:Ft, Sumner NM AQUA MODIS L1B RGB Image FIRST Balloon AIRS Footprints CloudSat/CALIPSO Footprint Track • Test flight

  10. FIRST and AIRS Cloud Signatures • Instrument collocation • FIRST balloon-borne spectra • AIRS • MODIS • FIRST residuals are consistent with clouds ~ 5 km, CER ~ 6 μm Cloud Detected ! • Test flight

  11. CloudSat/CALIPSO signals • CloudSat and CALIPSO near collocation • No signal from CloudSat • CALIPSO signal consistent with FIRST residual • Test flight

  12. Climate Model Considerations • Climate models produce fields that specify mid- & far-IR spectra. • RT in Far-IR requires state and spectral space treatment. • Far-IR climate model analysis requires more far-IR data • Far-IR extrapolation should retain physical basis and be verified with measurements. • Agreement with CERES is a partial verification and presents a non-unique checksum • Future work required to assess how mid- and far-IR spectra impart information towards far-IR climate model processes. • Model evaluation

  13. Conclusions • AIRS measures mid-IR, but far-IR is not covered by A-Train spectrometers. • FIRST describes far-IR but limited spectra are available. • FIRST clear-sky T retrievals comparable, improved UT H2O retrieval relative to AIRS • Implied cooling rate information difference is small. • Extrapolating far-IR channels with cirrus cloud good for Tb ~ 220 K, ok for Tb ~ 300 K, difficult for Tb ~250-270 K. • Multi-instrument analysis with A-Train facilitates comprehensive understanding of FIRST test flight spectra. • AIRS mid-IR spectra can validate climate models, but far-IR should not be neglected. • Conclusions

  14. Acknowledgements • NASA Earth Systems Science Fellowship, grant number NNG05GP90H. • Yuk Yung Radiation Group: Jack Margolis, Vijay Natraj, King-Fai Li, & Kuai Le • George Aumann and Duane Waliser from JPL • Xianglei Huang from U. Michigan and Yi Huang from Princeton • AIRS, CloudSat, and CALIPSO Data Processing Teams • Thank you for your time

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