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Determination of atmospheric temperature, water vapor, and heating rates from mid- and far- infrared hyperspectral measurements AGU Fall Meeting, Wednesday,

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Presentation on theme: "Determination of atmospheric temperature, water vapor, and heating rates from mid- and far- infrared hyperspectral measurements AGU Fall Meeting, Wednesday,"— Presentation transcript:

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 2

3 The Far-Infrared Frontier 3 Current EOS A-Train spectrometers measure 3.4 to 15 μm, don’t measure 15-100 μm Far-IR, through H 2 O rotational band, affects OLR, tropospheric cooling rates Far-IR processes inferred from other spectral regions Mid-IR, Microwave, Vis/NIR Interaction between UT H 2 O and cirrus clouds requires knowledge of both Currently inferred from measurements in other spectral regions Figures derived from Mlynczak et al, SPIE, 2002  Motivation No spectral measurements to the right of line

4 FIRST: Far Infrared Spectroscopy of the Troposphere NASA IIP FTS w/ 0.6 cm -1 unapodized 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 instrument 4 FIRST AIRS

5 Retrieval Sensitivity Test Flow Chart 5 Model AtmosphereA priori Atmospheric State) Random Perturbations Synthetic Measurement RTM + Noise A priori spectrum RTM Retrieval algorithm A priori uncertainty Analyze retrieved state, spectra, and associated statistics  Sensitivity tests T(z) H 2 O(z) O 3 (z) CWC(z) CER(z) From Rodgers, 2000

6 Clear-Sky Retrieval Test 6  Sensitivity tests AIRS and FIRST T(z) retrievals comparable. FIRST better than AIRS in H 2 O(z) retrievals 200-300 mbar. Residual signal in far IR seen 100-200 cm -1 → low NeDT critical

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. 7  Heating Rates A priori σ(T(z)) = 3 K σ(H 2 O(z)) = 20% σ(O 3 (z)) = 20% A posteriori σ(T(z)) ≈ 1 K σ(H 2 O(z)) ≈ 10% σ(O 3 (z)) ≈ 10%

8 Extrapolating Far-IR with Clouds Retrieval of T(z), H 2 O(z), CWC(z), CER(z) difficult with AIRS spectra AIRS H 2 O 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 8  Clouds

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

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

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

12 Climate Model Considerations 12 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 H 2 O retrieval relative to AIRS – Implied cooling rate information difference is small. Extrapolating far-IR channels with cirrus cloud good for T b ~ 220 K, ok for T b ~ 300 K, difficult for T b ~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. 13  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 14  Thank you for your time


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