1 / 14

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);

thanos
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

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

  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

More Related