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Remote Sensing Using NASA EOS A-Train Measurements

Remote Sensing Using NASA EOS A-Train Measurements. Presentation at Sonoma Technology, Inc. Monday, June 16, 2008 Daniel R. Feldman Caltech Department of Environmental Science and Engineering. Presentation Outline. Overview of satellite-based remote sensing.

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Remote Sensing Using NASA EOS A-Train Measurements

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  1. Remote Sensing Using NASA EOS A-Train Measurements Presentation at Sonoma Technology, Inc. Monday, June 16, 2008 Daniel R. Feldman Caltech Department of Environmental Science and Engineering

  2. Presentation Outline • Overview of satellite-based remote sensing. • Discussion of several EOS A-Train datasets. • AIRS, CloudSat, CALIPSO. • Products derived from the datasets. • Standard retrieval products. • Radiative heating/cooling rate profiles. • The next generation of instrumentation. • Conclusions. • Outline

  3. The Power of Remote Sensing • With measurements at different wavelengths: • Distribution of trace gases. • Aerosols and cloud properties. • Energy balance/exchange. • From satellite-based measurements, we obtain a comprehensive, quantitative picture used to (in)validate earth science hypotheses. • Measurements have implications for policy. • Remote Sensing & Society

  4. The EOS A-Train Data Age • The polar-orbiting EOS A-Train flotilla presents a voluminous dataset describing the earth’s lower atmosphere: • Aqua platform operational for ~ 6 years. • CloudSat and CALIPSO platforms operational for ~ 2 years. • This data can be very scientifically useful in the context of measurement/ model comparisons. Artist’s rendition of the A-Train courtesy of NASA • Datasets

  5. Dataset Overview • Many disparate datasets measuring at different wavelengths. • AIRS: hyperspectral, cross-track scanning mid-IR data. • T profiles within 1 K/km, H2O profiles within 15 % / 2km. • Near-global coverage on a daily basis. • CloudSat/CALIPSO: cloud water content profiles from radar/lidar. • 50% CWC uncertainty / 240 m. • Near-global coverage on a bi-weekly basis. • Other instruments in the A-Train shed light on current earth science questions. • Datasets

  6. AIRS Instrument • Grating spectrometer measures 3.7 to 15.4 μm (650-2700 cm-1). • Cross-track scanning mirror yields 90 footprints in 2.7 sec. • Space & BB view for calibration. • Each footprint produces 2378 radiance measurements.. • 15 km footprint. • Collocated 15-channel passive microwave sounder at 45 km footprint. From JPL AIRS website • Datasets

  7. AIRS Achievements • Unprecedented view of temperature, water vapor, and carbon dioxide distribution on a bi-weekly basis. Avg Trop Relative Humidity From AIRS, Dec-Feb 2002-2005 • Datasets

  8. CloudSat Overview • CloudSat • Nadir-pointing 94-GHz radar • Cloud-profiles at ~240 m vertical resolution • Horizontal resolution ~1.4 km • Sensitivity of -31 dBZ, 80 dBZ dynamic range • Datasets

  9. CALIPSO Overview cloudsat height (km, MSL) calipso • CALIPSO: Cloud-Aerosol LIdar with Orthogonal Polarization • Nadir-pointing 2-channel (532 nm and 1064 nm) lidar. • Vertical resolution ~30 m. • Horizontal resolution ~100 m. • Min τvis sensitivity of 0.005, max τvis = 5. • Combined product with CALIPSO offers detailed understanding of cloud vertical distribution • Datasets

  10. CloudSat/CALIPSO Achievements • Unprecedented global coverage of cloud-profile distribution on a seasonal basis. JJA zonally averaged distribution of cloudiness from one of the IPCC FAR climate models , from Mace and Klein. JJA zonally averaged distribution of cloudiness derived from the CloudSat 2B-GEOPROF product. • Datasets

  11. Interpreting Measurements • Raw measurements are inverted into higher level products. • Inversion requires understanding of radiative transfer. • Planck emission. • Absorption features: line strengths, broadening/continuum. • Optical properties of scatterers. • Mechanics of integrating fundamental eqn. of RT. From JARS RT tutorial From Goody & Yung, Ch 1 • Inversion

  12. Inversion of Measurements • With a working RT model, profile quantities can be derived from the measurements. • However, problem is ill-conditioned => methods required to produce mathematical stability. From Boesch, et al, 2006 • Inversion

  13. Derivation of Retrieval Products • NASA satellite instrument data processing protocols specify several levels of products: • L1A: raw measurements • L1B: geolocated, calibrated measurements • L2: retrieved from L1B data, forward model, etc. • L3: gridded, averaged L2 products • Higher-level products should be utilized with care • Meaningful scientific analysis requires full tabulation of the retrieval deficiencies. • Inversion

  14. Circulation Models & Radiation • Stratosphere in approximate radiative equilibrium → SW heating ≈ IR cooling. • In troposphere, IR cooling>SW heating. • Circulation model performance requires proper treatment of radiative energy exchange. Flowchart of model calculation for an isolated timestep from Kiehl, Ch. 10 of Trenberth, 1992 Predict T, q, u PBL & Surface Prediction of Condensation Cloud Fraction Radiation Dissipation Terms Solution of Primitive Equations • Novel products

  15. Cooling Rate Profile Uncertainty • Perturbations in T, H2O, O3 profiles lead to θ’ changes that propagate across layers. • Calculation of θ’ uncertainty requires formal error propagation analysis. From Feldman, et al., 2008. • Novel products

  16. Retrieval of Cooling Rates • Many products derived from the satellite instrument measurements through retrievals. • Many different approaches to retrieving quantities from measurements. From Feldman, et al., 2006. • Novel products

  17. CloudSat Heating/Cooling Rates • Radar reflectivity → CWC profiles + ECMWF T, H2O, O3 → fluxes and heating rate profiles (2B-FLXHR). • Uncertainty estimates not given in current (R04) release. From Feldman, et al., In Review • Novel products

  18. Net Heating from CloudSat/CALIPSO From Feldman, et al., In Review • Novel products

  19. Moving from OLR to Cooling Rates • Qualitative agreement between measurement/model mean OLR values • Different cooling rate profiles, though OLR, cooling rates are closely related. From Feldman, 2008 • Novel products

  20. CLARREO: The Next Generation Fundamental differences between measurements and climate models and in key feedback descriptors for IPCC FAR models. Long-term trend characterization & attribution from satellite instruments is very difficult. NRC 2007 Decadal Survey recommended the development of an instrument that is NIST-calibrated in orbit. CLimate Absolute Radiance and Refractivity Observatory (CLARREO) will have high spectral resolution in the visible, mid- and far-IR. • Future missions

  21. FIRST: Far Infrared Spectroscopy of the Troposphere AIRS AIRS • FIRST is a test-bed for CLARREO • NASA IIP FTS w/ 0.6 cm-1 unapodized resolution, ±0.8 cm scan length • 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 • Future missions

  22. Towards CLARREO • CLARREO, as a future NASA mission, is currently being studied by several institutions. • Exacting engineering requirements to achieve NIST calibration. • Test-bed instrumentation under development • FIRST provides a comprehensive description of the far-infrared which is relevant to CLARREO development. • Establishing climate trends from satellite data and attributing causes to these trends is within reach. • With the establishment of a benchmark, climate model discrepancies can be rectified. • Future missions

  23. Conclusions • Satellite-based remote sensing is a powerful tool for earth science. • Proven utility to society for nearly almost 40 years. • EOS A-Train data contain information about many aspects of the earth-atmosphere system: • Temperature profile, trace gas constituents, cloud profiles. • Description of fields that are of direct relevance to weather and climate model evaluation (e.g., radiative energy exchange). • The next generation of satellite instruments will be designed not just for process and trend description. • Climate models will directly motivate mission specifications. • Conclusions

  24. Acknowledgements • NASA Earth Systems Science Fellowship, grant number NNG05GP90H. • Yuk Yung Radiation Group: Jack Margolis, Vijay Natraj, King-Fai Li, & Kuai Le, Xi Zhang, Xin Guo • George Aumann, Duane Waliser, Jonathan Jiang, and Hui Su from JPL. • Tristan L’Ecuyer from CSU. • Marty Mlynczak and Dave Johnson of NASA LaRC. • Xianglei Huang from U. Michigan. • Yi Huang from Princeton. • AIRS, CloudSat, and CALIPSO Data Processing Teams. • Thank you for your time

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