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. 2  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. 4  Datasets Artist’s rendition of the A-Train courtesy of NASA

5. Dataset Overview Many disparate datasets measuring at different wavelengths. – AIRS: hyperspectral, cross-track scanning mid-IR data. T profiles within 1 K/km, H 2 O profiles within 15 % / 2km. Near-global coverage on a daily basis. – CloudSat/CALIPSO: c loud 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. 5  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. 7 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 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 τ vis sensitivity of 0.005, max τ vis = 5. Combined product with CALIPSO offers detailed understanding of cloud vertical distribution height (km, MSL) cloudsat calipso  Datasets

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

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. 13  Inversion

14. Circulation Models & Radiation 14 Predict T, q, u PBL & Surface Radiation Dissipation Terms Solution of Primitive Equations Prediction of Condensation Cloud Fraction 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  Novel products

15. Cooling Rate Profile Uncertainty Perturbations in T, H 2 O, O 3 profiles lead to θ’ changes that propagate across layers. Calculation of θ’ uncertainty requires formal error propagation analysis. 15 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. 16 From Feldman, et al., 2006.  Novel products

17. CloudSat Heating/Cooling Rates 17 From Feldman, et al., In Review Radar reflectivity → CWC profiles + ECMWF T, H 2 O, O 3 → fluxes and heating rate profiles (2B-FLXHR). Uncertainty estimates not given in current (R04) release.  Novel products

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

19. Moving from OLR to Cooling Rates 19 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 20 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 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 21 FIRST AIRS  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. 22  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. 23  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. 24  Thank you for your time