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![Page 1: Remote Sensing Using NASA EOS A-Train Measurements Presentation at Sonoma Technology, Inc. Monday, June 16, 2008 Daniel R. Feldman Caltech Department of.](https://reader030.fdocuments.us/reader030/viewer/2022032704/56649d5e5503460f94a3e1c7/html5/thumbnails/1.jpg)
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
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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 OutlineOutline
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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 & SocietyRemote Sensing & Society
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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 DatasetsDatasets
Artist’s rendition of the A-Train courtesy of NASA
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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.
5 DatasetsDatasets
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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
DatasetsDatasets
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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
DatasetsDatasets
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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
Horiz. Res.
Vert. Res.
DatasetsDatasets
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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 τvis sensitivity of 0.005, max τvis = 5.
• Combined product with CALIPSO offers detailed understanding of cloud vertical distribution
heig
ht (
km,
MS
L) cloudsat
calipso
DatasetsDatasets
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CloudSat/CALIPSO Achievements
10 DatasetsDatasets
• 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.
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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
InversionInversion
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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
InversionInversion
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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.
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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 productsNovel products
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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.
15
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1 1
2 ,cov
From Feldman, et al., 2008.
Novel productsNovel products
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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 productsNovel products
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CloudSat Heating/Cooling Rates
17From Feldman, et al., In Review
• Radar reflectivity → CWC profiles + ECMWF T, H2O, O3 → fluxes and heating rate profiles (2B-FLXHR).
• Uncertainty estimates not given in current (R04) release.
Novel productsNovel products
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Net Heating from CloudSat/CALIPSO
18From Feldman, et al., In Review Novel productsNovel products
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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 productsNovel products
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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 Future missions
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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
FIRSTAIRS AIRS
Future missions Future missions
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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 Future missions
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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 ConclusionsConclusions
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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 timeThank you for your time