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.
• 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
2
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
The EOS A-Train Data Age
Artist’s
rendition of
the A-Train
courtesy of
NASA
• 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.

Datasets
4
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
5
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
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
7
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
Vert. Res.
Horiz. Res.

Datasets
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.
height (km, MSL)
• Combined product with CALIPSO offers detailed
understanding of cloud vertical distribution

Datasets
cloudsat
calipso
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.

Datasets
JJA zonally averaged distribution of
cloudiness derived from the CloudSat 2BGEOPROF product.
10
Interpreting Measurements
• Raw measurements are
inverted into higher level
products.
• Inversion requires
understanding of radiative
transfer.
From JARS RT tutorial
– Planck emission.
– Absorption features: line
strengths,
broadening/continuum.
– Optical properties of scatterers.
– Mechanics of integrating
fundamental eqn. of RT.
From Goody & Yung, Ch 1

Inversion
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
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
13
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
14
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.
n
 z   z 
covxi , x j 

x

x
j 1
i
j
n
 z   
2
i 1
From Feldman, et al., 2008.

Novel products
15
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
16
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
17
Net Heating from CloudSat/CALIPSO
From Feldman, et al., In Review

Novel products
18
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
19
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
20
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

Future missions
AIRS
FIRST
AIRS
21
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
22
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
23
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
24