Dust Properties Retrieved from the AERI Observations at Niamey

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Dust Properties Retrieved from the AERI
Observations at Niamey

• Dave Turner
• Space Science and Engineering Center
• University of Wisconsin - Madison

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Overview

• Atmospheric Emitted Radiance Interferometer
• General description of instrument
• Calibration approach
• Data corrections needed
• Scaling radiosonde humidity profiles
• Dust retrieval method
• Results
• Importance of the dust microphysical and optical
  properties to the (infrared) forcing?
• Are the dust properties a function of either the
  diurnal cycle or season?

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Atmospheric Emitted Radiance Interferometer (AERI)

• Automated instrument measuring downwelling IR
  radiation from 3.3-19 µm at 0.5 cm-1 resolution
• Uses two well characterized blackbodies to
  achieve accuracy better than 1 of the ambient
  radiance
• Non-linearity correction well-known

• Data used in a wide variety of research
• During AMF NIM deployment, AERI collected 3-min
  avg every 8 min
• Knuteson et al. JAM 2004 (2 papers)

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AERI Interferometer Assembly
Front End Assembly
IR Detector Dewar with Cooler Cold Finger
Blackbodies Scene Mirror Assembly Forced Air
Inlet Rain Sensor Sun Sensor
ABB
Stirling Cooler Compressor
HBB
Bomem Interferometer
Front-end Closeout (thermal)
Optics Bench
Shock Mounts (4)
Interferometer / AERI Electronics Interface Box
Knuteson et al., JTECH, 2004
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Calibration Targets (Blackbodies)are Key to
Accurate Radiances
Emissivity gt 0.999
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Example Raw AERI Spectra
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Calibration of AERI Spectra
So the non-linearity correction is also critical!
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Updated Calibration and Correction

• Discovered after NIM deployment that the spectral
  calibration (stretch factor ? was slightly off)
• Easy correction to determine and apply via
  post-processing
• Very little uncertainty in this correction
• AERI observations are always slightly warmer than
  LBLRTM clear sky calculations
• Easiest to observe in low PWV conditions
• Observed with all AERI systems (SGP, NSA, etc),
  albeit with slightly different magnitudes
• Only important for clear sky research OR
  retrieval of properties from optically thin
  layers (like dust)
• Believe that there is a small obscuration in FOV
  or that there is some contribution from
  scattering inside interferometer
• Model this as an obscuration Fv (Nobs - Nsky) /
  (B(Teff) - Nsky) where Nsky is a clear sky LBLRTM
  calculation in dust-free conditions. Fv 0.01
  for NIM

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Impact of Corrections

• Identified 6 cases, manually identified to have
  negligible AOD, to characterize both ? and Fv
• Cases from 2 May, and 23-26 Dec
• Plan on using AMF data in Germany to verify
  adequacy

Residuals with original observations
Observed minus Calculated
Residuals with corrected observations
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Importance of Water Vapor

• Water vapor contributes a significant portion of
  the downwelling IR signal in the 8-13 µm band
• Critical that the water vapor be correctly
  specified before dust properties can be retrieved
  from the AERI observations
• Used interpolated radiosondes, where the water
  vapor profile has been scaled (height-independent
  factor) to agree with the microwave radiometer
  PWV
• This last factor is critical, as Vaisala
  radiosonde humidity observations have a
  well-known diurnal bias (daytime approximately 6
  to 8 drier than nighttime sondes for RS-90/92
  sondes)

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Dual Sonde Launch ExamplesVaisala RS-80H
1996 WVIOP
1997 WVIOP
Calibration differences between radiosondes
appear to act as height-independent scale factors
in the lower troposphere!
Revercomb et al., BAMS, 2003
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AERI / LBLRTM Results

• Using height-independent nature of the radiosonde
  calibration differences, the sonde humidity
  profiles were scaled to agree with the MWR in PWV
• LBLRTM runs were made with both the unscaled and
  scaled radiosondes
• Model runs made with the scaled results show 2
  times less variability than the unscaled sonde
  results

Turner et al., JTECH, 2003
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Using the AERI / LBLRTM Results to Look into the
Diurnal Issue

• Unscaled sonde results show significant diurnal
  differences, both in mean value and standard
  deviation
• Scaled sonde results are virtually identical both
  day and night
• Radiosondes have a considerable diurnal
  difference

Turner et al., JTECH, 2003
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Microwave Radiometer Retrievals (MWRRET)

• New algorithm to retrieve PWV and LWP from the
  ARM Microwave Radiometers (MWRs)
• Uses both a physical-iterative and an advanced
  statistical retrieval
• Physical retrieval only applied at radiosonde
  launch times
• Statistical retrieval, which uses surface P/T/U
  data to estimate the retrieval coefficients, is
  then tuned to the physical retrieval
• MWRRET algorithm accounts for biases in the
  observed brightness temperatures if not
  accounted for, then physically unreasonable LWPs
  exist
• MWRRET dataset processed for NIM and in ARM
  archive (PI products section)

Turner et al., IEEE TGRS, in press
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MIXCRA

• Developed to retrieve simultaneously the
  properties of both the liquid and ice particles
  in a mixed-phase cloud from AERI observations
• Optical depth and effective radius of both
  components
• Optimal estimation used to propagate
  uncertainties in observations and sensitivity of
  forward model to provide uncertainties of
  retrieved solution
• Turner, JAM, 2005
• Scattering properties are provided to algorithm
  via a lookup table, so I am able to retrieve dust
  properties simply by exchanging the ice/liquid
  tables with dust tables
• Different phases must have different absorption
  bands
• Modeled the dust as spherical particles with a
  lognormal size distribution
• Adequate for 8-13 µm band, not for 3-5 µm band

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Example 8-13 µm Band Fit
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More Examples

• Dust assumed to be pure kaolinite in this example
• Obs / calc fits are impressive, but not perfect
  across the bands incorporating a second mineral
  improves fit to obs
• Clear sky calcs show significant IR surface
  forcing

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Example 3-5 µm Band Fit
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IR Sensitivity to Aerosol Particle Size
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Time-series of Dust Optical Depthand Effective
Radius
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Approach

• Downwelling IR radiance is sensitive to dust
  composition, optical depth, and effective radius
• To detect differences in composition, each
  mineral must absorb in different spectral regions
• Able to distinguish between quartz, kaolinite,
  and gypsum using IR data
• Performed 6 sets of retrievals on manually
  identified cloud-free periods
• Quartz-only, kaolinite-only, gypsum-only
• Quartzkaolinite, quartzgypsum, kaolinitegypsum
• Retrieval with the best statistical fit for each
  sample was identified
• Results analyzed as function of season and local
  meteorology

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Infrared Spectral Signaturesof Different Mineral
Types
Quartz absorbs here (not Kaolinite or Gypsum)
Kaolinite absorbs here (not Quartz or Gypsum)
Gypsum and Quartz absorb here (not Kaolinite)
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Wind Direction and Water Vapor at ARM Site in
Niamey
Early monsoon
Late monsoon
Pre-monsoon
Post-monsoon
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Dust Optical Depth and Composition Distribution
Distribution for entire year
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Dust Optical Depth and Composition Distribution
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Distribution of Effective Radius Per Period
Kaolinite
Quartz and Gypsum
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Kaolinite Fraction forEach of the Periods

• Kaolinite fraction defined as Fk ?k / ?total

• Fk significantly different for KaoliniteGypsum
  in the pre-monsoon and post-monsoon periods

• Quartz becomes more dominant (relatively) during
  the early- and late-monsoon periods

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Summary

• Retrieved infrared optical depth, effective
  radius, and dust composition from AERI
  observations at NIM
• Only used observations from 8-13 µm, and assumed
  the dust particles to be spherical
• Dust assumed to be kaolinite, quartz, gypsum, or
  some combination of any two
• Only applied to observations that were manually
  identified as cloud free
• Kaolinite is the dominant component of dust for
  entire year, with 66 of observations are best
  satisfied by a combination of kaolinite gypsum
  and 23 best satisfied with a combination of
  kaolinite quartz
• Kaolinite fraction changes significantly during
  the 4 periods in 2006. The changes are likely
  associated with land-use changes associated with
  the precipitation cycle

dturner_at_ssec.wisc.edu