JCSDA Workshop on Satellite Data Assimilation
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Transcript of JCSDA Workshop on Satellite Data Assimilation
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Project Title: Detection and Correction of Aerosol Contamination in Infrared Satellite Sea Surface Temperature Retrievals
Principal Investigators: James Cummings, Doug Westphal Naval Research Laboratory, Monterey,
CA
Co-Investigators: Jeff Hawkins, Doug May, Andy Harris
Budget: $110 FY03 $115 FY04 $150 FY05
Talk Outline: Project Objectives and Tasks
Progress to Date
Future Plans
JCSDA Workshop onSatellite Data Assimilation
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Project Project Objectives
• Detection of aerosol contamination in infrared satellite sea surface temperature (SST) retrievals using Navy Aerosol Analysis Prediction System (NAAPS) aerosol distributions.
• Correction of satellite SSTs for aerosol contamination using NAAPS aerosol products.
JCSDA Workshop onSatellite Data Assimilation
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Project TasksProject Tasks
• Collocate NAAPS optical depth forecast fields valid for the time SST retrievals are generated (Doug May, NAVOCEANO).
• Estimate SST retrieval reliability relationship to AOD content (Doug May, NAVOCEANO - Jim Cummings, NRL)
• Develop SST quality control schemes to recognize aerosol contamination (Jim Cummings, NRL).
• Correct satellite SSTs for aerosol contamination (Andy Harris, NESDIS).
• Validate NAAPS aerosol products using using independent data - improve NAAPS model (Jeff Hawkins, Doug Westphal, NRL).
JCSDA Workshop onSatellite Data Assimilation
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SST Retrievals and NAAPS Collocations at NAVOCEANO
• On going since February 2004– NAAPS AOD forecast fields obtained via ftp from NRL 4 times daily– Append AOD value closest in time and location to each MCSST retrieval
• total AOD used (sum of dust, smoke, sulfate components)• globally for N-16 and N-17 (26 Jan 2004)
– Global SST observation data file with NAAPS AOD values provided daily at 1000 UT to US GODAE server in Monterey
• New capabilities added May 2004– NAAPS AOD components plus total AOD collocated with MCSST– Cloud cleared radiances for AVHRR channels 3,4,5 saved with AOD values
JCSDA Workshop onSatellite Data Assimilation
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QC of SST Retrievals with NAAPS Collocations at NRL
• Develop discriminant analysis functions to distinguish aerosol contaminated vs. uncontaminated SST retrievals
• SST retrievals from verified Saharan dust events are used as training data sets
• Discriminant functions computed using NAAPS AOD components (dust, sulfate, smoke), AVHRR channels 3,4,5 brightness temperatures, and SST innovation from 6 hourly global 9 km SST analysis
• Provides probabilistic framework for QC – outcome is probability SST retrieval is contaminated– allows simple query capability when gathering data for assimilation
JCSDA 2nd Workshop onSatellite Data Assimilation
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Case 20050212Case 20040725
QC Discriminant Analysis Training Data Sets
• Jun 2-6, 2004
• Jul 15-17 and 20-25, 2004
• Sep 12-15, 2004
• Oct 10-13, 2004
• Nov 2-4 and 6-8, 2004
• Dec 13-15 and 28-29, 2004
• Jan 5-8, 2005
• Feb 10-13, 2005
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12:48 GMT 14:18 GMT 15:55 GMT
Case 20040725: Visible & AOD
s u n
g l
I n t
s u n g l i n t
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Resultant Composite AOD Image12:48 GMT 14:18 GMT 15:55 GMT
Composite
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NPS AOD Image Reduction: Matching NAAPS DomainFinal: 20 X 20 pixelsOriginal: 2250x1200 pixels Intermediate: 60 x 60 pixels
NAAPS Dust AOD valid: 2004072512
0.1 0.4 1.6 6.4
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MODIS (GSFC) AOD Image Reduction: Matching NAAPS DomainFinal: 20 X 20 pixelsOriginal: 2250x1200 pixels Intermediate: 60 x 60 pixels
NAAPS Dust AOD valid: 2004072512
0.1 0.4 1.6 6.4
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202 observations 231 observations
NAAPS vs NPS AOD (left) &
NAAPS vs MODIS (GSFC) AOD (right)
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AOD .25 .30 .40 .50R2: .68 .53 38 .29
AOD .50
R2: .37
202 observations
Scatter Plot: NPS vs NAAPS AOD for Case 20040725
NAAPS vs NPS AOD
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Suggested Improvements
• Cloud Filtering
• Conversion of Image data to NAAPS grid
• Include AERONET measurements
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RT Modeling of aerosol effectsConsider Merchant et al. notation…
SST = aTk k is aerosol ‘mode vector’
a is vector of retrieval coefficients
So, need to ascertain weights of mode vector for 3.7, 11 and 12 µm channels, i.e.
12
11
7.3
T
T
T
k
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Effect on brightness temperatures
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Dependency on total transmittance
Primary cause of scatter is attenuation of near-surface aerosol effect by intervening atmosphere
Can be mitigated by linear fit to total clear-sky transmittance
Different aerosol types have significantly different coefficients
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Role of air-sea temperature differenceResidual error in fit depends on air-sea temperature difference
Magnitude and range of ASTD-dependence is a function of total clear-sky transmittance
Could parameterize ∂T/∂Χ as a function of both and ASTD…
dzTT
TTBTTBTBT
zzA
ASTi
iAS
i
S
...where
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Suggested form of k-estimation
ASAS
ASAS
ASAS
TTcTTcccTTbTTbbbTTaTTaaa
12321210
11321110
7.3327.310
k
k-coefficients will be different for different aerosol types
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Conclusions from RTM studiesMerchant et al. approach requires modification in the tropospheric case because aerosols are not at the top of the atmosphere
Similar reason for greater success of Nalli & Stowe methodology in stratospheric aerosol case
Addition of total atmospheric transmittance (from NWP or e.g. SSM/I water vapor) should assist in correcting for much of the scatter
Air-sea temperature difference (NWP) useful addition
Still need discrimination of aerosol type (e.g. via NAAPS)
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Conclusions from RTM – part 2NAAPS data can permit full RT treatment of problem, but this is costly → reduced predictor approach proposed here
More work required in order to develop and validate this approach
May be desirable to adopt an interim empirical approach using satellite-derived AODs (analyses) and ancillary clear-sky transmittance, air-sea temperature differences (NCEP fields?) Beware of cross-talk between AOD & WV, ASTD
Stratospheric aerosols have much greater impact for given AOD – suggest using alternative sources (e.g. HIRS retrieval, or another analysis/product)