Transcript Document 7463963

10th GHRSST Science Team Meeting
1-5 June 2009, Santa Rosa, CA
The SST Quality Monitor (SQUAM)
Alexander “Sasha” Ignatov*, Prasanjit Dash*,
John Sapper**, Yury Kihai*
NOAA/NESDIS
*Center for Satellite Applications & Research (STAR)
**Office of Satellite Data Processing & Distribution (OSDPD)
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NESDIS Operational AVHRR SST Products
 Heritage Main Unit Task (MUT)
- 1981 - present (McClain et al., 1985; Walton et al., 1998).
 New Advanced Clear-Sky Processor for Oceans (ACSPO)
- May 2008 – present
Objective of the SST Quality Monitor (SQUAM)
http://www.star.nesdis.noaa.gov/sod/sst/squam/
Employ L4 SSTs (Reynolds, RTG, OSTIA, ODYSSEA, ..) to
 Evaluate MUT and ACSPO SST products in near-real time
for self-, cross-platform and cross-product consistency
 Identify product anomalies & help diagnose their causes (e.g.,
sensor malfunction, cloud mask, or SST algorithm)
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Customarily, satellite SSTs are
validated against in situ SSTs
However, in situ SSTs have limitations
 They are sparse and geographically biased (cover retrieval
domain not fully and non-uniformly).
 Have non-uniform and suboptimal quality (often
comparable to or worse than satellite SSTs).
 Not available in near real time in sufficient numbers to
cover the full geographical domain and retrieval space.
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AVHRR SST
MetOp-A GAC, 3 January 2008 (Daytime)
Heritage MUT SST product
ACSPO SST product
SST imagery is often inspected visually for quality and artifacts.
Large-scale SST background dominates making it not easy to discern
“signal” from “noise”.
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Removing large-scale SST background
(daily 0.25º Reynolds) emphasizes ‘noise’
Heritage MUT SST product
ACSPO SST product
Mapping deviations from a global reference field constrains the SST
“signal” and emphasizes “noise”.
This helps reveal artifacts in SST product (cold stripes at swath edges).
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View angle dependence of
‘MUT - daily Reynolds SST’ (NOAA-17)
The SQUAM diagnostics helped uncover a bug in the
MUT SST which was causing across-swath bias >0.7K.
After correction, bias reduced to ~0.2K
and symmetric with respect to nadir.
Such ‘retrieval-space’ dependent biases are difficult to uncover
and quantify using customary validation against in situ data,
which do not fully cover the retrieval space.
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Use global L4 SST products to
quantitatively evaluate satellite SST
 Satellite & reference SSTs are subject to near-Gaussian errors
TSAT = TTRUE + εSAT ; εSAT = N(μsat,σsat2)
TREF = TTRUE + εREF; εREF = N(μref,σref2)
where μ’s and σ’s are global mean and standard deviations of ε‘s
 The residual is distributed near-normally
ΔT = TSAT - TREF = εSAT - εREF; εΔT = N(μΔT,σΔT2)
where μΔT = μsat - μref ; σΔT2 = σsat2 + σref2 (if εSAT and εREF are
independent)
 If TREF = Tin situ, then it is customary ‘validation’. If (μref, σref) are
comparable to (μin situ, σin situ), and if εSAT and εREF are not too
strongly correlated, then TREF can be used to monitor TSAT
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Global Histograms of TSAT - TREF
(Nighttime MUT)
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Histogram of SST residual
Reference SST: In situ
30 days of data: ~6,500 match-ups with in situ SST
Median = -0.04 K; Robust Standard Deviation = 0.27 K
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Histogram of SST residual
Reference SST: OSTIA
8 days of data: ~483,500 match-ups with OSTIA SST
Median = 0.00 K; Robust Standard Deviation = 0.30 K
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Histogram of SST residual
Reference SST: Daily Reynolds
8 days of data: ~483,700 match-ups with daily Reynolds SST
Median = +0.08 K; Robust Standard Deviation = 0.44 K
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Observations
from global histograms analyses
 Global histograms of TSAT - TREF are close to Gaussian,
against all TREF including Tin situ
 Normal distribution is characterized by location
(median) and scale (robust standard deviation, RSD)
 Reduced number/magnitude of outliers with respect to
L4 TREF compared to Tin situ
 For some TREF (e.g., OSTIA), VAL statistics is closer to
Tin situ than for others (e.g., Reynolds).
*
More histograms (ACSPO/MUT, day/night, other platforms /
reference SSTs) are found at SQUAM page
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Time Series
Global Median Biases of (TSAT - TREF)
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Global Median Biases
TSAT – Tin situ
 1 data point = 1 month match-up with in situ
 Median Bias within ~0.1 K (except for N16 - sensor problems)
 MetOp-A and N17 fly close orbits but show a cross-platform bias of
~0.1 K
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Global Median Biases
TSAT – TOSTIA
 1 data point = 1 week match-up with OSTIA SST
 Patterns reproducible yet crisper (finer temporal resolution)
 Cross-platform biases: Slightly differ from Val (diurnal cycle)
 OSTIA artifacts observed in early period (2006-2007)
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Global Median Biases
TSAT – TReynolds
 1 data point = 1 week match-up with Reynolds SST
 Patterns reproducible but noisier than with respect to OSTIA
 Artifacts also observed but different from OSTIA
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Observations
from time series of global biases
 Number of match-ups is more than two orders of magnitude
larger against L4 TREF than against Tin situ
 Major trends & anomalies in TSAT are captured well against
all TREF. More detailed and crisper than against Tin situ
 Some TREF are “noisier” for VAL purposes than others.
Different artifacts are seen in different TREF
 Nevertheless, time series of (TSAT – TREF) can be used to
monitor TSAT for cross-platform & cross-product consistency
*
More time series (ACSPO/MUT, other reference SSTs) are available from
SQUAM page
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Cross-Platform Consistency
Using Double-Differences (TSAT – TSAT_REF)
 Cross-platform consistency of TSAT can be evaluated from
time series of TSAT -TREF overlaid for different platforms
 For more quantitative analyses, one ‘reference’ platform can
be selected & subtracted from all other (TSAT -TREF)
 N17 was selected as ‘reference’, because it is available for
the full SQUAM period, and its AVHRR is stable
 Double-differences (DD) were calculated as
DD = (TSAT -TREF) - (TN17 -TREF)
for SAT=N16, N18, and MetOp-A
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Global Median Biases
TSAT – Tin situ
 Same as slide 14
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In situ Double-Differences
(TSAT – Tin situ ) - (TN17 – Tin situ )
 Biases are due to errors in TSAT and TSAT /Tin situ skin/bulk differences
 Before mid-2006, all SSTs agree to within ~0.01 K
 In 2006, N16 develops a low bias up to ~-0.7 K, and N18 and MetOpA a warm bias up to ~+0.1 K
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OSTIA Double-Differences
(TSAT – TOSTIA ) - (TN17 – TOSTIA )
 DD’s with respect to global reference fields: Errors in TSAT + Missing
diurnal signal in TREF (TREF do not resolve diurnal cycle)
 N16: sensor problems. MetOp-A: suboptimal regression coefficients
 Diurnal correction to TREF is needed to rectify inconsistencies in TSAT
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Reynolds Double-Differences
(TSAT – TReynolds ) - (TN17 – TReynolds )
 DD’s are consistent for different TREF (biases/noises in TREF largely
cancel out in calculating DD’s)
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Observations
from Satellite-to-Satellite Double Differences
 In situ DD’s are close to ‘true’ cross-platform bias in TSAT
(bulk Tin situ partially accounts for diurnal cycle in skin TSAT)
 DD’s with respect to global TREF additionally include diurnal
signal (current L4 TREF do not resolve diurnal cycle)
 Employing diurnal-cycle resolved TREF in DD’s (or adding
diurnal correction on the top of existing TREF) should rectify
the ‘true’ cross-platform inconsistency in TSAT
 The DD’s provide quick global ‘validation’ of the diurnal cycle
model (e.g., Gentemann et al, 2003; Kennedy et al, 2007;
Filipiak and Merchant, 2009)
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Day-Night Consistency
Using Double-Differences TDAY – TNIGHT
 Day-Night consistency of TSAT can be evaluated as
DD = (TDAY -TREF) - (TNIGHT -TREF)
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In situ Day-Night Double-Differences
(TDAY – Tin situ ) - (TNIGHT – Tin situ )
 During daytime, all platforms show a warmer ~+(0.1±0.1) K bias
(except for N16 – sensor problem)
 Seasonal structure seen in DD’s
 Different capturing of diurnal cycle by skin TSAT and bulk Tin situ
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OSTIA Day-Night Double-Differences
(TDAY – TOSTIA ) - (TNIGHT – TOSTIA )
 Day-Night DD’s wrt OSTIA show biases due to diurnal warming
 Seasonal variability seen in all DD’s
 For N17 and MetOp-A (~10am/pm), diurnal signal is (+0.1±0.1) K
 For N18 (~2am/pm), diurnal signal is (+0.3±0.1) K
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Reynolds Day-Night Double-Differences
(TDAY – TReynolds ) - (TNIGHT – TReynolds )
 DD’s are closely reproducible for all TREF (biases/noise in TREF
largely cancel out in calculating DD’s)
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Observations
from Day-Night Double Differences
 DD’s wrt in situ data more closely represent cross-platform
inconsistencies in TSAT, less difference in the diurnal
 If global TREF is used, then DD’s additionally include diurnal
signal (currently, TREF‘s do not resolve diurnal cycle)
 Employing diurnal-cycle resolved TREF in DD’s is expected
to improve cross-platform consistency
 The DD’s provide quick global ‘validation’ of the diurnal
cycle model (e.g., Gentemann et al, 2003; Kennedy et al,
2007; Filipiak and Merchant, 2009)
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Summary and Future Work
 Validation against global reference fields is currently
employed in SQUAM to monitor two NESDIS operational
AVHRR SST products, in near-real time
 It helps quickly uncover SST product anomalies and diagnose
their root causes (SST algorithm, cloud mask, or sensor
performance), and leads to corrections
 Work is underway to reconcile AVHRR & reference SSTs
-
Improve AVHRR sensor calibration
Adjust TREF for diurnal cycle (e.g., Kennedy et al., 2007)
Improve SST product (cloud screening, SST algorithms)
Provide feedback to TREF producers
Objective is to have a single “benchmark” SST in NPOESS era
 Add NOAA-19 and eventually MetOp-B, -C and VIIRS to SQUAM
 We are open to integration with GHRSST and collaboration (to
test other satellite & reference SSTs, diurnal correction, ..)
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NESDIS NRT SST analyses on the web
 SQUAM page http://www.star.nesdis.noaa.gov/sod/sst/squam/
Real time maps, histograms, time series (including double
differences), dependencies
 CALVAL page http://www.star.nesdis.noaa.gov/sod/sst/calval/
Cal/Val of MUT and ACSPO data against in situ SST (currently,
password protected but will be open in 2-3 months)
 MICROS page http://www.star.nesdis.noaa.gov/sod/sst/micros/
(Monitoring of IR Clear-sky Radiances over Oceans for SST)
Validation of SST Radiances against RTM calculations with
Reynolds SST and NCEP GFS input
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