4 June 2009GHRSST-X STM - SQUAM1 The SST Quality Monitor (SQUAM) 10 th GHRSST Science Team Meeting...

4 June 2009 GHRSST-X STM - SQUAM 1

The SST Quality Monitor (SQUAM)

10th GHRSST Science Team Meeting1-5 June 2009, Santa Rosa, CA

Alexander “Sasha” Ignatov*, Prasanjit Dash*, John Sapper**, Yury Kihai*

NOAA/NESDIS

*Center for Satellite Applications & Research (STAR)**Office of Satellite Data Processing & Distribution (OSDPD)


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

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)

Objective of the SST Quality Monitor (SQUAM)





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.


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”.


Heritage MUT 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).

Removing large-scale SST background (daily 0.25º Reynolds) emphasizes ‘noise’

ACSPO SST product


View angle dependence of‘MUT - daily Reynolds SST’ (NOAA-17)

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.

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.


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 = σsat

2 + 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


Global Histograms of TSAT - TREF (Nighttime MUT)


Histogram of SST residualReference 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


8 days of data: ~483,500 match-ups with OSTIA SST

Median = 0.00 K; Robust Standard Deviation = 0.30 K

Histogram of SST residualReference SST: OSTIA


8 days of data: ~483,700 match-ups with daily Reynolds SST

Median = +0.08 K; Robust Standard Deviation = 0.44 K

Histogram of SST residualReference SST: Daily Reynolds


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

Observationsfrom global histograms analyses


Time SeriesGlobal Median Biases of (TSAT - TREF)


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


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)

Global Median Biases TSAT – TOSTIA


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

Global Median BiasesTSAT – TReynolds


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

Observationsfrom time series of global biases


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

Cross-Platform Consistency Using Double-Differences (TSAT – TSAT_REF)


Global Median Biases TSAT – Tin situ

Same as slide 14


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 MetOp-A

a warm bias up to ~+0.1 K


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


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)


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)

Observationsfrom Satellite-to-Satellite Double Differences


Day-Night consistency of TSAT can be evaluated as

DD = (TDAY -TREF) - (TNIGHT -TREF)

Day-Night Consistency Using Double-Differences TDAY – TNIGHT


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


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


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)


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)

Observationsfrom Day-Night Double Differences


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

Summary and Future Work

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, ..)


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

NESDIS NRT SST analyses on the web




http://www.star.nesdis.noaa.gov/sod/sst/calval/

http://www.star.nesdis.noaa.gov/sod/sst/micros/

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4 June 2009GHRSST-X STM - SQUAM1 The SST Quality Monitor (SQUAM) 10 th GHRSST Science Team Meeting...

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