sst quality monitor (squam): focus on suomi npp viirs

2012 EUMETSAT Met. Sat. Conf., 6-Sep-2012, Sopot, Poland 1

2012 EUMETSAT Meteorological Satellite Conference

3 – 7 September, 2012, Sopot, Poland

SST Quality Monitor (SQUAM):Focus on Suomi NPP VIIRS

Prasanjit Dash1,2 and Alexander Ignatov1

1NOAA/NESDIS, Center for Satellite Applications & Research (STAR)2Colorado State Univ, Cooperative Institute for Research in the Atmosphere (CIRA)

Objective: A global, web-based, community, quasi NRT, monitor for SST producers & users !


Major SST data providers: Projects and international group

Contributions/Support

Level-2 SST: VIIRS/AVHRR/MODIS- NESDIS SST Team : ACSPO (GAC: 5 platforms, FRAC: MetOp-A, VIIRS: NPP, MODIS: Terra/Aqua)- P. LeBorgne: O&SI SAF MetOp-A FRAC- D. May, B. McKenzie: NAVO SEATEMP- S. Jackson: IDPS (NPP)

Level-3 SST: AVHRR:- K. Casey, R. Evans, J. Vazquez, E. Armstrong: PathFinder v5.0

Level 4 SSTs:- R. Grumbine, B. Katz: RTG (Low-Res & Hi-Res)- R. Reynolds, V. Banzon: OISSTs (AVHRR & AVHRR+AMSRE)- M. Martin, J. R. Jones: OSTIA foundation, GHRSST Median Product Ensemble, OSTIA Reanalysis- D. May, B. McKenzie: NAVO K10- J.-F. Piollé, E. Autret : ODYSSEA- E. Maturi, A. Harris, J. Mittaz: POES-GOES blended- B. Brasnett: Canadian Met. Centre, 0.2 foundation- Y. Chao: JPL G1SST- H. Beggs: ABOM GAMSSA- M. T. Chin, J. Vazquez, E. Armstrong: JPL MUR

L4s in SQUAM pipeline:J. Hoyer:DMI OISST; S. Ishizaki:MGDSST; C. Gentemann:RSS products; J. Cummings:NCODA

GHRSST support: - Craig Donlon, Peter Minnett, Alexey Kaplan

Definitions of levels:L2: at observed pixels (satellite)L3: gridded with gaps (satellite)L4: gap-free gridded, time-averaged

CMC


SST datasets available in community

Level 4Reynolds (AVHRR; +AMSR-E*)

RTG (Low, High Resolution), GSI

OSTIA, Operational + Retro (UKMO)

ODYSSEA (France)

GMPE (GHRSST)

NAVO K10

NESDIS POES-GOES Blended

JPL G1SST, JPL MUR

NCODA (NRL)

CMC 0.2 (Canada)

GAMSSA (Australia)

MGDSST (JAXA, Japan)

RSS (MW, MW+MODIS)

DMISST (Danish Met. Inst.)

In situSources-GTS, ICOADS, GODAE/FNMOC

Platforms-Drifters, Moorings, Ships, ARGO Floats

Quality Control-May be unavailable or non-uniform

Level 2/3Polar-AVHRR (NESDIS, NAVO, O&SI SAF, U. Miami, NODC)-VIIRS (NESDIS, IDPS),MODIS,ATSR,-Microwave

Geostationary-GOES (NESDIS, NAVO, O&SI SAF)-SEVIRI (NESDIS, O&SI SAF)-MTSAT (NESDIS, JAXA)

- SST community is data-rich

- Are these products self-consistent? Cross-consistent?

pdash

Self note: after stating the issue and posing the questions - continue to the next slide stating that SQUAM is designed to address all these questions.!


SQUAM Objectives and Methodology

Cross-evaluate all major global SST products and validate against consistent reference standard, in near-real time

Report summary performance statistics online

SQUAM is organized into three major modules: L2, L3, and L4

The diagnostics aimed at assessing (relative) performance of

o Cloud mask

o SST Algorithm

o Ice-mask ..

Methodology

Analyze global differences, ΔTS = TS minus expected TR

• Evaluate global maps for “uniformity”

• Evaluate distributions for Normality (X~N(µ,σ))

• Compare and Trend Gaussian parameters in time

pdash

Continue to talk about :1) choice of expected states. Why gap-free along is additionally required (sparse in situ)2) mention in passing about transferability of the technique to other products


NPP VIIRS(TS) – Drifters(TR):

Conventional Val wrt. In situ SST:

- Sparse (voids), geographically non-uniform

- Quality non-uniform & sub-optimal

- Number of Match-ups ~2,000/Night

NPP VIIRS(TS) – OSTIA(TR):

Using L4:

- Complements traditional in situ VAL

- Global snapshot, in near-real time

- Number of Match-ups 90,000,000/Night

Choice of expected state: In situ or L4?


SQUAM Interface

Locate this website: Google: “SST + SQUAM”, the first hit


L2/3 SQUAMΔTS (TS - TR) analyzed:

Maps

Histograms

Time series (Gaussian moments, outlier info, double differences)

Dependencies (on geophysical & observational parameters)

Hovmöller Diagrams (Time series of dependencies)

The SST Quality Monitor (SQUAM)Journal of Atmospheric & Oceanic Technology, 27, 1899-1917, 2010

(GHRSST ST-VAL)


L2/L3 SQUAM: Maps Histograms Time-series Dependencies

Hovmöller

• The Suomi National Polar Partnership (NPP) satellite launched in Oct 2011

• Cryoradiator Doors opened on 18 Jan 2012• VIIRS Global SST Products produced since 23 Jan 2012

ACSPO and IDPS: Two SST processing systems applied to the same satellite data

– ACSPO (Advanced Clear-Sky Processor for Oceans) - NOAA– IDPS (Interface Data Processing Segment) – NPOESS Contractor,

transitioning to NOAA



Hovmöller

NIGHT: ACSPO VIIRS L2 minus OSTIA L4, 13 August 2012

• Deviation from TR is flat & close to 0• Residual Cloud/Aerosol leakages seen in the Tropics



Hovmöller

NIGHT: IDPS VIIRS L2 minus OSTIA L4, 13 August 2012

• More Cloud leakages in IDPS than in ACSPO Two processors applied to the same satellite dataMore HR Level-2 SST analyses at:http://www.star.nesdis.noaa.gov/sod/sst/squam/HR/

pdash

In passing, mention about limb cooling and probably sub-optimal SST cal coefficients



Hovmöller

NIGHT: ACSPO VIIRS L2 minus OSTIA L4, 13 August 2012

• Shape close to Gaussian• Domain and performance stats close to expected



Hovmöller

• IDPS confidently clear sample ~27% larger• Shape less Gaussian (negative skewness, increased kurtosis)• Increased Std Dev/Robust Std Dev; Larger fraction of outliers

NIGHT: IDPS VIIRS L2 minus OSTIA L4, 13 August 2012



Hovmöller

# (%ACSPO) Min/ Max Mean/ STD Med/ RSD Skew/ Kurt

ACSPO 93.2 M (100%) -7.3/ +15.3 -0.01/0.58 +0.01/0.45 +0.7/ +9.0

IDPS 118.3 M (127%) -17.1/+26.7 -0.23/0.89 -0.18/0.47 -1.64/+32.3

Summary stat of ΔT = “VIIRS minus OSTIA” SST for 13 Aug 2012(expected ~0)

• IDPS SST domain +27% larger than ACSPO

• IDPS performance statistics degraded, compared to ACSPO

• Gap between Conventional and Robust stats wider in IDPS - More outliers

IDPS/ACSPO Analyses routinely performed in SQUAM

Comparisons with other AVHRR and MODIS products are also done

VIIRS, MODIS, and AVHRR SST products are being improved



Hovmöller

SST Std Dev from OSTIA for VIIRS, AVHRR, MODIS

•AVHRR and MODIS closely track each other•ACSPO VIIRS is consistent with MODIS/AVHRR•So far, IDPS VIIRS EDR shows larger Std Dev than ACSPO

NPP JPSS

Warm-Up Cool-Down Event

IDPS shows larger Std Dev

Loss of Envisat

pdash

- features are reproducible for any TR, but crispier and less lag in L4- SQUAM facilitates quicker diagnostics


Inter-compare L4 SSTs using similar diagnostics:

maps, histograms, time series, Hovmöller

All analyses are done in two modes:

including and excluding ice fields (if ice flags available)

Validate consistently against quality controlled in situ data

Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons—Part 1: A GHRSST multi-product ensemble (GMPE)Deep Sea Research Part II:, 77-80, 21-30, 2012

Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons—Part 2: Near real time web-based level 4 SST Quality Monitor (L4-SQUAM)Deep Sea Research Part II:, 77-80, 31-43, 2012

L4 intercomparison


http://www.star.nesdis.noaa.gov/sod/sst/squam/L4/

L4 SQUAM: Maps Histograms Time-series Hovmöller


Summary and Future Work

SQUAM currently monitors major global polar L2/3 SST products from VIIRS, MODIS, and AVHRR, and 14 L4 SST products

Two VIIRS SST products were added in Jan 2012: ACSPO and IDPS

ACSPO SST is consistent with AVHRR/MODIS family, IDPS SST is less accurate

Work is underway to improve both VIIRS SST products, and reconcile

Future data additions in SQUAMo Polar: MODIS MOD29/MYD28 and (A)ATSR

o Geostationary: MSG, MTSAT, GOES – Preparation for GOES-R

o Remaining L4 SSTs

Work with community towards Improved self- and cross-consistency of SST productso Improve cloud masks, SST algorithms, sensor calibration

o Explore SST diurnal variability models

o Understand relative merit and differences of products, and reconcile

o Facilitate blending towards improved analyses, forecast, and climate

THANK YOU!


BACK UP Slides


SQUAM web


Role / Responsibility

NOAA/POES and NASA/EOS

NPOESS JPSS

Platform, Launch Vehicle

Private Industry Private Industry Private Industry

Instruments Private Industry Private Industry Private Industry

Algorithms Government Private IndustryIn transition to Government

Data Products Government Private Industry IPO/NPOESS

Cal/Val Government Private Industry Government

Archival & Distribution

Government Government Government

JPSS Data Products:IDPS – Interface Data Processing Segment

Algorithms: IPO/NPOESS (Northrop Grumman Aerospace Systems) Operational Products: IPO/NPOESS (Raytheon) IDPS (RDRs, SDRs, EDRs)



Hovmöller

High Res. L2 SST mean differences:VIIRS, AVHRR, MODIS

-ACSPO VIIRS is consistent with MODIS & AVHRR

- IDPS VIIRS EDR shows larger diff (also Std Dev; not shown)

L2 vs. in situ driftersnight, ~1500-2000/day

NPP JPSS


L2 vs. OSTIAnight, ~50-100mi/day

pdash

- features are reproducible for any TR, but crispier and less lag in L4- SQUAM facilitates quicker diagnostics


NIGHT STD DEV wrt. OSTIA L4

• AVHRR & MODIS SSTs are consistent• ACSPO VIIRS is consistent with MODIS & AVHRR • IDPS VIIRS EDR shows larger STD DEV


IDPS shows larger STD DEV


Hovmöller


DAY STD DEV wrt. OSTIA L4


IDPS shows much larger STD DEV

• AVHRR & MODIS SSTs are consistent• ACSPO VIIRS is consistent with MODIS & AVHRR • IDPS VIIRS EDR shows much larger STD DEV


Hovmöller



HovmöllerPla

tform

sYear

Wind Speed (ms-1)

NOAA-17 mid-morning platform - Diurnal warming suppressed


“OISST – CMC” mean zonal difference

Year

Latitude

More combinations at: http://www.star.nesdis.noaa.gov/sod/sst/squam/L4/

Diff. in ice mask

HL issues


pdash

calls for consensus between the L4 developers


Mean

Year

Std

Dev

Year

wrt drifters wrt drifters

DOI_AVDOI_AARTG_HRRTG_LRGOESPOESOSTIAOSTIA_RANCMC 0.2NAVO K10GAMSSAODYSSEAGMPEG1SSTMUR

Reynolds OISST (AVHRR)Reynolds OISST (+ AMSR-E)Real Time Global high resolutionRTG low resolutionBlended POES and GOESOperational SST and Sea ice analysisOSTIA Reanalysis (retro)Canadian Met. Centre 0.2 degreeNAVOCEANO 1/10 degreeMERSEA IFREMER/CERSATAustralian BOMGHRSST Median Ensemble ProductJPL 1km G1SSTJPL Multi scale ultra high res. SST

Roughly, L4 products form 3 major groups(when compared against GMPE):

DOI_AV, DOI_AA, RTG_LR, NAVO K10,

G1SST

RTG_HR, GOES-POES blended (with seasonal variation between: RTG_HR, RTG_LR)

OSTIA, CMC, GAMSSA & GMPE

Too many products busy .. Interactive plots available online


sst quality monitor (squam): focus on suomi npp viirs

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