Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 1 Prasanjit Dash 1,2, Alex Ignatov 1, Yuri Kihai 1,3, John.

Slides:

Advertisements

Similar presentations

Global Processing of MODIS for Operational SST, Ocean Color, and GHRSST Bryan Franz and the NASA Ocean Biology Processing Group 8th GHRSST-PP Science Team.

Advertisements

JPSS and GOES-R SST Sasha Ignatov

15 May 2009ACSPO v1.10 GAC1 ACSPO upgrade to v1.10 Effective Date: 04 March 2009 Sasha Ignatov, XingMing Liang, Yury Kihai, Boris Petrenko, John Stroup.

March 6, 2013iQuam v21 In situ Quality Monitor (iQuam) version 2 Near-real time online Quality Control, Monitoring, and Data Serving tool for SST Cal/Val.

Pathfinder –> MODIS -> VIIRS Evolution of a CDR Robert Evans, Peter Minnett, Guillermo Podesta Kay Kilpatrick (retired), Sue Walsh, Vicki Halliwell, Liz.

GHRSST XI Science Team Meeting, ST-VAL, June 2010, Lima, Peru Recent developments to the SST Quality Monitor (SQUAM) and SST validation with In situ.

Sea water dielectric constant, temperature and remote sensing of Sea Surface Salinity E. P. Dinnat 1,2, D. M. Le Vine 1, J. Boutin 3, X. Yin 3, 1 Cryospheric.

1 Overview LTSRF Status GHRSST Reanalysis Example L4 Analyses Future Activities GlobColour Connection Villefranche sur mer,

Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA Sea Surface Temperature Science.

Characterizing and comparison of uncertainty in the AVHRR Pathfinder SST field, Versions 5 & 6 Robert Evans Guilllermo Podesta’ RSMAS Nov 8, 2010 with.

8 November 2010SST Science Team Meeting1 Towards Community Consensus SSTs and Clear-Sky Radiances from AVHRR SST Science Team Meeting 8-10 November 2010,

1 High Resolution Daily Sea Surface Temperature Analysis Errors Richard W. Reynolds (NOAA, CICS) Dudley B. Chelton (Oregon State University)

NOAA Climate Obs 4th Annual Review Silver Spring, MD May 10-12, NOAA’s National Climatic Data Center 1.SSTs for Daily SST OI NOAA’s National.

1 Improved Sea Surface Temperature (SST) Analyses for Climate NOAA’s National Climatic Data Center Asheville, NC Thomas M. Smith Richard W. Reynolds Kenneth.

1 Overview LTSRF Status GHRSST Reanalysis Example L4 Analyses Future Activities Beijing, October 2006 The GODAE High Resolution.

1 Comparisons of Daily SST Analyses for NOAA’s National Climatic Data Center Asheville, NC Richard W. Reynolds (NOAA, NCDC) Dudley B. Chelton.

4 June 2009GHRSST-X STM - SQUAM1 The SST Quality Monitor (SQUAM) 10 th GHRSST Science Team Meeting 1-5 June 2009, Santa Rosa, CA Alexander “Sasha” Ignatov*,

Combining Data Assimilation with an Algorithm to Improve the Consistency of VIIRS Chlorophyll: Toward a Multidecadal, Multisensor Global Record NASA ROSES.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 CLOUD MASK AND QUALITY CONTROL FOR SST WITHIN THE ADVANCED CLEAR SKY PROCESSOR.

S-NPP EDR Validated Maturity Readiness Review 3-4 September 2014, College Park, MD Validated Stage 3 SST Maturity Review Alexander Ignatov And STAR JPSS.

MODIS Sea-Surface Temperatures for GHRSST-PP Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer,

Number of match-ups Mean Anomaly Fig. 3. Time-series of night MUT SST anomaly statistics compared to daily OISST SST. SST from different platforms mostly.

Quantifying the effect of ambient cloud on clear-sky ocean brightness temperatures and SSTs Korak Saha 1,2, Alexander Ignatov 1, and XingMing Liang 1,2.

GOES-R AWG 2 nd Validation Workshop 9-10 January 2014, College Park, MD GOES-R and JPSS SST Monitoring System Sasha Ignatov, Prasanjit Dash, Xingming Liang,

Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2 Bruce Brasnett and Dorina Surcel Colan CMDE November 21, 2014 Brasnett, B. and.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 In Situ SST for Satellite Cal/Val and Quality Control Alexander Ignatov.

GHRSST XIV STM – AUS TAG. Woods Hole 18 June 2013, Cape Cod, MA 1 GHRSST 2013 Annual Meeting – AUS TAG breakout session 18 June, 2013, Woods Hole, MA SQUAM.

1 GOES-R AWG Product Validation Tool Development Sea Surface Temperature (SST) Team Sasha Ignatov (STAR)

1 SST Near-Real Time Online Validation Tools Sasha Ignatov (STAR) AWG Annual Meeting June 2011, Ft Collins, CO.

Monitoring/Validation of HR SSTs in SQUAM GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 1 The 15 th GHRSST 2014 meeting, ST-VAL Breakout session 2–6.

SST from Suomi-NPP VIIRS: Algorithm Development And Uncertainty Estimation Peter J Minnett, Robert H Evans, Kay Kilpatrick, Guillermo Podestá, Elizabeth.

1 RTM/NWP-BASED SST ALGORITHMS FOR VIIRS USING MODIS AS A PROXY B. Petrenko 1,2, A. Ignatov 1, Y. Kihai 1,3, J. Stroup 1,4, X. Liang 1,5 1 NOAA/NESDIS/STAR,

Ambiguity of Quality in Remote Sensing Data Christopher Lynnes, NASA/GSFC Greg Leptoukh, NASA/GSFC Funded by : NASA’s Advancing Collaborative Connections.

Introduction GOES-R ABI will be the first GOES imaging instrument providing observations in both the visible and the near infrared spectral bands. Therefore.

GHRSST XI Meeting, IC-TAG Breakout Session, 22 June 2010, Lima, Peru Cross-monitoring of L4 SST fields in the SST Quality Monitor (SQUAM)

Request for SST Beta Maturity 23 February 2013, College Park, MD Request for SST Beta Maturity Sasha Ignatov – SST Lead John Stroup – SST Technical Liaison.

1 Overview LTSRF Status GHRSST Reanalysis Example L4 Analyses Future Activities D.C., Nov 2006 Long Term Stewardship and Reanalysis.

GHRSST XIV Science Team Meeting. Woods Hole June 2013, Cape Cod, MA 1 GHRSST 2013 Annual Meeting 17–21 June, 2013, Woods Hole, MA Preliminary analyses.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS) Alexander.

The GODAE High Resolution SST Project as an NDE Customer Presented By Ken Casey (NESDIS/NODC)

STATUS of MODIS AQUA and TERRA SST Transition from V5 to V6

24 January th AMS Symposium on Future Operational Environmental Satellite Systems 22 – 26 January 2012, New Orleans, LA NPP VIIRS SST Algorithm.

2 November 2011JPSS SST at STAR 1 2 nd NASA SST Science Team Meeting 2 – 4 November 2011, Miami Beach, FL Joint Polar Satellite System (JPSS) SST Algorithm.

Quality Flags: Two separate sets of QFs are provided in IDPS Product: (1) SST QFs; and (2) VIIRS Cloud Mask. Analyses were aimed at identifying a suitable.

CLIMAR-III, Gdynia, Poland, May 2008 Advances in the AVHRR Pathfinder Sea Surface Temperature Climate Data Record and its Connections with GHRSST Reanalysis.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Ship SST data for satellite SST and ocean.

A comparison of AMSR-E and AATSR SST time-series A preliminary investigation into the effects of using cloud-cleared SST data as opposed to all-sky SST.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Advanced Clear-Sky Processor.

Use of high resolution global SST data in operational analysis and assimilation systems at the UK Met Office. Matt Martin, John Stark,

International GHRSST User Symposium Santa Rosa, California, USA 28-29th May 2009 MODIS Sea-Surface Temperatures Peter J Minnett & Robert H. Evans With.

2 Jun 09 UNCLASSIFIED 10th GHRSST Science Team Meeting Santa Rosa, CA 1 – 5 June Presented by Bruce McKenzie Charlie N. Barron, A.B. Kara, C. Rowley.

1 March 2011iQuam GHRSST DV-WG, HL-TAG and ST-VAL Meeting 28 February – 2 March 2011, Boulder, CO In situ Quality Monitor (iQuam) Near-real time.

Characterizing and comparison of uncertainty in the AVHRR Pathfinder Versions 5 & 6 SST field to various reference fields Robert Evans Guilllermo Podesta’

© Crown copyright Met Office Report of the GHRSST Inter-Comparison TAG (IC-TAG) Matt Martin GHRSST XI meeting, Lima, Peru, June 2010.

1 Santa Rosa, CA, June 2009 Kenneth S. Casey, Tess Brandon NOAA National Oceanographic Data Center Nick Rayner Met Office Hadley Centre June 2010 GHRSST.

New Australian High Resolution AVHRR SST Products from the Integrated Marine Observing System Presented at the GHRSST Users Symposium, Santa Rosa, USA,

Validation in Arctic conditions Steinar Eastwood (met.no) David Poulter (NOCS) GHRSST9, Perros-Guirec OSI SAF METOP SST, days mean.

Prototyping SST Retrievals from GOES-R ABI with MSG SEVIRI Data Nikolay V. Shabanov 1,2, (301)

Report from breakout session in the High Latitude Working group Prepared by Jacob L. Høyer, Bob Grumbine and Steinar Eastwood.

29 May 2009GHRSST User's Symp - SQUAM1 The SST Quality Monitor (SQUAM) 1 st GHRSST Int’l User’s Symposium May 2009, Santa Rosa, CA Alexander “Sasha”

Validation of MTSAT-1R SST for the TWP+ Experiment Leon Majewski 1, Jon Mittaz 2, George Kruger 1,3, Helen Beggs 3, Andy Harris 2, Sandra Castro 4 1 Observations.

Monitoring of SST Radiances

Joint GRWG and GDWG Meeting February 2010, Toulouse, France

SST – GSICS Connections

NOAA Report on Ocean Parameters - SST Presented to CGMS-43 Working Group 2 session, agenda item 9 Author: Sasha Ignatov.

DCC inter-calibration of Himawari-8/AHI VNIR bands

Using Double Differences in MICROS for Cross-Sensor Consistency Checks

Comparison of AATSR SST with other sensors

Towards Understanding and Resolving Cross-Platform Biases in MICROS

Radiometric Consistency between AVHRR, MODIS, and VIIRS in SST Bands

Presentation transcript:

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 1 Prasanjit Dash 1,2, Alex Ignatov 1, Yuri Kihai 1,3, John Stroup 1,4, John Sapper 1, Boris Petrenko 1,3 1 NOAA NESDIS, NCWCP College Park, MD 2 Colorado State Univ, CIRA 3 GST, Inc, MD, USA 4 STG, Inc, VA, USA ( s: The 15 th GHRSST 2014 meeting, ST-VAL Breakout session 2–6 Jun, 2014, Cape Town, South Africa Which VIIRS product to use? NOAA ACSPO vs. NAVO SEATEMP SQUAM objective: A global, web-based, community, quasi NRT, monitor for SST producers & users ! AUS TAG and VIIRS breakout GHRSST XV, 2014 X Jun 2014, X:XX-X:XX AM

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 2 Major SST data providers: Projects and international group Acknowledgments Level-2 SST: VIIRS/AVHRR/MODIS -NESDIS SST Team : ACSPO (GAC: 5 platforms, FRAC: Metop-A & B, VIIRS: NPP, MODIS: Terra/Aqua) -D. May, B. McKenzie : NAVO SEATEMP -S. Jackson : IDPS (NPP) Level 4 SSTs: -M. Martin, J. R. Jones : OSTIA foundation, GHRSST Median Product Ensemble, OSTIA Reanalysis -B. Brasnett : Canadian Met. Centre, 0.2  foundation GHRSST support: Peter Minnett, Craig Donlon, Alexey Kaplan CMC

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 3 Outline 1. High-resolution (HR) SQUAM 2. Monitoring of VIIRS SSTs (ACSPO, IDPS, NAVO) in SQUAM Maps, Histograms, Time series, Dependence 3. Validation against QC’ed drifters 4. Persistent features in monthly maps 5. Closer look at ACSPO vs. NAVO: VZA dependence in Hovmöller space 6. Summary p: 4 p: 5-9 p: p: p: 14 p: slides

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 4 Locate this website: Google: “SST + SQUAM + HR” The SST Quality Monitor (SQUAM), JTech, 27, , VIIRS SSTs in High-Res (HR) SQUAM

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 5 MapsHistogramsTime-seriesDependenciesHovmöller Night: VIIRS (IDPS) minus CMC L4, 15-May-2014 Initial product developed by private contractor, now discontinued and replaced by ACSPO Coverage is good but many cold spots indicate residual cloud/aerosol leakages 2. VIIRS SST in SQUAM: IDPS

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 6 MapsHistogramsTime-seriesDependenciesHovmöller Night: VIIRS (ACSPO) minus CMC L4, 15-May-2014 ACSPO is NOAA heritage SST product, now operational and replaced the IDPS Residual Cloud/Aerosol leakages reduced compared to IDPS 2. VIIRS SST in SQUAM: ACSPO

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 7 MapsHistogramsTime-seriesDependenciesHovmöller Night: VIIRS (NAVO) minus CMC L4, 15-May-2014 Reduced coverage (VZA<54°; 2×2 pixel processing; more conservative mask) Fewer leakages especially in the Tropics (on this particular day) 2. VIIRS SST in SQUAM: NAVO

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 8 MapsHistogramsTime-seriesDependenciesHovmöller ACSPO VIIRS – CMC NAVO VIIRS – CMC IDPS VIIRS – CMC 2. VIIRS SSTs in SQUAM: Example 15 May 2014, Night IDPS vs. ACSPO o Retrieval domain: Comparable o SST performance statistics: ACSPO superior NAVO vs. ACSPO o Retrieval domain: NAVO factor of ×2.6 smaller o SST performance statistics: Comparable

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 9 MapsHistogramsTime-seriesDependenciesHovmöller 2. Monitoring in SQUAM: VIIRS SSTs, Day o Mean and Std Dev of residuals (Sat SST – OSTIA) closely track each other o Similar observations for other references o NAVO: # of NAVO observations ~1/3 of ACSPO Day: very similar (this figure); Night: NAVO slightly but consistently better (see SQUAM-HR webpage) (for other products: see SQUAM-HR webpage) Monthly in situ val ---  NEXT SLIDE

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 10 o Mean and Std Dev of residuals (Sat SST – Drifters) closely track each other o Similar observations for other references o NAVO: # of NAVO observations ~1/3 of ACSPO Std Dev: NAVO slightly but consistently better MapsHistogramsTime-seriesDependenciesHovmöller 2. Validation: VIIRS SSTs (monthly, vs. drifters, Day)

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa NAVO/ACSPO Summary Statistics Products# of matches (%ACSPO)Min / Max (◦C) Mean / MedianStd Dev / RSDSkew / Kurt ACSPO NPP42,917 (night) 42,586 (day) / / / / / / / / 4.46 NAVO NPP12,912 (30.08%) 10,063 (23.62%) / / / / / / / / 5.89 IDPS NPP48,638 (113.33%) 46,208 (108.50%) / / / / / / / / * QC’ed drifters from iQuam: Products# of matches (%ACSPO)Min / Max (◦C) Mean / MedianStd Dev / RSDSkew / Kurt ACSPO NPP million (night) million (day) / / / / / / / / NAVO NPP45.29 mi (38.68%) mi (32.58%) / / / / / / / / 8.14 IDPS NPP (102.58%) (100.95%) / / / / / / / / Validation wrt. drifters, Feb 2014 Monitoring wrt. CMC, 15 May 2014

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 12 MapsHistogramsTime-seriesDependenciesHovmöller Day: VIIRS (ACSPO) minus CMC L4, Apr Looking for persistent features: Monthly aggregation Negative residuals in monthly maps suggest persistent cloud/aerosol leakages Positive residuals: Daytime diurnal warming? or NLSST algorithm artifacts?

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 13 MapsHistogramsTime-seriesDependenciesHovmöller Day: VIIRS (NAVO) minus CMC L4, Apr-2014 Some areas of the ocean are not covered even at monthly time interval Warm spots more pronounced: Differences in NAVO./ACSPO cloud screening? or SST algorithm? 4. Looking for persistent features: Monthly aggregation

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa VAL Statistics as a Function of Scan Angle ACSPO makes retrievals in full swath whereas NAVO at VZA<54° only Within the limited VZA domain, ACSPO and NAVO Std Dev values are close ACSPO STD Dev degrade towards swath edges Degradation is more complex than merely a function of VZA, and shows some seasonality

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 15  There are 2 global VIIRS products: NOAA ACSPO and NAVO SEATEMP. Both are archived at PO.DAAC / NODC in GDS2 format  IDPS product is being discontinued, due to suboptimal performance & lack of users. It is currently archived at CLASS in hdf5 and will be wiped off once complete record of ACSPO VIIRS from Jan 2012-on is archived  Relative merits of ACSPO and NAVO VIIRS SSTs o NAVO global coverage is about 1/3 of ACSPO. As a result, some areas of the ocean remain uncovered by the NAVO product for extended periods up to a month o Measured in their corresponding full retrieval domains, NAVO outperforms ACSPO, by a narrow (day) to moderate (night) margin. o However, in the “intersection” domain, both products show comparable performances THANK YOU! 6. Summary

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 16 Backup slides

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 17 MapsHistogramsTime-seriesDependenciesHovmöller 2. Monitoring in SQUAM: VIIRS SSTs, Night o Mean and Std Dev of residuals (Sat SST – OSTIA) closely track each other o Similar observations for other references o NAVO: # of NAVO observations ~1/3 of ACSPO slightly but consistently better (for other products: see SQUAM-HR webpage) Monthly in situ val ---  NEXT SLIDE

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 18 MapsHistogramsTime-seriesDependenciesHovmöller 2. Validation: VIIRS SSTs (monthly, vs. drifters, Night) o Mean and Std Dev of residuals (Sat SST – OSTIA) closely track each other o Similar observations for other references o NAVO: # of NAVO observations ~1/3 of ACSPO slightly but consistently better

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 19 MapsHistogramsTime-seriesDependenciesHovmöller 2. Monitoring in SQUAM: VIIRS SSTs, Day o Mean and Std Dev of residuals (Sat SST – OSTIA) closely track each other o Similar observations for other references o NAVO: # of NAVO observations ~1/3 of ACSPO Day: very similar (this figure); Night: NAVO slightly but consistently better (for other products: see SQUAM-HR webpage) Monthly in situ val ---  NEXT SLIDE

Which VIIRS product to use: ACSPO vs. NAVO GHRSST-XV, 2-6 June 2014, Cape Town, South Africa 20 o Mean and Std Dev of residuals (Sat SST – Drifters) closely track each other o Similar observations for other references o NAVO: # of NAVO observations ~1/3 of ACSPO Std Dev: NAVO slightly but consistently better MapsHistogramsTime-seriesDependenciesHovmöller 2. Validation: VIIRS SSTs (monthly, vs. drifters, Day)