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

Slides:



Advertisements
Similar presentations
SNPP VIIRS green vegetation fraction products and application in numerical weather prediction Zhangyan Jiang 1,2, Weizhong Zheng 3,4, Junchang Ju 1,2,
Advertisements

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.
Experiments with Monthly Satellite Ocean Color Fields in a NCEP Operational Ocean Forecast System PI: Eric Bayler, NESDIS/STAR Co-I: David Behringer, NWS/NCEP/EMC/GCWMB.
1 High resolution SST products for 2001 Satellite SST products and coverage In situ observations, coverage Quality control procedures Satellite error statistics.
Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA Sea Surface Temperature Science.
Medspiration user meeting, dec 4-6 Use of Medspiration and GHRSST data in the Northern Seas Jacob L. Høyer & Søren Andersen Center for Ocean and Ice, Danish.
1 High Resolution Daily Sea Surface Temperature Analysis Errors Richard W. Reynolds (NOAA, CICS) Dudley B. Chelton (Oregon State University)
Climate Variability and Prediction in the Little Colorado River Basin Matt Switanek 1 1 Department of Hydrology and Water Resources University of Arizona.
1 Remote Sensing of the Ocean and Atmosphere: John Wilkin Sea Surface Temperature IMCS Building Room 214C
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.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 NOAA Operational Geostationary Sea Surface Temperature Products from NOAA.
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 NOAA’s National Climatic Data Center April 2005 Climate Observation Program Blended SST Analysis Changes and Implications for the Buoy Network 1.Plans.
1 Sea Surface Temperature Analyses NOAA’s National Climatic Data Center Asheville, NC Richard W. Reynolds.
Determining the accuracy of MODIS Sea- Surface Temperatures – an Essential Climate Variable Peter J. Minnett & Robert H. Evans Meteorology and Physical.
1 Comparisons of Daily SST Analyses for NOAA’s National Climatic Data Center Asheville, NC Richard W. Reynolds (NOAA, NCDC) Dudley B. Chelton.
Ongoing calibration and extension of SST 4 and 11 μm waveband algorithms for AQUA and TERRA MODIS using the in situ buoy, radiometer matchup database Robert.
MISST FY1 team meeting April 5-6, Miami, FL NOAA: Gary Wick, Eric Bayler, Ken Casey, Andy Harris, Tim Mavor Navy: Bruce Mckenzie, Charlie Barron NASA:
MODIS Sea-Surface Temperatures for GHRSST-PP Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer,
8th IOCCG Meeting in Florence, Italy (24-26 Feb 03) Current Status of MODIS (Terra and Aqua) by Chuck Trees for the MODIS Team Members.
A43D-0138 Towards a New AVHRR High Cloud Climatology from PATMOS-x Andrew K Heidinger, Michael J Pavolonis, Aleksandar Jelenak* and William Straka III.
Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2 Bruce Brasnett and Dorina Surcel Colan CMDE November 21, 2014 Brasnett, B. and.
General Objective: Conduct R&D activities to improve the quality of SST products used by MERSEA modeling and assimilation centers and produce global, Atlantic.
Initial Trends in Cloud Amount from the AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew K Heidinger, Michael J Pavolonis**, Aleksandar.
DMI-OI analysis in the Arctic DMI-OI processing scheme or Arctic Arctic bias correction method Arctic L4 Reanalysis Biases (AATSR – Pathfinder) Validation.
Application of in situ Observations to Current Satellite-Derived Sea Surface Temperature Products Gary A. Wick NOAA Earth System Research Laboratory With.
Introduction to CMUG assessments, SST and plans for phase 2 Roger Saunders 4 th Integration Meeting.
Andrew Heidinger and Michael Pavolonis
SST from Suomi-NPP VIIRS: Algorithm Development And Uncertainty Estimation Peter J Minnett, Robert H Evans, Kay Kilpatrick, Guillermo Podestá, Elizabeth.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 The Influences of Changes.
General Objective: Conduct R&D activities to improve the quality of SST products used by MERSEA modeling and assimilation centers and produce global, Atlantic.
Sea-surface Temperature from GHRSST/MODIS – recent progress in improving accuracy Peter J. Minnett & Robert H. Evans with Kay Kilpatrick, Ajoy Kumar, Warner.
MODIS Sea-Surface Temperatures for GHRSST-PP Peter J. Minnett & Robert H. Evans Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer,
1 Daily OI Analysis for Sea Surface Temperature NOAA’s National Climatic Data Center Asheville, NC Richard W. Reynolds (NOAA, NCDC) Thomas M. Smith (NOAA,
Retrieval Algorithms The derivations for each satellite consist of two steps: 1) cloud detection using a Bayesian Probabilistic Cloud Mask; and 2) application.
Transfer of AVHRR SST Pathfinder to NODC to sustain long term production, distribution and archiving Sept 8, 2010 CICS PI Robert Evans, RSMAS/U Miami ,
Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.
General Objective: to improve the quality of SST products used by EU-MERSEA modeling and assimilation centres and produce Mediterranean Sea analyzed SST.
Infrared and Microwave Remote Sensing of Sea Surface Temperature Gary A. Wick NOAA Environmental Technology Laboratory January 14, 2004.
General Objective: Conduct R&D activities to improve the quality of SST products used by MERSEA modeling and assimilation centers and produce global, Atlantic.
November 28, 2006 Derivation and Evaluation of Multi- Sensor SST Error Characteristics Gary Wick 1 and Sandra Castro 2 1 NOAA Earth System Research Laboratory.
STATUS of MODIS AQUA and TERRA SST Transition from V5 to V6
NOAA Climate Observation Annual Review Silver, Spring, MD Sept. 3-5, Intercomparisons Among Global Daily SST Analyses NOAA’s National Climatic Data.
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
CLIMAR-III, Gdynia, Poland, May 2008 Advances in the AVHRR Pathfinder Sea Surface Temperature Climate Data Record and its Connections with GHRSST Reanalysis.
GHRSST HL_TAG meeting Copenhagen, March 2010 Validation of L2P products in the Arctic Motivation: Consistent inter-satellite validation of L2p SST observations.
The MODIS SST hypercube is a multi-dimensional look up table of SST retrieval uncertainty, bias and standard deviation, determined from comprehensive analysis.
Page 1© Crown copyright 2004 Three-way error analysis between AATSR, AMSR-E and in situ sea surface temperature observations
AQUA AMSR-E MODIS POES AVHRR TRMM TMI ENVISAT AATSR Multi-satellite, multi-sensor data fusion: global daily 9 km SSTs from MODIS, AMSR-E, and TMI
1 Two- Stage High Resolution Daily SST Analysis Richard W. Reynolds (NOAA’s CICS) Dudley B. Chelton (Oregon State University) Thomas M. Smith (NOAA, STAR)
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.
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.
Incorporating Satellite Time-Series data into Modeling Watson Gregg NASA/GSFC/Global Modeling and Assimilation Office Topics: Models, Satellite, and In.
Characterizing and comparison of uncertainty in the AVHRR Pathfinder Versions 5 & 6 SST field to various reference fields Robert Evans Guilllermo Podesta’
EARWiG: SST retrieval issues for High Latitudes Andy Harris Jonathan Mittaz NOAA-CICS University of Maryland Chris Merchant U Edinburgh.
Uncertainty estimation from first principles: The future of SSES? Gary Corlett (University of Leicester) Chris Merchant (University of Edinburgh)
Edward Armstrong Jorge Vazquez Mike Chin Mike Davis James Pogue JPL PO.DAAC California Institute of Technology 28 May 2009 GHRSST Symposium, Santa Rosa,
Validation in Arctic conditions Steinar Eastwood (met.no) David Poulter (NOCS) GHRSST9, Perros-Guirec OSI SAF METOP SST, days mean.
Sandra Castro and Gary Wick.  Does direct regression of satellite infrared brightness temperatures to observed in situ skin temperatures result in.
SST from MODIS AQUA and TERRA Kay Kilpatrick, Ed Kearns, Bob Evans, and Peter Minnett Rosenstiel School of Marine and Atmospheric Science University of.
GHRSST-9 Perros-Guirec, France 9-13 June Intercomparisons Among Global Daily SST Analyses NOAA’s National Climatic Data Center Asheville, NC, USA.
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”
Pathfinder –> MODIS -> VIIRS Evolution of a CDR Robert Evans, Peter Minnett, Guillermo Podesta Kay Kilpatrick (retired), Sue Walsh, Vicki Halliwell, Liz.
Joint GRWG and GDWG Meeting February 2010, Toulouse, France
Comparison of AATSR SST with other sensors
The SST CCI: Scientific Approaches
Presentation transcript:

Characterizing and comparison of uncertainty in the AVHRR Pathfinder SST field, Versions 5 & 6 Robert Evans Guilllermo Podesta’ RSMAS Nov 8, 2010 with special thanks to R. Reynolds for the provision of reference fields

Description of Satellite Data LATBAND Latitude increments Pathfinder Version 6 (PF6) based on LATBAND formulation – 6 zonal bands 20 degrees wide centered on the Equator 2.5 degree wide transition band, linearly interpolated Coefficients estimated monthly, e.g. all January sat-in situ observations grouped, for available years of a given sensor Matchup criteria – within 2km and ±30 minutes for buoy- satellite observation Skin temperature product SST retrievals validated against radiometer matchups (MAERI)

Description of Satellite Data-2 Test data set, NOAA-18 for years [ ] NOAA-18 chosen due to processing speed of GAC data, approximately 30 minutes/year of global GAC observations and good availability of buoy observations and reference fields All available satellite pixels processed and compared to reference field Subsequent processing averages all best quality pixels within a ¼ degree box Time of observation, Difference from reference field (DT) included

Pathfinder Version 6 Quality Hypercube Quality variables and partitions Provides per pixel Bias and Standard Deviation

Comparison Products Processed ~5.4 years of NOAA-18, ¼ degree daily Pathfinder – Reference field (DT) Comparison with each of 5 reference fields Three types of comparison Reference to buoys using buoys not included in coefficient estimation Daily DT fields, to show regions of difference Latitude, Time plots based on best quality zonal average of each DT field, to show persistent zonal differences and temporal trends

Buoy Comparison of Pathfinder Versions 5 & 6

SST Reference File – 5 versions Richard Reynolds provided 3 versions of the ¼ degree, daily V2 OI OI + AVHRR (from NAVO, day + night satellite data) OI + AVHRR + AMSR (day + night satellite data) OI + AVHRR + AMSR (night only satellite data, minimize possible impact of residual diurnal warming) 3 Day AMSR composite (day + night), daily, ¼ degree field (RSS) AATSR (night only), based on 0.1 degree night only, 3 channel, dual view product (processing version: May, 2010) R. Reynolds processed AATSR daily fields into monthly, ¼ degree maps to fill gaps due to combination of narrow swath and cloudy observing conditions

Product Validation July 19, 2009 N18 night Version 6 LATBAND SST DT Field V6 SST – 3day AMSR composite, Yellow-green shows agreement within 0.2K Dark blue near Equator shows dust aerosol affected cold retrievals Significant 11, 12 μm SST challenge

Comparison of 1 day vs Ref Fields 3day AMSR reference OISST (NAVO AVHRR) reference Monthly AATSR reference Night only, dual view, 3 channel July 19, 2009 N18 night Note differences: Mediterranean Sea, High north latitude

N18 Night with and without new Quality test Pathfinder V6 without quality tree Pathfinder V6 with quality tree, minimizes aerosol pixels Color step = 0.2K, “zero” at blue-green transition

PF6 minimizes seasonal low and mid-latitude anomalies compared to PF5 ( ) Bias stable across time series Ref: OI+(NAVO)AVHRR+AMSR Night only-sat, N18 PF5 Grey ±0.1K PF6

6 Year Night Pathfinder V6-Reference Comparisons 3day AMSR Little difference at high lat, minimal zonal and temporal oscillation OISST: NAVO AVHRR High north lat summer, mid lat zonal oscillation (N) OISST: NAVO AVHRR+ AMSR Similar comparison for all OI versions Middle grey band ± 0.1K AATSR : Monthly Average Night – Dual view, 3 channel High lat not available in summer Pronounced seasonal zonal oscillation (N+S) N equatorial aerosol more pronounced

Conclusions Comparison with 3day AMSR fields shows smallest residuals, OI and AATSR show larger seasonal, zonal oscillation Comparison with July night field shows differences between reference fields (High Lat, western N Pacific, Mediterranean Sea) Ensemble average of reference fields possibly the preferred approach Improved quality test reduced cloud and aerosol impact However, dust aerosol impact remains an issue

Conclusions-2 Pathfinder V6 stable for 5.4 years of N-18, no long term trends observed Pathfinder Version 6 reduced but did not eliminate seasonal anomalies except for AMSR comparison PF6 includes Quality Hypercube, time of observation and DT fields, consistent with MODIS and VIIRS implementation AVHRR, MODIS and VIIRS all now supported in SeaDAS L2gen

END