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

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

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.
SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Sea surface temperature (SST) basics NASA Ocean Biology Processing Group Goddard Space Flight Center,
Pathfinder –> MODIS -> VIIRS Evolution of a CDR Robert Evans, Peter Minnett, Guillermo Podesta Kay Kilpatrick (retired), Sue Walsh, Vicki Halliwell, Liz.
MODIS Ocean Products MODIS/AIRS Workshop Pretoria, South Africa April 4-7, 2006 Liam Gumley Space Science and Engineering Center University of Wisconsin-Madison.
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.
VIIRS LST Uncertainty Estimation And Quality Assessment of Suomi NPP VIIRS Land Surface Temperature Product 1 CICS, University of Maryland, College Park;
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.
Characterizing and comparison of uncertainty in the AVHRR Pathfinder SST field, Versions 5 & 6 Robert Evans Guilllermo Podesta’ RSMAS Nov 8, 2010 with.
1 High Resolution Daily Sea Surface Temperature Analysis Errors Richard W. Reynolds (NOAA, CICS) Dudley B. Chelton (Oregon State University)
1 Remote Sensing of the Ocean and Atmosphere: John Wilkin Sea Surface Temperature IMCS Building Room 214C
1 A High Resolution Daily SST Analysis Richard W. Reynolds (NOAA, CICS) Dudley B. Chelton (Oregon State University) Thomas M. Smith (NOAA, STAR)
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Correction of Vegetation Time Series for Long Term Monitoring Marco Vargas¹.
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 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:
Orbit Characteristics and View Angle Effects on the Global Cloud Field
MODIS Sea-Surface Temperatures for GHRSST-PP Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer,
AN ENHANCED SST COMPOSITE FOR WEATHER FORECASTING AND REGIONAL CLIMATE STUDIES Gary Jedlovec 1, Jorge Vazquez 2, and Ed Armstrong 2 1NASA/MSFC Earth Science.
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.
11 Ice Cover and Sea and Lake Ice Concentration with GOES-R ABI Presented by Yinghui Liu Presented by Yinghui Liu 1 Team Members: Yinghui Liu, Jeffrey.
Application of in situ Observations to Current Satellite-Derived Sea Surface Temperature Products Gary A. Wick NOAA Earth System Research Laboratory With.
Andrew Heidinger and Michael Pavolonis
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Satellite Wind Products Presented.
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,
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
ISCCP Calibration 25 th Anniversary Symposium July 23, 2008 NASA GISS Christopher L. Bishop Columbia University New York, New York.
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.
Infrared and Microwave Remote Sensing of Sea Surface Temperature Gary A. Wick NOAA Environmental Technology Laboratory January 14, 2004.
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.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS) Alexander.
STATUS of MODIS AQUA and TERRA SST Transition from V5 to V6
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
AIRS Land Surface Temperature and Emissivity Validation Bob Knuteson Hank Revercomb, Dave Tobin, Ken Vinson, Chia Lee University of Wisconsin-Madison Space.
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
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.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
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.
International GHRSST User Symposium Santa Rosa, California, USA 28-29th May 2009 MODIS Sea-Surface Temperatures Peter J Minnett & Robert H. Evans With.
Characterizing and comparison of uncertainty in the AVHRR Pathfinder Versions 5 & 6 SST field to various reference fields Robert Evans Guilllermo Podesta’
MODIS and VIIRS Sea-Surface Temperatures: Validation of continuity products Kay Kilpatrick and Peter J Minnett Susan Walsh, Elizabeth Williams, Goshka.
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.
GHRSST interest in upgraded drifters - Summary from the GHRSST Joint Workshop Melbourne Andrea Kaiser-Weiss, Gary Corlett, Chris Merchant, Piere LeBorgne,
1 Objective Determination of Feature Resolution in an SST Analysis Dudley B. Chelton (Oregon State University) Richard W. Reynolds (NOAA, CICS) Dimitris.
Calculation of Sea Surface Temperature Forward Radiative Transfer Model Approach Alec Bogdanoff, Florida State University Carol Anne Clayson and Brent.
Impacts of GSICS inter-calibration on JAXA’s HIMAWARI-8 SST
MODIS SST Processing and Support for GHRSST at OBPG
IMAGERY DERIVED CURRENTS FROM NPP Ocean Color Products 110 minutes!
Passive Microwave Radiometer constellation for Sea Surface Temperature Prepared by CEOS Sea Surface Temperature Virtual Constellation (SST-VC) Presented.
Validation of Satellite-derived Lake Surface Temperatures
The SST CCI: Scientific Approaches
NOAA Objective Sea Surface Salinity Analysis P. Xie, Y. Xue, and A
Hippocampal “Time Cells”: Time versus Path Integration
Comparison of observed SST Vs. Satellite AVHRR SST
Presentation transcript:

Pathfinder –> MODIS -> VIIRS Evolution of a CDR Robert Evans, Peter Minnett, Guillermo Podesta Kay Kilpatrick (retired), Sue Walsh, Vicki Halliwell, Liz Williams GHRSST-X – June 1-5, 2009

Standard SST Retrieval Equation (Pathfinder, MODIS and VIIRS) SST = a + b*T4 + c*(T4-T5)*T surface + d*(sec(q)-1)*(T4-T5) – where q is the zenith angle of the instrument – T4 and T5 are the brightness temperatures from AVHRR channels 4 and 5, or channels 31 and 32 for MODIS – Two set of monthly coefficients are determined for T4 - T5 <= 0.7 (temperate to polar) T4 - T5 > 0.7 (equatorial to temperate) Quality Levels – Pathfinder defines eight quality level (0-7) where 7 indicates the highest confidence of an accurate retrieval, (0-3 for MODIS) Current Challenges – Mid-high latitude seasonal anomalies, – Dust aerosols and high water vapor

V5 Path – HadSST2 (night) 5.1 for N7 Operational AVHRR – HadSST2 (night) V5 Path – HadSST2 (day) Operational AVHRR – HadSST2 (day) +N7 +N9 +N11 +N9+N14 +N16+N18 Pathfinder–HADSST2 residuals for 1982 through 2007, provided by Casey and Brandon, NODC

Next Generation SST Improvements Minimize High Latitude Seasonal Oscillation Approach: – Estimate Coefficients using 20 o zonal bands centered at the Equator, ± 2.5 o transition between bands - Coefficients estimated monthly and repeat for each year for a given sensor (volcano periods to be handled separately) - Reference SST – Reynolds ¼ o, daily Uses AMSR, Pathfinder and In situ observations retains satelite SST in high gradient regions New algorithms are first developed and tested for MODIS (improved sensor characterization and more stable operating environment) Latband algorithms will be transitioned to AVHRR as part of our SDS program

Application of LATBAND to MODIS AQUA Median of SST residuals (VALIDATION set) by quarter ( ). Each line corresponds to a latitude band. Upper panel corresponds to latitude-specific SST residuals; lower panel is current SST (CSST). MODIS algorithm produces a ‘skin temperature’ product Note high latitude seasonal oscillations Latband implementation removes seasonal residual oscillations

Application of LATBAND to MODIS AQUA Standard Deviation of SST residuals (VALIDATION set) by quarter ( ). Each line corresponds to a latitude band. Upper panel corresponds to latitude-specific SST; lower panel is current MODIS SST retrieval-buoy (CSST). Latband S.D. order 0.4 Current algorithm S.D. order 0.5

Next Generation MODIS + VIIRS SST algorithm Reference SST field is the V2 daily, 0.25 o Reynolds OI analysis that incorporates AMSR, Pathfinder and In Situ observations. Use of this higher spatial and temporal resolution field enables retention of Pathfinder and MODIS retrievals in high gradient regions. For MODIS and VIIRS, a 3band algorithm permits more accurate retrievals in regions of high water vapor and dust aerosols. The 3band algorithm significantly minimizes the presence of cold fringes around clouds. For MODIS (VIIRS TBD) the use of the 3-4 μ m bands extends the available retrievals in the presence of high water vapor and dust aerosols relative to the coverage provided by the μ m bands.

MODIS AQUA SST4 2 & 3 band (Night) – (3.75, 3.95, 4.05 μm) GHRSST Quality 4 (Reynolds V2 – MODIS) Latband 2 band Latband 3 band Difference Field Reynolds V2- MODIS Yellow, Red MODIS Cold

MODIS AQUA 2 and 3 band (8.6, 11, 12μ) Night - Latband SST- GHRSST Quality 4 2 Band 02 Jul 06 3 Band, 02 Jul 06 Difference Field Reynolds V2- MODIS Yellow, Red MODIS Cold Use of Q0 data removes cold retrievals

Histograms of MODIS AQUA SST residuals Latband algorithm has reduced cold residuals Top panel shows distribution of latitude SST residuals, bottom panel shows old SST (CSST). The vertical lines correspond to, from left to right, percentiles 0.05, 0.25, 0.50, 0.75, and From MODIS satellite-in situ match-up database. Residuals calculated as Satellite - Buoy Histograms comparing Latband retrievals for 2 and 3-band nighttime SST, MODIS AQUA night 02 Jul 06 Reynolds V2 – (2 or 3) band Q0 residuals Dashed curve is 2-band algorithm (‘cold tail’) Solid curve is 3-band algorithm (more symmetric) 2-band algorithm has increased number of colder retrievals 3 band algorithm reduces cold retrievals 3 band algorithm reduces cold retrievals

MODIS AQUA compared to Reynolds V2 Black – Quality 5, Red – Quality 4 July 2, 2006,East Coast US, Gulf Mex, Caribbean Two bandThree Band Quality 4 includes data approaching cloud fronts, higher water vapor regions and dust aerosols. Use of 3 band provides improved retrievals.

AQUA MODIS Summary statistics for SST and SST4 Full Swath, all StdDev assigned to MODIS, buoy ref Current Algorithm StdDevSST 0.45 – 0.5SST4 0.4 Data SetMinQ1MedianMeanQ3MaxStdDevN 2 band Latitude SST Training Validation band Dust SST Training Validation Latitude SST4 Training Validation Dust SST4 Training Validation Nighttime only – Δ distance < 11kmΔ time < 30 minutes

After grouping MDB records by quality level the dataset is partitioned into a multi-dimensional array with the following 7 dimensions: -time by season (4) -latitude bands (5 steps in 20 degree from 60S to 60N) -surface temperature (8 increments in 5 degree steps) -satellite zenith angle (4 increments) -brightness temperature difference as a proxy for water vapor (4 intervals for 4µ and 3 intervals for 11-12µ SST) -retrieved satellite SST quality level (2 intervals ql=0 and 1) -day/night selection (2 intervals). Can consider interpolation for red dimension

AQUA LATBAND Distribution of RMS as a function of Satellite Zenith Angle and SST The diagram shows SST as a function of (a) buoy SST (x-axis) and (b) satellite zenith angle. The density of occurrence of residuals in the bivariate space is indicated by the colors in each bin (hexagon); the color scale is shown on the right. The overlaid contours indicate the RMS of SST residuals inside each bin. This graphic is intended to help build a “data hypercube”. 0.5 RMS 0.4 RMS <0.4 RMS RMS < 0.4 for 0<T<27C and 0<SatZ<40 RMS < 0.5 for 27<T<30C and 40<SatZ<60

Conclusions New algorithm approaches (Latband) has resulted in a significant reduction of uncertainty in IR satellite SST retrievals S.D. reduced from 0.5 to <0.4 for 11μm band retrievals Future implementation of 3 band algorithm for MODIS and VIIRS suggests that cold fringes around clouds and aerosols can be detected and correction in these conditions is significantly improved. Latband and 3 band algorithm approaches will be implemented for VIIRS. AVHRR, MODIS and VIIRS satellite observations will be available through community accessible SEADAS programs. Pathfinder processing is being transferred to NODC to ensure continued availability of the multi-decade Pathfinder SST time series.

END

Boxplot of latitude-SST residuals (validation SET), by quarter (Latband AQUA MODIS). Central box contains 50% of residuals, outer bars represent 95% of residuals. Most residuals are within ± 1.5K. Each zonal band consistent with time with same monthly coefficient set applied for each year. Time, by quarter from July 2002 to April, 2008 AQUA SST residuals (SST- buoy)

NOAA-7 added to Pathfinder Time Series RSMAS work supported by Chet Ropelewski, NOAA CPC Challenges – Few buoy in-situ observations exist during this period – El Chichon eruption – volcanic aerosols Approach – Collaboration with R. Reynolds to provide access to ship observations corrected to buoy reference – Iterative approach to compute N7 SST fields (Miami) and OI reference fields (Reynolds, NCDC) using V2 OI ( daily, 1/4° resolution)

V6 MODIS & Pathfinder Status SEADAS code base is being used to support MODIS and is being modified to support AVHRR Pathfinder, will facilitate distribution to interested users MODIS Latband code recently delivered to GSFC for incorporation into SEADAS AVHRR support – Add GHRSST format output files including hypercube – L2GEN Pathfinder (SEADAS) has been delivered to Ken Casey, will need to be integrated into current GSFC L2GEN version. Tested on N16, 17, 18 – Latband for NOAA sensors will be integrated when NOAA SDS funding becomes available

Next generation MODIS SST algorithm results 02 JUL 06 Nighttime 4 um SST Using Latband + 3band algo 4um SST + cloudy areas filled with Reynolds V2