NASA s Multi-mission Ocean Color Data Record
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1 NASA s Multi-mission Ocean Color Data Record Bryan Franz Ocean Ecology Laboratory NASA Goddard Space Flight Center 2016 CLEO Workshop 6-9 September 2016, ESA/ESRIN, Frascati, Italy
2 Outline NASA Ocean Biology Processing Group Approach to producing long-term record of global ocean color for climate research Results of latest multi-mission processing, focusing on long-term consistency Future plans
3 NASA Ocean Biology Processing Group Serves as the Science Investigation Processing System (SIPS) for all ocean color measurements collected or supported by NASA. Integrated with the Ocean Biology DAAC (OB.DAAC), which is the archive and distribution facility for all ocean biology datasets within NASA s Earth Science Data Information Systems (ESDIS) program. Primary focus is to produce a longterm, consistent record of global ocean color measurements spanning multiple missions. Global Processing & Distribution VIIRS/NPP (USA) * MODIS/Aqua (USA) * MODIS/Terra (USA) * SeaWiFS (USA) CZCS (USA) MERIS (Europe) OCTS (Japan) Regional Processing & Distribution GOCI (Korea) * HICO (USA) Limited Mission Support Landsat-8/OLI (USA) * OCM-1/2 (India) OSMI (Korea) MOS (Germany) * active missions 3
4 Ocean Science Missions Ocean Biology Processing Group Science Data Science Processing data Ground Segment Mission Operations instrument schedules command loads science algorithms Instrument Operations instrument calibration data science software Science Software reprocessing Ocean Biology DAAC Project Science Calibration Support Teams Instrument Calibration instrument calibration science products Product Validation validation data derived products Other DAACs validation results External Elements science algorithms field calibration data field validation data science products and user support data sw & information flight project External Science Teams Vicarious Cal System Research Community OBPG functions science products and user support 4
5 climate study requires multi-mission timeseries SeaWiFS MERIS MODIS-Aqua VIIRS-SNPP OLCI-S3A VIIRS-J1 OLCI-S3B SGLI-GCOMC PACE
6 How do we achieve consistency? Focus on instrument calibration establishing temporal and spatial stability within each mission Apply common algorithms ensuring consistency of processing across missions Apply common vicarious calibration approach ensuring spectral and absolute consistency of water-leaving radiance retrievals under idealized conditions Perform comparative trend analyses assessing temporal stability & and mission-to-mission consistency Reprocess multi-mission timeseries incorporating calibration and algorithm advancements
7 Latest Multi-Mission Ocean Color Reprocessing Motivation incorporate knowledge gained in instrument-specific radiometric calibration and updates to vicarious calibration incorporate algorithm updates and advances from community and Mission Science Teams developed since 2010 (last algorithm change) improve interoperability of the product suite by adopting modern data format, standards, and conventions (netcdf4, CF and ISO conventions, etc.) Status CZCS, OCTS, VIIRS, MODISA, MODIST, SeaWiFS, GOCI done MERIS pending Highlights significant improvements in VIIRS and SeaWiFS temporal calibrations improved consistency between missions over common lifetimes new chlorophyll algorithm (OCI, Hu et al. 2012), improved clear water stability new suite of inherent optical properties (IOP, Werdell et al. 2013) 7
8 R Instrument Calibration Changes VIIRS MODISA SeaWiFS lunar calibration now used to correct more frequent but more uncertain solar calibration trends. correction of the lunar calibration trends for time-dependent changes in spectral response of the primary mirror. additional statistical correction for detector striping. Eplee et al updated temporal calibration from MCST (V _OC2) updated temporal corrections to response versus scan angle, Meister and Franz enhanced tracking of dark offset changes at the sub-count level, Patt et al (in prep). correction for time-dependent change in system spectral response due to spectral dependence of detector responsivity changes, Eplee et al (in prep). updated trending of lunar vs earth view commanded gain
9 SeaWiFS Rrs(555) Monthly Clear-Water Anomaly Trend Before R Reprocessing Oligotrophic Region
10 SeaWiFS R Radiometric Instability Issue determined to be due to 1-count shift in avg dark offset Angstrom Chlorophyll global mean oligotrophic anomaly trends Offset (Counts) Rrs(555) 865nm Dark Offsets median Rrs(443) +3e-4 median -3e-4 +3e-4-3e-4 Time Time
11 SeaWiFS R Recalibration correct for 1-count shift and changing system spectral response Angstrom Chlorophyll global mean oligotrophic anomaly trends Rrs(555) +3e-4-3e-4 Rrs(443) +3e-4-3e-4 Time Time 11
12 Global Subsets for Temporal Stability Assessment Deep Water Subset (+1000m) Oligotrophic Mesotrophic Eutrophic
13 SeaWiFS & MODISA Rrs(λ) Deep-Water Time-Series showing good agreement R SeaWiFS = solid line MODISA = dashed line Mean Ratio
14 SeaWiFS & MODISA Rrs(λ) Clear-Water Time-Series showing good agreement R SeaWiFS = solid line MODISA = dashed line Mean Ratio
15 MODISA & VIIRS Rrs(λ) Deep-Water Time-Series showing good agreement R MODISA = solid line VIIRSN = dashed line suspect decline in MODISA Rrs(412) Mean Ratio
16 MODISA & VIIRS Rrs(λ) Clear-Water Time-Series showing good agreement R MODISA = solid line VIIRSN = dashed line Mean Ratio
17 Rrs(λ) Spectral Comparison common mission mean by water type SeaWiFS and MODISA MODISA and VIIRS SeaWiFS = filled circle MODISA = diamond MODISA = filled circle VIIRSN = diamond Oligotrophic Mesotrophic Eutrophic
18 Chlorophyll Algorithm Change SeaWiFS OC4 SeaWiFS OCI Hu, C., Lee, Z., & Franz, B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research, 117(C1).
19 SeaWiFS and MODISA R Chlorophyll Clear-Water Time-Series OCI OCI algorithm yields higher values of chlorophyll in clear water relative to OCx ( to 0.01 mg m -3 ), with improved consistency between sensors. OCx Mean Stdv MODISA mg m -3 SeaWiFS mg m -3 MODIS/SeaWiFS
20 OCI MODISA and VIIRS R Chlorophyll Clear-Water Time-Series OCI algorithm yields higher values of chlorophyll in clear water relative to OCx (+0.01 to mg m -3 ), with improved consistency between sensors. OCx Mean Stdv MODISA mg m -3 VIIRS mg m -3 VIIRS/MODISA
21 SeaWiFS and MODISA R Chlorophyll Deep-Water Time-Series Mean Stdv SeaWiFS mg m -3 MODISA mg m -3 MODIS/SeaWiFS MODISA = solid line VIIRSN = dashed line Black = OCI algorithm Blue = OCx algorithm 21
22 MODISA and VIIRS R Chlorophyll Deep-Water Time-Series Mean Stdv MODISA mg m -3 VIIRS mg m -3 VIIRS/MODISA MODISA VIIRS degraded confidence in MODISA trends 22
23 Seasonal Chlorophyll Comparison (2015) MODISA VIIRS Fall Spring 23
24 Seasonal Chlorophyll Comparison (2005) SeaWiFS MODISA Fall Spring 24
25 Long-term (18-year) record of phytoplankton chlorophyll a for mid-latitude oceans (± 40⁰), constructed from R Chla (mg m -3 ) Chla (%) SeaWiFS MODISA VIIRS Chla Anomaly (mg m -3 ) Permanently Stratified Ocean (PSO) mean SST > 15C Multivariate Enso Index (MEI) PSO SeaWiFS MODISA VIIRS Chla Anomaly (%)
26 Long-term (18-year) record of phytoplankton chlorophyll a for mid-latitude oceans (± 40⁰), constructed from R Chla (mg m -3 ) SeaWiFS MODISA VIIRS Chla Anomaly (mg m -3 ) Chla (%) Multivariate Enso Index (MEI) SeaWiFS MODISA VIIRS Chla Anomaly (%)
27 Sentinel-3 OLCI support Future Plans OLCI processing capability integrated into NASA ocean color software (l2gen), testing in progress. operational acquisition, processing, distribution of OLCI RR and FR data expected in coming months (L1B mirror, L2-L3 NASA). MERIS reprocessing and integration likely wait for updated Level-1B from ESA Other missions coming soon J1 VIIRS: full mission processing and distribution planned GCOMC SGLI: processing support development in progress Hyperspectral mission support
28 Thanks
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