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JPSS and GOES-R SST Sasha Ignatov John Stroup, Yury Kihai, Boris Petrenko, Prasanjit Dash, Xingming Liang, Irina Gladkova, John Sapper, Feng Xu, Xinjia.

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Presentation on theme: "JPSS and GOES-R SST Sasha Ignatov John Stroup, Yury Kihai, Boris Petrenko, Prasanjit Dash, Xingming Liang, Irina Gladkova, John Sapper, Feng Xu, Xinjia."— Presentation transcript:

1 JPSS and GOES-R SST Sasha Ignatov John Stroup, Yury Kihai, Boris Petrenko, Prasanjit Dash, Xingming Liang, Irina Gladkova, John Sapper, Feng Xu, Xinjia Zhou, Maxim Kramar, Yaoxian Huang, Marouan Bouali, Karlis Mikelsons NOAA; CIRA; GST Inc; CUNY Bruce Brasnett Canadian Met Centre 25 February 2015JPSS and GOES-R SST NOAA Satellite Science Week February 2015, Boulder, CO

2 Outline JPSS SST –From POES/EOS to JPSS (via NPOESS) –JPSS SST Product – ACSPO (Advanced Clear-Sky Processor for Oceans) –ACSPO SST replaces the initial “IDPS SST EDR” –NOAA SST Monitoring –Users GOES-R SST: Work in progress –Himawari-8 SST Project –NOAA SST Monitoring is updated to include Geo –Polar and Geo ACSPO codes are consolidated 25 February 20152JPSS and GOES-R SST

3 JPSS – Joint Polar Satellite System (1:30 am/pm orbit) S-NPP (Oct’2011) J1 (2017), J2 (2023) S-NPP – The Suomi National Polar-orbiting Partnership Bridge between NOAA POES / NASA EOS and JPSS Successfully launched on 28 October 2011 VIIRS – Visible Infrared Imager Radiometer Suite Replaces AVHRR – workhorse onboard NOAA / METOP Builds on MODIS heritage: Multispectral, high spatial resolution, high radiometric performance and imagery VIIRS Data Products -RDRs: Raw Data Records (L1A) -SDRs: Sensor Data Records (L1B) -EDRs: Environmental Data Records (L2) JPSS, S-NPP, VIIRS 25 February 20153JPSS and GOES-R SST

4 Role / Responsibility NOAA POES, NASA EOS NPOESS JPSS (Owned by NOAA, 2010) Platform, Launch Vehicle Private Industry InstrumentsPrivate Industry AlgorithmsGovernmentPrivate IndustryGovernment Data ProductsGovernmentPrivate IndustryGovernment Cal/ValGovernmentPrivate IndustryGovernment Archival & Distribution Government NPOESS Data Products: IDPS – Interface Data Processing Segment 25 February 20154JPSS and GOES-R SST Algorithms: NPOESS/IPO/Northrop Grumman Operational Products: NPOESS/IPO/Raytheon IDPS (RDR, SDR, EDR)

5 ACSPO – NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) NOAA heritage SST system, operational with AVHRR 4km/GAC & 1km/FRAC Jan 2012: Experimental with VIIRS and Terra/Aqua MODIS Mar –May 2014: Operational with VIIRS: & NODC Reported in10min granules (aggregate of original 86sec granules) IDPS – Interface Data Processing Segment (IDPS) NPOESS SST EDR was developed by Northrop Grumman Operated by Raytheon, Archived at CLASS. Transitioned to NOAA in 2010 Reported in original 86sec granules NOAA ended up with two VIIRS SST Products Evaluation of IDPS EDR has shown large room for improvement IDPS SST has substantially improved, but still outperformed by ACSPO There was a confusion in users’ community about 2 JPSS products at NOAA Users requested ACSPO SST and expressed no interest in IDPS EDR In Jan 2014, JPSS Program Office recommends “discontinue the IDPS SST EDR, and concentrate on ACSPO sustainment, development, and Cal/Val” VIIRS SST Products at NOAA 25 February 20155JPSS and GOES-R SST

6 625 February 20156JPSS and GOES-R SST STAR leads monitoring and Cal/Val of NOAA and partners’ SST products Products are monitored online in near-real time, to ensure high quality & consistency, to support and facilitate SST applications NOAA SST Monitoring

7 DAY: ACSPO L2 minus CMC L4 16 February February 2015JPSS and GOES-R SST7 Delta close to zero as expected Cold spots – Residual Cloud/Aerosol leakages Warm spots – Diurnal warming

8 DAY: IDPS L2 minus CMC L4 16 February February 2015JPSS and GOES-R SST8 IDPS SST Algorithms consistent with ACSPO Residual cloud leakages more pronounced Diurnal Warming spots consistent with ACSPO

9 DAY: ACSPO L2 minus in situ SST 16 February February 2015JPSS and GOES-R SST9 Shape close to Gaussian, cold tail suggests residual cloud Performance Stats well within specs (Bias<0.2K, STD<0.6K)

10 NIGHT: IDPS L2 minus in situ SST 16 February February 2015JPSS and GOES-R SST10 Cold tail more pronounced – more residual cloud Performance Stats degraded compared to ACSPO On this particular day, IDPS is not meeting specs

11 DAY STD DEV wrt. in situ SST IDPS SST improved but still out of family & not meeting specs 25 February 2015JPSS and GOES-R SST11 Wrt. OSTIA SST, the pedestal is smaller– OSTIA “internal noise” smaller Both OSISAF and ACSPO STDs are reduced, but OSISAF to a greater extent. Recall that OSISAF L2 is assimilated in OSTIA L4 and ACSPO is not All ACSPO products from AVHRR, MODIS, and VIIRS in family and meeting specs OSISAF Metop-A ACSPO Metop-A

12 Some Early Results Assimilating ACSPO VIIRS L2P Datasets Bruce Brasnett Canadian Meteorological Centre May, 2014

13 ACSPO VIIRS L2P Datasets Received courtesy of colleagues at STAR Two periods: 1 Jan – 31 Mar 2014 & 15 Aug – 9 Sep 2013 Experiments carried out assimilating VIIRS data only and VIIRS data in combination with other satellite products Rely on independent data from Argo floats to verify results (Argo floats do not sample coastal regions or marginal seas) 25 February JPSS and GOES-R SST

14 Assessing relative value of 2 VIIRS datasets: NAVO vs. ACSPO Using ACSPO improves CMC assimilation, at all latitudes 25 February JPSS and GOES-R SST

15 CMC Summary ACSPO VIIRS L2P is an excellent product Daily coverage with this product is very good over internal and coastal waters, in the Tropics, and in the High Latitudes Based on the Jan – Mar 2014 sample, ACSPO VIIRS contains more information than either the NAVO VIIRS, OSI-SAF Metop-A or the RSS AMSR2 datasets CMC assimilated ACSPO VIIRS SST in May 2014, as soon as it was archived at PO.DAAC and NODC 25 February JPSS and GOES-R SST

16 1625 February JPSS and GOES-R SST SST is an essential climate variable VIIRS/ABI unprecedented imagery, accuracy, precision, spatial/temporal resolution lead to superior SST analysis, forecast, applications JPSS SST is fused with other satellite and in situ SSTs, to produce blended L4 products. GOES-R SST will be included in analyses VIIRS/ABI resolution & quality is unique, and allows exploration of new techniques: Pattern recognition, Temporal analysis, and Radiative transfer methodology Continuous monitoring and validation of the products in near real time is needed, for optimal applications S-NPP VIIRS MSG SEVIRI JPSS and GOES-R SST

17 ACSPO SST and Monitoring JPSS SST: Retrieval and Monitoring in advanced stage ACSPO retrieval domain and performance statistics comparable, or superior to other community products JPSS SST used in blended products (NOAA geo-polar blended, CMC L4; UK MO OSTIA, BoM GAMSSA, JMA MGD) Focus on users – working individually, addressing concerns Geo work underway Himawari SST Project Consolidation of Geo/Polar ACSPO SST codes Incorporation of Geo SST in NOAA SST Monitoring Polar work underway Generation of JPSS L3 (requested by CMC, UK MO, BoM, JMA) Reprocessing L2 and L3 back to Jan 2012 and Archival Conclusion and Ongoing Work 25 February JPSS and GOES-R SST

18 Thank You! Questions? 25 February JPSS and GOES-R SST

19 Back Up Slides 25 February JPSS and GOES-R SST

20 Examples of ACSPO Imagery 25 February JPSS and GOES-R SST

21 25 February 2015JPSS and GOES-R SST21 ACSPO Florida ACSPO_V2.30b01_NPP_VIIRS_ _ _ _NAVO

22 25 February 2015JPSS and GOES-R SST22 Korea China ACSPO ACSPO_V2.30b01_NPP_VIIRS_ _ _ _NAVO

23 25 February 2015JPSS and GOES-R SST23 ACSPO Africa ACSPO_V2.30b01_NPP_VIIRS_ _ _ _NAVO

24 ACSPO destriping capability Currently under testing for NOAA Operations 25 February JPSS and GOES-R SST

25 DAY – SST from original BTs in M15 and M February 2015JPSS and GOES-R SST Striping affects quality of SST imagery and Ocean Dynamics Analyses

26 DAY – SST from destriped BTs in M15 and M February 2015JPSS and GOES-R SST Destriping is applied to brightness temperatures and improves SST

27 VIIRS vs. AVHRR and MODIS 25 February JPSS and GOES-R SST

28 CharacteristicAVHRR FRACMODISVIIRS File compressionNoYesNo Swath width, km2,9002,3303,000 Pixel km # of FOVs (Pixels) per scan line 2,0481,3543,200 # of Detectors11016 # of push brooms per 5 min interval 3, # of scan lines per 5 min interval 3,600*1=3,600203*10=2, *16=2,679 L1b file aggregation All bands + Geo = 1 file All bands = 1file + Geo = 1file Each band = 1file + Geo = 1file # of L1b files/24hr28 ×Half-orbits576 × 5min ~4,000 × 86sec (3 bands+1 geo) AVHRR, MODIS and VIIRS Characteristics 25 February JPSS and GOES-R SST

29 VIIRS 1 MODIS – Aqua 2 AVHRR 3 (A)ATSR 4 λ, µm NEDT, λ, µm NEDT, λ, µm NEDT, λ, µm NEDT, Not available on VIIRS Used by U. Miami for producing MO(Y)D Currently not used Currently not used Specs On-Orbit 1 Cao and VIIRS SDR Team, https://cs.star.nesdis.noaa.gov/NCC/VIIRS https://cs.star.nesdis.noaa.gov/NCC/VIIRS 2 Xiong et al., IEEE/TGRS, 2009; 3 Trishchenko et al., JGR, 2002 (NOAA9-16); 4 Merchant and Embury, Personal communication, 2012 NEdT in SST Bands (BB-based; Not aggregated pixels) 25 February JPSS and GOES-R SST


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