1. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Recent developments to the SST Quality Monitor (SQUAM) and SST validation with In situ SST Quality Monitor (iQUAM) data http://www.star.nesdis.noaa.gov/sod/sst/squam http://www.star.nesdis.noaa.gov/sod/sst/iquam GHRSST XI Science Team Meeting 21-25 June 2010, Lima, Peru Alexander Ignatov 1, Prasanjit Dash 1,2 and Pierre LeBorgne 3 1 NOAA/NESDIS, Center for Satellite Applications & Research (STAR) 2 Colorado State Univ, Cooperative Institute for Research in the Atmosphere (CIRA) 3 Meteo-France, Satellite Meteorology Centre 1

2. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Initial Objectives of SQUAM  Monitor NESDIS operational AVHRR SST products in NRT Heritage Main Unit Task (MUT, 2001-pr) Advanced Clear-Sky Processor for Oceans (ACSPO, 2008-pr) for stability, self-consistency, cross-platform & cross- product consistency  Evaluate satellite SST products daily in global domain, against global L4 fields (Reynolds, RTG, OSTIA, ODYSSEA)  Quickly identify anomalies & facilitate product diagnostics (e.g., due to sensor malfunction, cloud mask, or SST algorithm) 2

3. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Recent new additions  Worked with NCEP to add NRT inter-comparison of daily L4 SSTs : http://www.star.nesdis.noaa.gov/sod/sst/squam/L4/ Two Reynolds (AVHRR-only and AVHRR+AMSR) Two RTG (low and high-resolution) OSTIA ODYSSEA  Collaborated with NAVOCEANO to include SEATEMP GAC SSTs : http://www.star.nesdis.noaa.gov/sod/sst/squam/NAVO/ Platform/sensor: AVHRRs onboard NOAA-14 through 19,MetOp-A Time: 2000 – recent  MetOp-A AVHRR ~1km FRAC SST products were added : http://www.star.nesdis.noaa.gov/sod/sst/squam/FRAC/ NESDIS: ACSPO FRAC Worked with O&SI SAF (MGR SST) 3

4. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Premises of validating against L4  In situ measurements have limitations :  Sparse and geographically biased  Quality non-uniform and suboptimal  Not available in NRT in sufficient numbers  SQUAM complements heritage VAL against in situ:  Calculates ΔT S = Satellite SST (T S ) – L4 SST (T R ) Tabs for ΔT S in SQUAM  Maps  Histograms  Statistical time series  Dependencies  Hovmöller time series (selected products) 4

5. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Maps of ΔT S (T S -T R ) Maps are used to assess satellite SST globally “at a glance” MetOp-A – OSTIA, NESDIS ACSPO 1km FRAC, 17-Jan-2010, Night 5

6. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Histograms of ΔT S (T S -T R ): Reference SST: In situ (from CalVal system) 30 days of data: ~7,000 match-ups with in situ SST Median = -0.04 K; Robust STD = 0.27 K MUT satellite SST – quality controlled in situ SST (iQUAM)**, Night, Mar-2009 ** Quality controlled monthly in situ data available at: http://www.star.nesdis.noaa.gov/sod/sst/iquam 6

7. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Histograms of ΔT S (T S -T R ): Reference SST: OSTIA (from MUT SQUAM system) More at SQUAM web MUT satellite night SST – OSTIA, 11-May to 20-May 2009 8 days of data: ~500,000 match-ups with OSTIA Median = 0.00 K; Robust STD = 0.30 K 7

8. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Time Series (NESDIS MUT SST) Reference SST: In situ SST 1 data point = 1 month match-up with in situ data Median bias within ~0.1 K (except N16 – sensor problems) MUT satellite SST – quality controlled in situ SST (iQUAM), Night 8

9. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Time series (NESDIS MUT SST) Reference SST: Daily Reynolds More at SQUAM web 1 data point = 1 week match-up with OSTIA Patterns reproducible yet crisper (finer temporal resolution) Short-term noise in time series: artifacts in Reynolds SST MUT satellite night SST – daily Reynolds 9

10. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Artificial dependencies View zenith angle Such ‘retrieval-space’ dependent biases are difficult to uncover and quantify using customary validation against in situ data, which do not fully cover the retrieval space. The SQUAM diagnostics helped uncover a bug in the MUT SST which was causing across-swath bias >0.7K. After correction, bias reduced to ~0.2K and symmetric with respect to nadir. 10

11. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Timeseries zonal dependences (MetOp-A FRAC - OSTIA: O&SI SAF, ACSPO) Timeseries plots of Mean ΔT S is used to detect persistent areas of cloud contamination. More combinations available at SQUAM web. 11

12. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Summary and Future Work  SQUAM currently monitors:  Three NESDIS AVHRR SSTs MUT GAC (NOAA-16 to NOAA-19, MetOp-A) ACSPO FRAC ACSPO GAC  O&SI SAF FRAC (together with NESDIS FRAC)  NAVOCEANO SEATEMP GAC (NOAA-14 to NOAA-19, MetOp-A)  Inter-comparison of six daily Level-4 (L4) products  Products show high degree of cross-platform and day-night consistency but there is room for improvement  Using data from In situ Quality Monitor (iQUAM), validation activity is ongoing with quality controlled buoy data (more results will be shown in next term).  Future plans  Reconcile NESDIS AVHRR SSTs (different platforms, Day-Night): Improve AVHRR sensor calibration (NESDIS) Adjust T REF for diurnal cycle (e.g., Gentemann model) Improve NESDIS SST product (cloud screening, SST algorithms)  Include more L4 SSTs to L4-SQUAM prototype (K10, GHRSST GMPE) Work with L4 producers to reconcile different L4s and satellite SSTs THANK YOU! 12

13. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru L4 intercomparisons in SQUAM Using Double Difference for cross-platform consistency BACK-UP SLIDES 13

14. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru L4 comparisons 14

15. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru L4 intercomparisons in SQUAM “The best” L4 SST is not easy to identify. For diagnostics & monitoring, any L4 may be used. Currently, the L4 SSTs show large differences, especially in the high latitudes. This should be coordinated among developers and resolved. More L4-analyses at http://www.star.nesdis.noaa.gov/sod/sst/squam/L4/ Reynolds minus OSTIA L4 SST – RTG (low resolution), Mean 15

16. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru  Cross-platform consistency of T SAT can be evaluated from time series of T SAT - T REF overlaid for different platforms  For more quantitative analyses, one ‘reference’ platform can be selected & subtracted from all other ( T SAT - T REF )  N17 was selected as ‘reference’, because it is available for the full SQUAM period, and its AVHRR is stable  Double-differences (DD) were calculated as DD = ( T SAT - T REF ) - ( T N17 - T REF ) for SAT=N16, N18, and MetOp-A Cross-Platform Consistency Using Double-Differences (T SAT – T SAT_REF ) 16

17. GHRSST XI Science Team Meeting, ST-VAL, 21-25 June 2010, Lima, Peru Quantitative evaluation of cross-platform consistency using Double-differences (DD): DD = ( T SAT - T REF ) - ( T N17 - T REF ) Cross-Platform Consistency in MUT Using Double-Differences (T SAT – T SAT_REF ) Choice of third transfer standard is not critical. More analyses at: http://www.star.nesdis.noaa.gov/sod/sst/squam 17