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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.

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Presentation on theme: "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."— Presentation transcript:

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

4 GHRSST XI Science Team Meeting, ST-VAL, 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, 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, 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 = 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: 6

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

15 GHRSST XI Science Team Meeting, ST-VAL, 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 Reynolds minus OSTIA L4 SST – RTG (low resolution), Mean 15

16 GHRSST XI Science Team Meeting, ST-VAL, 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, 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: 17


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