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General Objective:

CNR contribution to MERSEA SST activities (Task 2.2). General Objective:

chiquita-nunez
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General Objective:

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  1. CNR contribution to MERSEA SST activities (Task 2.2) General Objective: Conduct R&D activities to improve the quality of SST products used by MERSEA modeling and assimilation centers and produce global, Atlantic and Mediterranean Sea analyzed SST fields needed for MERSEA regional and global models

  2. CNR work within MERSEA concerns: • Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products. concluded • Tuning of Medspiration L4_processor stopped due to software licence issues • -configured Medspiration L4_processor for the Mediterranean • -identified possible evolutions/problems in the L4_processor code • -started tests on 2-step interpolation • Improvement of MFSTEP analyses • -Run MFS L4_processor only with L2P • -Update MFS format to standard GHRSST conventions • -Implementation of the new MFS-L4 production in the operational chain • -include MODIS data in the analyses

  3. Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products. Methods: Evaluation of processor performance: -qualitative -quantitative Comparison of SST L4 against quality controlled in situ XBT data acquired within MFSTEP Test performed: MFS at 1/16°AVHRR by CNR+CMS merging MFS data and SEVIRI/AATSR L2P only L2P (all infrared) original configuration Medspiration L4 at 2 kmresampled at 1/16° L2P original configuration Medspiration L4 at 1/16° (hereafter MERSEA L4) L2P different configurations starting from parameters similar to MFS ones

  4. Medspiration L4_processor configuration for the Mediterranean • Medspiration scheme has been tuned on the base of MFS processor (best performance with L2P in input): - similarspatial and temporal influential radius (‘bubble’)… - same correlation function - same grid/resolution • However the two processors have different data editing and selection criteria/strategies -bias between sensors MFS: adjustment to a reference sensor Medspiration: adjustment through OI (error covariance matrix) -selection of valid input (confidence values, clouds) -selection of influential observations within the bubble number of observations data reduction temporal selection

  5. THE SENSOR BIAS ISSUE: background • MFS: • Reference sensor ”merged files” • Interpolation uses in input ‘merged’ files (1 SST map per day) • Merging procedure selects valid pixels using the sensor sequence below: • AATSR NAR17 AVHRR17_L SEVIRI NAR16 AVHRR16_L • Before adding data to the merged map, the bias between each new image and the pixels that have already been merged is estimated and removed • (only if sufficient co-located pixels are found) • MERSEA: • No preliminar adjustment performed”collated files” • Data reduction in time (through OAN_KEEP_ALL_MEAS parameter) • Selection of best value for the same sensor: • SELMS_LIST > NAR17_SST AVHRR17_L NAR16_SST AVHRR16_L • BIAS adjustment within the OI algorithm: • The error covariance is calculated as • for points i, j , where b2, ELW are the variance of the white measurement noise and the bias error coming from a given SSES_Bias_error, respectively.

  6. MFS and Medspiration L4 original configuration

  7. MFS with new INPUT data

  8. MERSEA L4 (CLS processor with new configuration)

  9. MFS and MERSEA new L4 configuration

  10. MERSEA L4_processor configuration (GOS) Main results: MFS (L2P in input) Bias correction – Original signal variance  rms similar to MFS, higher bias vs in situ Bias correction (1 to 3 times the estimated MBE for each sensor) Lower signal variance (1-5 °C)  improved rms, bias always there! MBE=-0.11 °C Rms=0.46 °C MBE=-0.21 °C Rms=0.47 °C MBE=-0.22 °C Rms=0.41 °C

  11. THE SENSOR BIAS ISSUE: Conclusions MFS: Reference sensor ”merged files” -Maybe not optimal but sufficiently accurate -Very large scale bias adjustment • MERSEA: • BIAS adjustment within the OI algorithm: • -NOT accurate (in terms of MBE) BUT reduces the rms vs InSitu data • Performed only on the data effectively selected within the influential bubble (the first N most correlated values) • not evident ‘a priori’ how many data from each sensor are effectively selected and how distant they are •  is there a data selection/sub-sampling issue? -Assumes the bias does not change from one day to another for the same sensor(if different images are selected for the same sensor) • …the scale of the correction is related to the influential bubble: is this the origin of the rms improvement? • Preliminar adjustment required”adjusted collated files”

  12. Interpolation performed in 2 (or n) steps: • Create collated files at lower resolution (LR) • Interpolate with large decorrelation radius and large influential bubble • Substitute the LR SST field to the climatological first guess • Create high resolution collated files • Interpolate at HR using small decorrelation radius and small bubble  tests (using ‘a priori’ decorrelation radius) have been performed but were stopped due to license issues

  13. MFS format updated to standard GHRSST conventions and included in MERSEA catalog: variablesfilenames and data descriptions

  14. The SST Average from 1985 to 2005 The average and the time series

  15. The warm summer 2006 The 2006 SST anomaly was monitored in near real time by the GOS SST Processing system daily SST anomaly respect to the 1985-2004 climatology Time series of SST mean in the West Med

  16. MODIS data inclusion in the processing chain (ongoing) 1)estimate sensor bias 2)test cloud flagging MODIS Merged L2P

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