1. 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 CNR contribution to MERSEA SST activities (Task 2.2)

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 km resampled 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 ): - similar spatial 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. 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 b 2, E LW are the variance of the white measurement noise and the bias error coming from a given SSES_Bias_error, respectively. 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) THE SENSOR BIAS ISSUE: background

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=-0.22 °C Rms=0.41 °C MBE=-0.21 °C Rms=0.47 °C

11. 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” MFS: Reference sensor  ”merged files” -Maybe not optimal but sufficiently accurate -Very large scale bias adjustment THE SENSOR BIAS ISSUE: Conclusions

12. Interpolation performed in 2 (or n) steps: 1.Create collated files at lower resolution (LR) 2.Interpolate with large decorrelation radius and large influential bubble 3.Substitute the LR SST field to the climatological first guess 4.Create high resolution collated files 5.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. KEYENTRYSEL Product nameGOS High Resolution gridded SST foundation over Mediterranean Sea (short name: YYYYMMDD-GOS-L4HRfnd-MED-NRTv0.NC) near-real-time OISST from AVHRR only (short name: YYYYMMDD-GOS-L4HRfnd-MED-DTv0.NC) delayed-time OISST from AVHRR only (short name: YYYYMMDD-GOS-L4HRfnd-MED-RAv0.NC) Re-Analysis - OISST from AVHRR only (short name: YYYYMMDD-GOS-L4HRfnd-MED-NRTv1.NC) near-real-time OISST from infrared L2P (short name: YYYYMMDD-GOS-L4HRfnd-MED-DTv1.NC) delayed-time OISST from infrared L2P YES Product IdNO Product TypeRS datasetsYES Output TypeGriddedNO Product overview Dataset of gap-free daily sea surface temperature maps (L4 product) produced on the Mediterranean Forecasting System OGCM grid at 1/16° spatial resolution. This L4 SST product is assumed to represent night time SST around 00 UTC and is obtained from AVHRR night-time passages only. Daily Near-Real-Time (NRT) maps are produced weekly and reprocessed in Delayed Time (DT) after 2 weeks to include all relevant measurements in the analysis. A specific Optimal Interpolation (OI) scheme developed within MFSTEP project (www.bo.ingv.it/mfstep/) is used to combine L2 measures of SST from AVHRR sensors, and to fill gaps where no observations are available. As the Mediterranean sea is characterized by the presence of many islands and peninsulas, the scheme drives a ‘multi-basin’ analysis to avoid data propagation across land from one sub-basin to the other.www.bo.ingv.it/mfstep/ NO Themes and parameter groups Physics, D025YES Parameters, Units and Conventions sea_surface_temperature, kelvin, CF-1.0YES Geographical target Spatial coverage -Longitude: -18.125°W to 36.250°E -Latitude: 30.250°S to 46.000°N YES Spatial resolution -Longitude: 0.0625° -Latitude: 0.0625° NO Geographical scale Regional scaleYES Data coordinate system Projection -projection : geographic isolongitude/isolatitudeNO Temporal coverage & resolution -start: 2004-05-19 -end: ongoing YES Temporal resolution 24 hoursNO Product Supply Center CNR-ISAC-GOSNO Delivery by TEP TEP SATYES Periodicity of update WeeklyNO Type of update DailyNO Delivery mode * URLs * protocol http://gos.ifa.rm.cnr.it/index.php?id=382NO Volume5.0 Mo (uncompressed)NO FormatNetcdfNO Format version 3.6.0-p1 of Oct 16 2005 13:23:24NO Licence / conditions Free public access by internetNO Documentatio n MFSTEP 2 nd Year Progress Report (1 March 2004 – 28 February 2005) http://www.bo.ingv.it/mfstep/Docs/2nd_Year_progress_Report/2nd _Year_Progress_Report.PDF NO Currency date of catalogue entry 2006-05-15NO Creation date of catalogue entry 2006-05-15NO MFS format updated to standard GHRSST conventions and included in MERSEA catalog: variables filenames and data descriptions

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

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

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