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)

CNR work in particular concerns : Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products. Improvement of techniques to merge infrared sensors in the Mediterranean Sea.  Run MFS L4_processor only with L2P  Configure Medspiration L4_processor – identify possible evolutions/bugs in the code Continuation of the production of MFSTEP analyses (after the end of MFSTEP). EXPECTED DELIVERABLES:  Month 20:Report on the inter-comparison of MEDSPIRATION and MFSTEP SST analyses over the Mediterranean Sea.  Month 30:Report on the merging of AVHRR, AATSR and MODIS (from TERRA and AQUA) SST products in the Mediterranean Sea

Results already described at Brest meeting: Could Medspiration L4 sub-sampled over MFS grid be used in the assimilation? not with present configuration Could the present MFS analysis be improved by including Medspiration L2P data? Yes. possibly to be implemented operationally What was left: Can MFS L4 be run only with Medspiration L2P? Can the Medspiration L4 software be run to interpolate directly on MFS model grid, with stable results, possibly improving MFS?

Period examined: jan-oct 2005 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

REMARKS: MFS and CLS processors have different data editing and selection criteria/strategies -bias between sensors -selection of valid input (confidence values, clouds) -selection of influential observations within the bubble but the different OI configurations can be tuned to have - similar spatial and temporal influential radius (‘bubble’)… - same correlation function - same grid/resolution

MFSTEP –spatial distribution of XBT measurements Evaluation of SENSOR BIASES  needed to choose a reference sensor for data merging with MFS processor  impact on quality of MERSEA L4

Evaluation of SENSOR BIASES: results

L =180 km τ =7 days MERSEA: SELMS_LIST > NAR17_SST AVHRR17_L NAR16_SST AVHRR16_L X_BUL_RATIO=2.0 Y_BUL_RATIO=2.0 T_BUL_RATIO=1.0 OAN_KEEP_ALL_MEAS = 0 #For each sensor, in each cell of the collated file, only the nearest #measurement to the processing date is kept. There is only one #observation file created per sensor. # #OAN_KEEP_ALL_MEAS = 1 # For each sensor, in each cell of the collated file, the whole #measurements are kept. There is one observation file per sensor and #per days created. MFS: Reference sensor priority: AATSR NAR17 AVHRR17_L SEVIRI NAR16 AVHRR16_L DIST=200starting spatial influential radius for data selection. RMAXDIST=600maximum spatial influential radius for data selection (the influential radius is incremented if data selected within DIST are less then LIMIT). NPIX=3number of values selected in time for each pixel. Medspiration results:

MFS and Medspiration L4 original configuration MFS (only AVHRR) MEDSPIRATION L4 subsampled at 1/16°

MFS with new INPUT data MFS (MFS+infrared L2P)MFS (infrared L2P only)

MERSEA L4 (CLS processor with new configuration) MERSEA (no bias) MERSEA (bias correction)

MFS with new INPUT data

MFS and Medspiration L4 original configuration

MERSEA L4 (CLS processor with new configuration)

MFS and MERSEA new L4 configuration

MFS and Medspiration L4 original configuration

MFS with new INPUT data

MERSEA L4 (CLS processor with new configuration)

MFS and MERSEA new L4 configuration

No bias correction Bias correction applied Impact of bias correction on MERSEA L4

(A1d) bias, OAN_KEEP_ALL_MEAS=1 (A2e) bias, OAN_KEEP_ALL_MEAS=0 Impact of the number of observations per sensor

Bugs in the L4 processor code and suggested evolution when using data from a temporal window larger then 1 day, a different first guess (climatology file) is used for each input file.  same first guess should be subtracted from each of the observations within the influential bubble SOLVED BY CLS but we don’t have the new code! parameter AB_CEIL and corresponding check on maximum allowed correlation between observations and interpolation points have no sense.  Observation sub-sampling should be performed selecting only data that are not too much cross correlated. Correlation between observations and interpolation point must be high… One possible evolution #OAN_KEEP_ALL_MEAS = N #For each sensor, in each cell of the collated file, only the first N nearest in time #measurements are kept. There is one observation file per sensor and #per days created???? other possible evolution: data reduction strategy?

CONCLUSIONS Could Medspiration L4 sub-sampled over MFS grid be used in the assimilation? not with present configuration Could the present MFS analysis be improved by including Medspiration L2P data? Yes, already done, possibly to be implemented operationally Can it be run only with Medspiration L2P? Yes, already done, possibly to be implemented operationally Can the Medspiration L4 software be run to interpolate directly on the model grid, with stable results, possibly improving MFS ? Possibly yes, but more work has to be done (understand biases, reduce computation time)