Inter-comparison and development of SST analyses over the Mediterranean Sea B.Buongiorno Nardelli, C.Tronconi, R.Santoleri, E.Böhm Istituto di Scienze dell’Atmosfera e del Clima – sezione di Roma Via del Fosso del Cavaliere, 100 – Roma

GOS involvement in national and international projects/programmes Mediterranean Forecasting System Adricosm Medspiration Mersea PRIMI GODAE Global High Resolution SST Pilot Project (GHRSST-PP)

MFSTEP (mar feb. 2006) Mediterranean Forecasting System Toward Environmental Predictions RT Observing System satellite SST, SLA, VOS-XBT, moored multiparametric buoys, ARGO and gliders Upgrade of present basin scale operational system New model and assimilation Ecosystem Models Validation/calibration of Coupled physical and biochemical numerical models Marine forecast downscaling Regional and shelf models nesting 15 nations involved, 48 institutions End-User applications Development of modules for oil spill monitoring, ICZM and fishery management Meteorological forecast downscaling 10 km LAMs and 4 km N.H. mesoscale models

MFSTEP sub-regional and shelf systems MFS supports sub-regional (3 km) and shelf models (1 km) nesting: weekly forecasts are produced for ALL the sub-regional models and some shelf models Sub-regional models at 3 km Shelf models at 1.5 km

FORECASTRELEASE SLA XBT SST ECMWF AN ECMWF FC J-7 J-14 J-5J-4J-3J-2J-1 Wed ThuFriSatSunMonTue ThuFriWedThuFri J+1J+2J+7J+8J+9 J Wed Data are disseminated through a Web/ftp service ( The present day MFS (SYS3) weekly forecasting system ARGO

The MFS SST system Data mergingISAC Optimal Interpolation Data delivery on the GOS-ISAC web-site Data quality control Night-time SST using MF algorithm Cloud detection SST daily composite binning on model grid (1/16x1/16) MF AVHRR acquisition Atlantic buffer zone + west Med Night-time SST using Pathfinder algorithm Cloud detection SST daily composite binning on model grid (1/16x1/16) ISAC AVHRR acquisition Entire Mediterranean

CLOUD detection algorithms Cloud detection is an essential step to provide “high quality” SST fields for data assimilation Cloud detection requires a compromise between maximization of coverage and minimization of cloud contaminated pixels Cloud detection in MFSTEP is now performed at various steps:  On original images, before the composite is performed: ISAC: building a reference SST and fixing a threshold on the base of the histogram of the differences to this reference (as already described last year by E. Böhm)  Before selecting SST data in the optimal interpolation algorithm: comparison to the nearest analysis available (if interpolation error is lower than a fixed value)

EXAMPLE of DECLOUD PROCEDURE

SST INTERPOLATION ALGORITHM Basic theory Given n SST observations Φ obs at the locations x i (both in space and time) and their associated measurement error ε i (assumed to be zero mean and uncorrelated with the signal), the Gauss-Markov theorem states that the optimal least square estimate of the SST at the location x can be obtained as a linear combination of the observations Φ obs : where C xi represents the covariance between the quantity to be estimated and the i th observation: A ij represents the covariance matrix of the observations:

Any method described as OPTIMAL necessarily becomes strongly SUB-OPTIMAL when implemented to interpolate high resolution satellite data, due to: Volume of data Computational limitations Different scales to be considered in the interpolation Any scheme needs to be built with strong FLEXIBILITY SST INTERPOLATION: practical limitations

Some details about the scheme adopted… The data used to interpolate at a certain time-space location are selected within a limited sub-domain, close to the interpolation point The most correlated observation is selected first, while all successive data are selected only if they are found along a new direction in the space-time (until n observations are found). The scheme drives a ‘multi-basin’ analysis to avoid data propagation across land, from one sub-basin to the other. SST INTERPOLATION: operational implementation

FLAG_INTFLAG_INT=1 does not interpolate if a valid observed SST value is present at the interpolation time. FLAG_INT=0 interpolates anyway. LIMITmaximum number of selected data for each interpolation point DISTstarting spatial influential radius for data selection. RMAXDISTmaximum spatial influential radius for data selection (the influential radius is incremented if data selected within DIST are less then LIMIT). NPIXnumber of values selected in time for each pixel. THRESHmaximum difference admitted between the SST in input and the reference SST (used for residual cloudy pixels removal). The reference SST is either the corresponding day optimal field (for days before the interpolation day J) or the J-1 analysis for the interpolation day (J) and successive days (J+1, J+2,...) RMAXERROPTmaximum % error admitted on the reference SST field to activate the residual cloudy pixels removal. IBXdimension of the moving window used for cloud erosion MINSSTVALIDminimum SST value considered valid OI scheme configurable parameters

MF and GOS SST composite images

Daily MF/GOS composite

SST INTERPOLATION

The Global Ocean Data Assimilation Experiment (GODAE) high-resolution sea surface temperature pilot project aims to develop a new generation of global high-resolution (<10km) SST data products to the operational oceanographic, meteorological, climate and general scientific community, in real time and delayed mode SST

L2P data products provide satellite SST observations together with a measure of uncertainty for each observation in a common netCDF format. L4 gridded products are generated by combining complementary satellite and in situ observations within Optimal Interpolation systems.

MEDSPIRATION Project European RDAC for the GHRSST-PP Delivery of real-time high quality SST data, matching the GHRSST-PP specification [GDS v1.5] Operational and sustained production Generic and scalable system Products : Direct observations (L2P) Gap free high resolution maps (L4) Match-up database (MDB) High-resolution diagnostic datasets

Project organisation ESA/DUE O.Arino Medspiration consortium Project management, Ian S.Robinson, SOC core members system design, implementation, operations L2P product P.Le Borgne, Météo-France L4 product G.Larnicol, CLS support Project management/quality, J. Rickards, Vega Expertising (L4 product) L.Santoleri, B.Buongiorno Nardelli, CNR Test users Archive, dissemination, MDB and L4 processing J.F.Piollé, IFREMER HR-DDS D.J.Poulter L2P, L4 products L.Santoleri, B.Buongiorno Nardelli, CNR L2P products S.Eastwood, MetNo L2P processing software A.Coat, Avelmor

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 Specific CNR work: Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products Tuning of Medspiration L4_processor Improvement of MFSTEP analyses

Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products REMARKS: Medspiration L4 is at 2 km res., MFS is at 1/16° Different input -MFS: only AVHRR (CNR+CMS) -Medspiration: L2P (AATSR,AVHRR(CMS+Navoceano), SEVIRI…) Different data editing and selection -account for bias between sensors -selection of valid input (confidence values, temporal window…) Different OI algorithm configurations -spatial influential radius (‘bubble’) -strategy for the selection of influential observations within the bubble

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 Medspiration1/16 L4)  L2P different configurations starting from parameters similar to MFS ones MFS XBT data

Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products MEDSPIRATION L4 subsampled at 1/16° MFS (only AVHRR)

Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products MFS and Medspiration L4 original configuration MFS (only AVHRR) MEDSPIRATION L4 subsampled at 1/16° MBE=-0.11 °C Rms=0.52 °C MBE=-0.16 °C Rms=0.55 °C

MERSEA L4_processors configuration MFS processor modified to include all infrared sensors (L2P) Medspiration scheme tuned on the base of MFS processor : - 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

The Mediterranean GOS L4 SST products f l o w c h a r t Data mergingISAC Optimal Interpolation Data delivery on the GOS-ISAC web-site Data quality controll Night-time SST using MF algorithm Cloud detection SST daily composite binning on model grid (1/16x1/16) MF AVHRR acquisition Atlantic buffer zone + west Med Night-time SST using Pathfinder algorithm Cloud detection SST daily composite binning on model grid (1/16x1/16) ISAC AVHRR acquisition Entire Mediterranean L2P GHRSST Products

MFS L4 (Single-sensor vs multi-sensors) Single sensor Multi sensors

Medspiration L4 (old vs new configuration) OLD Config. New config.

MERSEA L4_processors configuration (GOS) Medspiration (new configuration) No Bias correction MFS (L2P in input) MBE=-0.11 °C Rms=0.46 °C MBE=-0.26 °C Rms=0.52 °C

Medspiration: 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

Example of evaluation of SENSOR BIASES (for existing L2P) SEVIRI MBE=-0.07 °C Rms=0.51 °C NAR17 MBE= °C Rms=0.49 °C NAR16 MBE=-0.18 °C Rms=0.64 °C AVHRR16 MBE=-0.56 °C Rms=0.76 °C

MERSEA L4_processors configuration (GOS) Main results:MFS (L2P in input) Medspiration Bias correction – Original signal variance  rms similar to MFS, higher bias vs in situ Medspiration 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

Last steps, future work : Inter-comparison and validation of MEDSPIRATION L4 and MFSTEP SST products.  concluded Tuning of Medspiration L4_processor -identified possible evolutions/problems in the L4_processor code -started tests on 2-step interpolation  stopped due to software licence issues Improvement of MFSTEP analyses -Run MFS L4_processor only with L2P -Update MFS format to standard GHRSST conventions -install and configure THREDDS, create catalog -include MODIS data in the analyses -Implement new MFS-L4 production in the operational chain -test on 2-step interpolation (at 2 km resolution)

Evaluation of SENSOR BIASES (MODIS data) MODIS Terra (11micron) MBE=-0.21 °C Rms=0.38 °C MODIS Terra (4 micron) MBE=-0.04 °C Rms=0.33 °C MODIS Aqua (4 micron) MBE=-0.16 °C Rms=0.54 °C MODIS Aqua (11 micron) MBE=-0.34 °C Rms=0.55 °C

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 climatology Time series of SST mean in the West Med

CNR THREDDS CATALOG STRUCTURE

According to Thomas.Loubrieu (ifremer) specification, we set up our Thredds Data Server MERSEA organization SEA SURFACE TEMPERATURE organization Near Real Time (NRT) - SST INTERPOLATED MAPSorganization Delayed Time (DT) - SST INTERPOLATED MAPSorganization OCEAN COLORorganization work in progress…..

CNR THREDDS CATALOG STRUCTURE MERSEA organization SEA SURFACE TEMPERATURE organization Near Real Time (NRT) - SST INTERPOLATED MAPSorganization Delayed Time (DT) - SST INTERPOLATED MAPSorganization OCEAN COLORorganization work in progress…..

For what concerns the SST product you can find 4 different SST products: NRT-v0: Optimal Interpolation SST dataset from AVHRR data only processed in near-real-time mode ( SST-NRTv0-OBS.html) NRT-v1: Optimal Interpolation SST dataset from all infrared L2P processed in near-real-time mode. ( SST-NRTv1-OBS.html) DT-v0: Optimal Interpolation SST dataset from AVHRR data only processed in delayed-time mode. ( SST-DTv0-OBS.html) DT-v1: Optimal Interpolation SST dataset from all infrared L2P processed in delayed-time mode. ( SST-DTv1-OBS.html) SST DATA PRODUCT

Two different types of time organization: NOT AGGREGATED AGGREGATED

SST DATA PRODUCT Two different types of time organization: NOT AGGREGATED AGGREGATED

XML THREDDS CATALOG FILES This structure is done with 13 configuration xml catalog files mersea.xml sst_init.xml, sst_NRT.xml nrt_v0.xml, nrt_v0_aggr.xml, nrt_v1.xml, nrt_v1_aggr.xml, sst_DT.xml dt_v0.xml, dt_v0_aggr.xml dt_v1.xml, dt_v1_aggr.xml ocean_init.xml Updated daily for V1 (NRT, DT) data, and weekly for V0 (NRT, DT) data

Delayed Time (DT) - SST INTERPOLATED MAPSorganization Single Sensor AVHRR(v0)product From to (10 days before last Monday) Not Aggregated: 476 single downloadable nc filesview Aggregated: 476 single nc files collated in a single nc fileview weekly updated: every Tuesday Multi Sensor (v1)product From to (10 days before this Monday)view Not Aggregated: 287 single downloadable nc filesview Aggregated: 287 single nc files collated in a single nc fileview daily updated CNR THREDDS CATALOG STRUCTURE MERSEA organization SEA SURFACE TEMPERATURE organization Near Real Time (NRT) - SST INTERPOLATED MAPS organization Single Sensor AVHRR(v0)product From to (last Monday) Not Aggregated:1062 single downloadable nc filesview Aggregated: 1062 single nc files collated in a single nc fileview weekly updated: every tuesday Multi Sensor (v1)product From to current data Not Aggregated:294 single downloadable nc filesview Aggregated:294 single nc files collated in a single nc fileview daily updated

1) Directly from our Thredds catalog page: Free access and free download You can download data clicking directly on the files link, for example for NRT-V0 aggregated data, you have to click on Access: OPENDAP: SST-NRTv0_aggr SST-NRTv0_aggr as ASCII DATA or Binary Data, choosing the variables to download and for each variable the time period you are interested in. SST DATA ACCESS AND DOWNLOAD You can access these data in two different ways: 2) From Ifremer link: Free access and free download You can search and download data use the Mersea THREDDS browser