Project RECOLOUR (REconstruction of COLOUR scenes) - SR/00/111 RESEARCH PROGRAMME FOR EARTH OBSERVATION STEREO II - BELGIAN SCIENCE POLICY

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MUMM - RBINS Cloud filling of TSM, CHL and SST remote sensing products by the Data Interpolation with Empirical Orthogonal Functions methodology (DINEOF), application to the

Damien Sirjacobs 1, Aïda Azcarate 1, Alexander Barth 1, YoungJe Park 2, Bouchra Nechad 2, Kevin Ruddick 2 and Jean-Marie Beckers 1 (1) GeoHydrodynamic and Environmental Research,

1 MUMM - RBINS Cloud filling of TSM, CHL and SST remote sensing products by the Data Interpolation with Empirical Orthogonal Functions methodology (DINEOF), application to the BELCOLOUR-1 database. Damien Sirjacobs 1, Aïda Azcarate 1, Alexander Barth 1, YoungJe Park 2, Bouchra Nechad 2, Kevin Ruddick 2 and Jean-Marie Beckers 1 (1) GeoHydrodynamic and Environmental Research, University of Liège, Belgium (2) Management Unit of the Mathematical Model of the North Sea, Royal Belgian Institute of Natural Sciences, Belgium Project RECOLOUR (REconstruction of COLOUR scenes) - SR/00/111 RESEARCH PROGRAMME FOR EARTH OBSERVATION STEREO II - BELGIAN SCIENCE POLICY ESA 2nd MERIS/(A)ATSR User Workshop, 22-26/09/2008 ESA-ESRIN Frascati

2 OUTLINE DINEOF tool: motivation and principle Data from Belcolour-1 database Illustration of DINEOF products from MERIS TSM and MERIS CHL Some DINEOF products from MODIS TSM and MODIS SST Less biaised TSM, CHL and SST seasonal climatologies CONCLUSIONS and PERSPECTIVES

3 DINEOF: motivation We study: a highly dynamic environment (! shelf and coastal zones) We need: complete time series of full and regular gridded data to force and validate ecosystem models... to calculate unbiaised mean values and trends We have: gappy satellite ocean data (in space and time) Objectives EOF-based analysis of historical satellite data in order to : 1) identify dominant spatio-temporal dynamics and correlations 2) fill missing data and produce full and regular gridded data 3) identify suspect/extreme data

4 DINEOF: principle EOF decomposition DINEOF field reconstruction Spatial Modes Temporal Modes (illustrated for MERIS TSM 2003 data on the North Sea)

5 1 col. = 1 image DINEOF X = U S V 1- EOF decomposition (iterations) 2- General Cross Validation Optimal nb. of EOFs N 3- Final SVD decomposition m pixels/image (sp. dim.) n images ( temp.dim ) = k singular values k sp. EOFfs n (temporal ( dimension k tp. EOFs

The BELCOLOUR-1 database The North Sea Belcolour-1 database: http://www.mumm.ac.be/belcolour Daily frequency 1km resolution Parameters : Chl.

6 The BELCOLOUR-1 database The North Sea Belcolour-1 database: Daily frequency 1km resolution Parameters : Chl.a, TSM, Flag for MERIS, MODIS, SeaWifs + SST for MODIS Some statistics on the channel area : 48.5 N-52.5 N 4 W-5 E; Sensor MERIS MODIS SeaWifs Period 01/03-12/06 07/02-12/06 09/97-12/04 Images Mean interval (days) Data points 34 * 10E6 185 * 10E6 57 * 10E6 Missing data %

8 MERIS TSM

14 Outliers and Error fields

15 Outliers: plane track spotted?

16 Outliers: probable clouds, haze area?

17 MERIS CHL

18 CEFAS buoy West G Scheelde turbidity maximum

21 Clear outliers observed at cloud edges

22 Clear outliers observed at cloud edges

23 Outliers: filtering well, a complex target

24 Outliers: filtering well, a complex target

25 MODIS TSM

27 MODIS SST

29 Monthly climatologies : MERIS CHL MODIS TSM MODIS SST

30 CONCLUSIONS Promising DINEOF applications to satellite optical data! (4,5 years of MODIS SST data 13 EOFs; varex = 98,0 %) 4 years of MERIS TSM data 18 EOFs; varex = 97,2 % 4 years of MERIS CHL data 8 EOFs; varex = 93,5 % 4,5 years of MODIS TSM data 14 EOFs; varex = 97,5 % DINEOF Advantages : -> parameter free -> synthetized description of the dominant dynamic -> pointing out «unusal data» as outliers (pixel/images) improve data quality provides efficient insigth into large databases - > estimation of correlation length of the observational error

31 PROBLEMS and PERSPECTIVES Spikes in reconstruction and unconsistent reconstructions: Eliminate outlying data and images before second DINEOF cycle Raise the minimal required presence of data per image Excessive filtering? To which extent DINEOF distinguishes noise from small scale natural processes? Combine DINEOF and DIVA (Data Interpolating Variational Analysis) to reduce filtering Comparison of DINEOF products with DIVA and various O.I. methods Multivariate analysis of TSM data with hydrodynamic model data Forecast from present data by using combined time lagged data

32 Thanks for attention! Acknowlegments: To the GHER and MUMM teams for their teaching and support RECOLOUR project was funded by the Belgian Science Policy (BELSPO) MERIS DATA was provided by ESA Links : GHER-ULG : MUMM - RBINS :

37 Summary of DINEOF related Products: Filled database Spatio-temporal EOF patterns Belcolour - 1 Database DINEOF Gridded fields at regular time-steps Extraction of multitemporal averages => data time series at specific locations Error maps of each analysis Maps of suspicious pixels

38 DINEOF 1- EOF decomposition A SVD decomposition with k eofs data matrix: ( X=X-mean(X missing data set to 0 (first guess), flagged X = U S V replace missing data until convergence B Repeat previous step for k=1:kmax kmax estimates of missing data 2- General Cross Validation Optimal Numbers of EOFs N 3- Final SVD decomposition of all data into N modes Reference DINEOF publication: Alvera-Azcárate et. al, Ocean Modelling, 9, , Beckers et al, Ocean Sciences, 2: , 2006

40 MODIS TSM

41 Outliers: probable cloud shadows or undetected cloud edges

42 Outliers: probable cloud shadows or undetected cloud edges

43 Outliers: probable cloud shadows or undetected cloud edges

44 Outliers and filtering.. : western channel bloom as consistent feature, but coastal spots of extreme low values partly classified as outliers

45 PROBLEMS: avoiding pollution of regular time step reconstructions and multitemporal averages by senseless reconstructions due to bad data

Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses.

Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses. Aida Alvera-Azcárate a,b,, Alexander Barth a,b, Damien Sirjacobs a, Fabian Lenartz a, Jean-Marie Beckers