OCEAN COLOUR PRODUCTION CENTRE Ocean Colour Mediterranean and Black Sea Observation Product

Transcription

1 OCEAN COLOUR PRODUCTION CENTRE Black Sea Observation Product OCEANCOLOUR_MED_OPTICS_L3_NRT_OBSERVATIONS_009_038 OCEANCOLOUR_MED_OPTICS_L4_NRT_OBSERVATIONS_009_039 OCEANCOLOUR_MED_OPTICS_L3_REP_OBSERVATIONS_009_095 OCEANCOLOUR_MED_CHL_L3_NRT_OBSERVATIONS_009_040 OCEANCOLOUR_MED_CHL_L4_NRT_OBSERVATIONS_009_041 OCEANCOLOUR_MED_CHL_L3_REP_OBSERVATIONS_009_073 OCEANCOLOUR_MED_CHL_L4_REP_OBSERVATIONS_009_078 OCEANCOLOUR_BS_OPTICS_L3_NRT_OBSERVATIONS_009_042 OCEANCOLOUR_BS_OPTICS_L4_NRT_OBSERVATIONS_009_043 OCEANCOLOUR_BS_OPTICS_L3_REP_OBSERVATIONS_009_096 OCEANCOLOUR_BS_CHL_L3_NRT_OBSERVATIONS_009_044 OCEANCOLOUR_BS_CHL_L4_NRT_OBSERVATIONS_009_045 OCEANCOLOUR_BS_CHL_L3_REP_OBSERVATIONS_009_071 OCEANCOLOUR_BS_CHL_L4_REP_OBSERVATIONS_009_079 Contributors: G. Volpe, J. Pitarch, S. Colella, V. E. Brando, V. Forneris, M Bracaglia, M. Benincasa Approval date by the CMEMS product quality coordination team: N/A

2 CHANGE RECORD Issue Date Description of Change Author Validated By /05/2015 all First version of document G. Volpe, S. Colella L. Crosnier /01/2016 ARV2 version G. Volpe, J. Pitarch, S. Colella, V.E.Brando /04/2016 all Revision for V2 ARR J. Pitarch /01/2017 all Version 3 G. Volpe, S. Colella, J. Pitarch, V. Forneris, V. E. Brando /03/2017 all Answer to AR V3 G. Volpe, S. Colella, J. Pitarch, V. Forneris, V. E. Brando /09/2017 all Version V3.2 Introduction of OLCI V. E. Brando, M. Bracaglia, M. Benincasa, G. Volpe, S. Colella, V. Forneris, Page 2/ 37

3 TABLE OF CONTENTS I Executive summary... 4 I.1 Products covered by this document... 4 I.2 Summary of the results... 4 II Production system description... 6 II.1 Product chain... 6 II.1.1 Level-2 data download from space agencies... 7 II.1.2 Striping effect removal from VIIRS and MODIS-AQUA data... 7 II.1.3 Bowtie effect in VIIRS data... 8 II.1.4 Application of Level-2 flags and projection over regular grid... 8 Band-shifting for MODIS-AQUA and VIIRS II.1.5 NRT Multi-sensor Rrs for MODIS-AQUA and VIIRS II.1.6 Level-3 geophysical products II.1.7 Level-4 interpolation for merged products II.1.8 MED and BS Climatology II.1.9 REP Level 3 and Level 4 CHL product over MED and BS III Validation framework III.1 Offline Validation III.2 OLCI datasets qualification III.3 Online Validation IV Validation results IV.1 Estimated Accuracy Numbers IV.2 Offline Validation IV.2.1 CHL IV.2.2 RRS IV.2.3 Kd IV.2.4 IOPs IV.3 OLCI datasets qualification IV.4 Online Validation V System s Noticeable events, outages or changes VI Quality changes since previous version VII References Page 3/ 37

4 I EXECUTIVE SUMMARY I.1 Products covered by this document Two products are covered within the Ocean Colour Thematic Assembly Centre (OCTAC): CHL and OPTICS. The primary OCTAC product from which it is virtually possible to derive all the geophysical products is the spectral Remote Sensing Reflectance (Rrs). This together with the diffuse attenuation coefficient, (Kd490), and the Inherent Optical Properties (IOP) constitute the macro-category of the OPTICS products. CHL is the phytoplankton chlorophyll concentration retrieved via regional algorithm applied to multi-sensor Rrs spectra. This product is generated using separate case I and case II algorithms and a posterior merging procedure. Level-3 (L3) products refer to the single snapshot or daily combined product both mapped onto a regular grid, whereas Level-4 (L4) products refer to both time-averaged and interpolated fields, and generally mapped over the same regular grid. These products are delivered to users in near-real time (NRT), delayed time (DT), and as consistent reprocessed time series (REP). The NRT products are generally available to users within one day from the satellite overpass. The space agencies commitment in delivering data in DT (e.g., using updated ancillary data) is thirty days, although DT data are generally available within one week. Similarly, OCTAC provides DT data within thirty-one days from the satellite overpass. This document synthesises the main processing steps and describes the validation framework and results performed over the products pertaining to the Mediterranean Sea (MED) and to the Black Sea (BS) areas. Two types of validation are here considered: the one performed daily over both NRT and DT products (online validation) and the one performed anytime a new or updated product is released (offline validation). The offline validation refers to the assessment of basic statistical quantities from the comparison of satellite-derived products and their in situ counter parts, when available. The online validation refers to the satellite data temporal consistency with respect to climatological values; this analysis is operationally performed and results are updated quarterly over the CMEMS web portal ( Details about both validation analysis tasks are shown in section III. I.2 Summary of the results Estimated Accuracy Numbers (EANs) provide a quick overview of the product performances with respect to in situ observations. The EAN retrieval is sometimes hampered by the scant availability of in situ observations, such as the absorption due to phytoplankton and detritus material or the light attenuation coefficient. For the L3 product, the online validation is a means for assessing its temporal consistency by comparison with relevant climatology. Since one of the inputs to the procedure to build the L4 is the climatology field (used as first guess in the DINEOF procedure, see section II.1.7), the online validation approach (see section III.3) is not applicable because of the lack of complete independency requirement between the input and the reference datasets used to assess the time consistency. The products presented in this document present an acceptable quality in terms of both their temporal consistency and with respect to their in situ counterparts. Product Mode Area Level r 2 N Page 4/ 37

5 Chl NRT/DT MED L Chl REP MED L Chl NRT/DT MED L Rrs412 NRT/DT MED L Rrs443 NRT/DT MED L Rrs490 NRT/DT MED L Rrs510 NRT/DT MED L Rrs555 NRT/DT MED L Rrs670 NRT/DT MED L Kd490 NRT/DT MED L Bbp443 NRT/DT MED L Chl NRT/DT BS L Chl REP BS L Rrs412 NRT/DT BS L Rrs443 NRT/DT BS L Rrs490 NRT/DT BS L Rrs510 NRT/DT BS L Rrs555 NRT/DT BS L Rrs670 NRT/DT BS L Table 1 Summary of Estimated Accuracy Numbers as defined in section III.1. Full metrics provided in Table 6 Page 5/ 37

6 II PRODUCTION SYSTEM DESCRIPTION The Ocean Colour Thematic Assembly Centre (OCTAC) builds and operates the European Ocean Colour Service for the Copernicus Marine Environment Monitoring Service (CMEMS) providing global, pan- European and regional (Atlantic, Arctic, Baltic, Mediterranean, and Black Seas) ocean colour products. This document describes the main achievements of the validation activity performed over the ocean colour operational products. Details on the validation context are provided in the next section. The OCTAC is a distributed centre made of three Production Units (PU), each of which in charge of the full production of single product. All products are then made available through the single Dissemination Unit (DU). Although managed centrally, each PU is responsible of the quality of its products. Figure 1 shows the area covered by the OCTAC. Figure 1 graphical view of the CMEMS areas. This document covers the quality of the products relative to the MED and BS areas. Production subsystem: OC-CNR-ROME-IT In the OCTAC operations, CNR provides near real-time, delayed time and reprocessed mode products. The production includes mulitsensor L3 and L4 from MODIS-AQUA and NPP-VIIRS and single sensor L3 and L4 from Sentinel3A-OLCI for both the Mediterranean and the Black seas. A brief scientific and technical description of the products and of the procedures used to retrieve them is provided below. II.1 Product chain This section describes all the steps needed to provide CMEMS users with state-of-the-art Level-3 and Level-4 OC products over the Mediterranean and the Black seas. Level-2 data are downloaded from space agencies (section II.1.1). After removal of stripes (both MODIS and VIIRS, section II.1.2) and the bowtie effects (VIIRS only, section II.1.3), single L2 files are remapped on the Equirectangular grid covering the regions of interest, after removal of all the flagged pixels. All regridded granules from the same sensor and from the same day are merged together into a single file containing the Remote Sensing Reflectance at nominal sensors wavelengths (section II.1.4). Section 0 is the description of the applied method to band-shifting the original spectra towards homogeneous spectra. At this stage all pixels from all satellite sensors are homogeneous in terms of Rrs wavebands, and as such can be Page 6/ 37

7 merged together to enhance the satellite coverage (section II.1.5). The retrieval of the geophysical products for both L3 and L4 is described in sections II.1.6 and II.1.7. Sections II.1.8 and II.1.9 provide an overview on the climatology fields and on the REP data. II.1.1 Level-2 data download from space agencies In order to optimize the bandwidth, the time and the disk space, as well as to adapt to Space Agencies download policies (which generally do not foresee too many simultaneous connections), CNR updated its operational processing chain for identifying granules covering the areas of interest. This approach becomes more crucial in case of limited areas such as the Mediterranean and the Black seas. CNR developed different algorithms for identifying the granules of interest; the most useful in operations are: NASA online query builder ( a web/perl system which permits to query NASA OC database for identifying and retrieving sensor s granules covering a particular area (identified by its label or its bounding box). The system allows using http _POST requests for querying the site via command line. This system works only for data already archived (no prediction) custom algorithms, based on satellite s cycles: thanks to satellite orbital cycles combined with NASA 5 or 6 minutes-granules (depending on the satellite/sensor), it is possible to identify a set of granules covering a given region in a specific date. This approach works well for identifying (past) or predicting (future) passages/swaths/granules, although sometimes it may need to be updated due to changes in satellite s orbits. EUMETSAT dedicated subscription on CODA for OLCI data for both basins II.1.2 Striping effect removal from VIIRS and MODIS-AQUA data An important task, operationally performed over both MODIS-AQUA and NPP-VIIRS images is the application of a destriping procedure over intermediate Level-2 products to remove the instrumentinduced stripes. These two sensors scan the Earth surface via a rotating mirror system which reflect the surface radiance to band detectors. Stripes derive from two hardware problems: i) the two sides of the mirror are not exactly identical, and ii) the band detector degradation is not homogeneous. Destriping correction is performed by applying the method developed by Bouali et al. (2014) and adapted to ocean color products by Mikelsons et al. (2014). Figure 2 shows the same satellite image with and without the application of the destriping procedure. It is shown how stripes are well identified and levelled, so maps appear without this artefact after the destriping procedure. Page 7/ 37

8 Figure 2 Snapshot of the MODIS L3 CHL product without the application of the destriping procedure (A), and its current operational version (B). II.1.3 Bowtie effect in VIIRS data Figure 3 Remote Sensing Reflectance at 486 nm from NPP-VIIRS over central Mediterranean Sea on the 1st April 2012: a) as downloaded from NASA and b) after the removal of the bowtie effect. Detectors have constant angular resolution so that the sampled Earth area increases with the scan angle. This results in consecutive scans to overlap away from nadir, so that the entire scan has the shape of a bowtie. NOAA s processing chain removes this effect in each VIIRS granule through a combination of aggregation and deletion of overlapping pixels, resulting in a series of rows of missing values at the edge of each granule (Figure 3a). In this production chain, these missing values are filled in by linear interpolation (Figure 3b). II.1.4 Application of Level-2 flags and projection over regular grid Each L2 granule, before being merged with the others taken from the same sensor and from the same day, is quality checked via the application of the L2 flags provided by Space Agencies. The flags applied to both MODIS and VIIRS are described in Table 2. The flags for OLCI are reported in Table 3. Figure 4 shows the cumulative effect of the application of the Level-2 flags and of the projection over the regular (Equirectangular) grid. All available granules for each day are then mosaicked into one single wavelength-dependent data file for each sensor covering the whole region of interest. Page 8/ 37

9 Figure 4 Same VIIRS granule (Rrs486) as Figure 3: a) L2 after the application of the striping effect described in previous section, and b) after the application of the L2 flags and of the projection over the regular grid. L2-Flags Meaning VIIRS MODIS ATMFAIL Atmospheric correction failure OFF ON LAND Pixel is over land ON ON HIGLINT Sun glint: reflectance exceeds threshold ON ON HILT Observed radiance very high or saturated ON ON HISATZEN Sensor view zenith angle exceeds threshold ON ON STRAYLIGHT Probable stray light contamination ON ON CLDICE Probable cloud or ice contamination ON ON HISOLZEN Solar zenith exceeds threshold ON ON LOWLW Very low water-leaving radiance ON ON CHLFAIL Chlorophyll algorithm failure ON ON NAVWARN Navigation quality is suspect ON ON ABSAER Absorbing Aerosols determined ON ON MAXAERITER Maximum iterations reached for NIR iteration ON ON CHLWARN Chlorophyll out-of-bounds ON ON ATMWARN Atmospheric correction is suspect ON ON NAVFAIL Navigation failure ON ON FILTER Insufficient data for smoothing filter ON ON Table 2 Names and meaning of the flags applied to MODIS and VIIRS Level-2, downloaded from NASA. OLCI Flags CLOUD CLOUD_AMBIGUOUS CLOUD_MARGIN INVALID COSMETIC SATURATED SUSPECT HISOLZEN Page 9/ 37

10 HIGHGLINT SNOW_ICE AC_FAIL WHITECAPS ANNOT_ABSO_D ANNOT_MIXR1 ANNOT_DROUT ANNOT_TAU06 RWNEG_O2 RWNEG_O3 RWNEG_O4 RWNEG_O5 RWNEG_O6 RWNEG_O7 RWNEG_O8 Table 3 Names of the flags applied to OLCI Level-2, downloaded from EUMETSAT Band-shifting for MODIS-AQUA and VIIRS One of the problems of the multi-sensor merging is the different set of bands present in the various operational ocean colour missions. Some bands are coincident but others can significantly differ: e.g., the green bands of MODIS-Aqua, SeaWiFS and OLCI are 547nm, 555nm and 560 nm respectively. A technique to collapse the various spectra on a pre-defined set of bands is thus essential for the multisensor merging; this technique is known as band-shifting. The band-shifting method here implemented is the one fully described in Melin and Sclep (2015). The first step allows the estimation of the Inherent Optical Properties (IOPs) from the apparent optical property (AOP), Rrs, at the sensor-native wavelengths, with the Quasi-Analytical Algorithm (QAA, first developed by Lee et al., 2002). The IOPs are estimated at the target wavelengths and, subsequently, the QAA is applied in forward mode to estimate the Rrs at the target bands. This approach produces a set of common bands (in this case the SeaWiFS wavelengths) for all sensors and allows the daily merging of the Rrs and the estimation of the daily geophysical fields (as described in the next sections). II.1.5 NRT Multi-sensor Rrs for MODIS-AQUA and VIIRS Once the single sensor spectra are homogeneous in terms of wavebands, that is after the single sensor wavebands have been band-shifted, it is possible for the Rrs from the available sensors to be merged together into single images. The output is a set of six Rrs images, each of which is treated as an individual image independently from the other Rrs bands of the spectrum. The aim is to assign a valid value to all pixels that were validly sampled independently from which sensor actually acquired the observation. Three possible conditions can happen after all the above steps: i) the pixel was observed from more than one sensor, ii) the pixel was observed from one sensor only, iii) the pixel was in no clear sky condition or masked out because of any of flags in Table 2, from all sensors. In the latter two conditions, the method is straightforward, the pixel is taken as is or is given the missing value, respectively. As for the former, single sensor statistics suggest that there is no significant difference between MODIS and VIIRS Rrs spectra when they are compared with in situ Rrs. This means that no specific weight is given to any sensor when they are averaged together. To avoid the horizontal discontinuity that might arise from pixels belonging to the first case next to pixels belonging to the Page 10/ 37

11 second, a smoothing procedure is applied. First, the field from each sensor (Figure 5a-b) is filled with relevant daily climatology (Figure 5e), as shown in Figure 5c-d. This enables the average of these two (or more) fields to be easily computed. All the not observed pixels are then set to the missing value (Figure 5f). After all bands are merged, single pixel Rrs spectra are available (Figure 6) for the geophysical products to be computed (section II.1.6). Within this step a mask is computed for keeping memory of the single sensor inputs to the multi-sensor product. This mask is added to the NetCDF file of all variables (Rrs, Kd490, Chl, IOPs), and is named sensormask (see inlet within Figure 6). Figure 5 Example of how the merging of MODIS and VIIRS works. Rrs 443 from MODIS AQUA (a), NPP-VIIRS (b) from April 1st Panels c and d are obtained by filling in panels a and b with climatology (e). The merged multi sensor product is obtained after removal of the unseen pixels (f). Page 11/ 37

12 Figure 6 Rrs spectra from the 1 st April, 2012, from MODIS-AQUA (blue), NPP-VIIRS (red), the merged multi-sensor product (green), and the in situ measurements (black), all in correspondence of the location shown over the map. The map is the sensormask of the day (see text for details) in which the pixels sampled by MODIS-AQUA only are shown in blue and those by NPP-VIIRS only in green; the pixels sampled by both sensors are shown in orange. II.1.6 Level-3 geophysical products II Chlorophyll a concentration Chl Chl data are the merged Case I Case II product. The generation of this product involves four main steps: 1. Application of the CHL algorithm suited for Case I waters to the entire L3 field 2. Application of the CHL algorithm suited for Case II waters to the entire L3 field 3. Determination of the pixels belonging to the two water types and identification of those which do not exactly belong to none of them 4. Merging of the two images Thus, any single image is processed twice, once for each water type. The exact identification of the two water types relies on in situ reference Rrs spectra as fully described in D'Alimonte et al. (2003). This approach takes account of the whole light spectrum from blue to NIR bands for both two water types. For the computation of these two average spectra two distinct datasets were used; for Case I and Case II waters the MedOC4 (Volpe et al., 2007) and CoASTS (Berthon et al., 2002, Zibordi et al., 2002) datasets are used, respectively. Similarly as the sensormask, the resulting water type mask is included in the NetCDF file. The Case I water CHL algorithms are described below. For this initial version of the OLCI Chlorophyll product, only the Case I algorithm were implemented in the processing chains in both basins. Mediterranean Sea MedOC4 Case I The algorithms used to produce these products have the same functional form as the traditional NASA algorithms (O Reilly et al., 2000) and use the same bands as SeaWiFS (or CCI) for MODIS-AQUA and VIIRS and the native bands for OLCI. They are however optimized for the Mediterranean open waters via the CNR in situ bio-optical dataset, as fully described in Volpe et al. (2007) and Santoleri et al. (2008). The current operational algorithms are an updated version of the one provided in Volpe et al. (2007), and Erreur! Source du renvoi introuvable. shows both the regional and the global algorithm functional forms superimposed to the in situ observations (Figure 7). Considering that the algorithms are the result of the in situ data they are derived from, this figure provides a means for understanding the need to regionalize the algorithms. It is particularly highlighted how the regionalized algorithm avoids the significant CHL overestimation that would be obtained with the global algorithm. Page 12/ 37

13 Figure 7 Algorithms for chlorophyll retrieval over the MED area for multi-sensors (A) and OLCI (B) products. The maximum band ratio (MBR) is shown on the X-axis; it is the ratio between the maximum value between the three bands in the blue (443, 490 and 510 nm) and the one in the green part of the light spectrum (555nm). Red dots are the in situ bio-optical data used to compute the operational algorithms (black lines). As a means of comparison the global algorithm (OC4v6) functional form is also superimposed (turquoise line) for multi-sensors. Black Sea BS CHL Case I The algorithm used to produce this product is the one developed by Kopelevich et al. (2013) (hereafter BSAlg), which has demonstrated to perform better than both the operational global algorithm and the Mediterranean algorithms. II Diffuse Attenuation Coefficient - Kd490 The algorithm used to compute the diffuse attenuation coefficient of light at 490 nm for multi-sensors product is a fourth power polynomial expression of the Rrs ratio (Rrs490 to Rrs555) (Table 4 and Figure 8). Given the lack of an appropriate in situ dataset able to describe the regional variability of the Black Sea, the global functional form is here alternatively implemented. Over the Mediterranean Sea, the same in situ bio-optical dataset as Chl is used instead (Volpe et al., 2007). Figure 8 shows both the regional and the global algorithm functional forms superimposed to the in situ observations collected in the Mediterranean Sea. For OLCI the standard algorithm is used ( 1_Waters.pdf) Area a 0 a 1 a 2 a 3 a 4 MED BS Table 4 Coefficients used to retrieve the Kd490 over the MED and BS areas, according to the equation shown in Figure 8. Page 13/ 37

14 Figure 8 Algorithm for the diffuse attenuation coefficient, Kd, at 490 nm over both MED (black line) and BS (global algorithm, turquoise line). Red dots are the in situ measurements over the MED area. The coefficients of the algorithms are shown in Table 4 for the two areas. II Inherent Optical Properties aph(443), adg(443), bbp(443) The algorithm used to produce the IOPs (only for multi-sensors products) is the QAA used in the context of the band-shifting (section 0). Figure 9 shows an example of the three parameters for the 1 st April Figure 9 Absorption due to phytoplankton (a ph (443), panel a), to gelbstoff and detritus (a dg (443), panel b), and the particulate backscattering coefficient (b bp (443), panel c), as computed with the application of the QAA algorithm to the multi-sensor Rrs spectra, for the 1 st April Page 14/ 37

15 II.1.7 Level-4 interpolation for merged products Input data The difference between the three processing modes (NRT, DT, and REP) resides in the way the input data matrix is built, starting from the daily L3 merged Chl. Figure 10 provides the schematics for each approach. Procedure This product relies on the technique first developed by Beckers and Rixen (2003). CNR implements three versions of this product for NRT, DT, and REP (using the ESA-CCI input data after regional adjustment). All versions are kept in the CNR storage system. In all configurations, the technique works as follows: starting from the day that has to be interpolated (grey square in Figure 10), the time series of the previous 10 day data (this is configurable) is used to build a data matrix as in Figure 10. All missing data are replaced with respective daily climatological sea pixels, and a mask matrix, namely holes, is built with zeros in correspondence of effective observations and ones where the climatology is used. Daily climatologies are built from SeaWiFS ( ) and described in section II.1.8. This data matrix constitutes the input to the iterative EOF procedure, that uses the Singular Value Decomposition approach (SVD, which is equivalent to the Empirical Orthogonal Function, EOF). After each iteration, the input data matrix to the next SVD iteration is built with original observations in correspondence of holes=0, and with the field reconstructed from the SVD output of the previous iteration in correspondence of holes=1. The reconstruction is carried out by using the number of modes corresponding to the iteration number: at the first iteration only the first mode is used, at the second the first two, and so on. Moreover, at each iteration, the procedure smoothens out differences between original observations and reconstructed field the same way as shown in Figure 5 for filling in observation and climatology. This scheme prevents artificial gradients to be created, resulting particularly effective during periods of scant data availability, e.g. winter, when the cloudy pixels are much more numerous than the clear sky pixels. The procedure stops when the variance explained by the current mode equals that of noise. Figure 10 Graphical representation of the input data matrix for a) NRT, b) DT, and c) REP processing modes. Single squares refer to daily sea pixel domain. The image that has to be interpolated is highlighted in grey for each configuration. Grey dashed lines are temporal isolines to illustrate that one single data file is used more than once with the purpose of accounting for the data temporal variability. Page 15/ 37

16 When seeking for data, the operational procedure first looks for the right L3 products. If, for any reason, this product is missing the climatology is taken. Figure 11 shows an example of the daily merged Chl product and of its interpolation, in DT mode. Figure 11 Upper panel shows the daily merged product from MODIS-AQUA and NPP-VIIRS on the 1st April Lower panel shows the Level-4, in DT mode. II.1.8 MED and BS Climatology Satellite climatology is extensively used in the production chain and is obtained from the thirteen years of SeaWiFS data at 1 km spatial resolution, which has been produced with SeaDAS 7.2, using the MedOC4 regional algorithm for CHL (Volpe et al., 2007), with a nominal spatial resolution of 1 km. These climatology maps have been created using the data falling into a moving temporal window of ± 5 days. One of the main purposes of a climatology field is to serve as reference, and as such it is expected to be as reliable as possible, thus avoiding biases caused by single incorrect pixel values. To overcome these possible biases, a filtering procedure has been applied to the entire SeaWiFS time series, by removing all isolated pixels and by filling in all isolated missing pixels using the nearneighbourhood approach. The resulting daily climatology time series includes the average, the median, the modal, the minimum, the maximum and the standard deviation (STD) on a pixel-by-pixel basis. II.1.9 REP Level 3 and Level 4 CHL product over MED and BS The two CHL REP products over MED and BS derive from the application of the MED and BS specific algorithms (as described in section II.1.6) over the ESA-CCI (Climate Change Initiative) input Rrs. ESA- CCIv3 Rrs were produced by Plymouth Marine Laboratory in the context of OCTAC at a nominal resolution of 1 km (instead of the 4 km of the CMEMS version 2). ESA-CCI processing chain merges SeaWiFS, MERIS, MODIS-Aqua and VIIRS observations, by applying a series of state-of-the-art algorithms, from the atmospheric correction to the band shift correction schemes (for a comprehensive overview of the ESA-CCI products see The net result is a higher spatial coverage of the fully consistent time series of the four sensor products. The application Page 16/ 37

17 of the same Chl algorithm ensures full compatibility between the NRT/DT and the REP products. The two Chl REP L4 products over MED and BS are the monthly and 8-day averages of the respective REP Level 3 products. Page 17/ 37

18 III VALIDATION FRAMEWORK CNR performs both on- and off-line validation of all core products. CHL is the only product for which regional and sensor specific algorithms are operationally implemented at CNR. III.1 Offline Validation Offline validation refers to the estimate of the statistical parameters listed in Table 5, using in situ observations, whose space-time distribution is shown in Figure 12. The insitu dataset includes Chl, apparent (Kd490 and Rrs) and Inherent Optical (absorption and backscattering coefficients) Properties. Mediterranean Sea in situ dataset derives from different cruises carried out by CNR from 1997 to Black Sea dataset consists of the reflectance data (Zibordi et al., 2009) acquired in Galata and Gloria AERONET-OC platforms during (Doctor Giuseppe Zibordi, from the Joint Research Center, is warmly acknowledged for the long-term effort to establish and maintain these two AERONET-OC sites). Here, chlorophyll is derived from the application of the ocean colour algorithm (Zibordi et al., 2015) developed using the BiOMaP dataset (Zibordi et al., 2011) to the reflectance acquired over the two AERONET-OC sites. Name Definition Estimated dataset mean (X E ) Reference dataset mean (X M ) X E = 1 N X M = 1 N N å i=1 N å i=1 X E i X M i Type-2 slope (S) Type-2 intercept (I) I = X E S X M Determination coefficient (r 2 ) Root Mean Square Error () Ψ = 1 N N i = 1 X i E X i M 2 1 / 2 Page 18/ 37

19 Centre-pattern Root Mean Square Error() Bias () æ = ç 1 è N d = 1 N N å i=1 N å i=1 é E Xi - X E ù ëê ûú - é X M i - X M ù { ëê ûú } 2 ö ø X E M ( i - X i ) 1/2 E M Table 5 Metrics used to compare the estimated (satellite-based) dataset X i,i=1..n to a reference (in-situ) dataset X i,i=1..n. For log-normally distributed variables (such as Chl) both datasets are log-transformed prior to computing the metrics. Notes: Type-2 regression (called also orthogonal regression) is used instead of minimising the vertical distance between independent data and linear fit (as in Type-1 regression), minimises the perpendicular distance between independent data and linear fit. Type-1 regression typically assumes the dependent variable (in situ data) is known infinitely well, when in reality, the in situ data are also affected by uncertainties (e.g. problems with in situ data sampling techniques) that are difficult to quantify. Therefore, A slope (S) close to one and an intercept (I) close to zero is an indication that the model compares well with the in situ data. The Centre-pattern (or unbiased) Root Mean Square Error describes the error of the estimated values with respect to the measured ones, regardless of the average bias between the two distributions. The RMSE is thus the distance, on average, of a data point from the fitted line, measured perpendicular to the regression line. The RMSE is directly interpretable in terms of measurement units, and so is a better measure of goodness of fit than a correlation coefficient. The bias indicator is also directly interpretable in terms of measurement units. The squaring is not done so negative values can cancel positive values. The bias is not a good indicator of average model performance and might be a misleading indicator of average error. It should be carefully considered taking into account the scatter plot. Figure 12 Location of the in situ dataset used for the offline validation. Red dots are the most recent observations ( ) used to compute the EAN for the NRT multi-sensor products (VIIRS was launched in late 2011), which together with the black dots form the entire dataset used to qualify the REP products. Page 19/ 37

20 III.2 OLCI datasets qualification This version of the document provides an initial qualification for the OLCI datasets introduced in October 2017 in the CMEMS catalogue, following the operational release of the L2 data stream by EUMETSAT on 5 July The Sentinel-3A Product Notice OLCI Level-2 Ocean Colour (add link in refs) describes the OLCI current Processing Baseline and Ocean Colour products, their quality, limitations, and product availability. The Notice also provides guidance on the use of the products, including the use of flags to mask cloudy or unreliable pixels. In this operational data stream, the System Vicarious Calibration gains has been implemented for OLCI Level-2 products to adjust to the Top-of-the- Atmosphere absolute radiance level for both NIR and VIS band ranges. The application of the SVC gains enabled to reduce OLCI absolute radiometric bias in Level 2 products when compared to inset measurements and other ocean colour missions. EUMETSat and ESA will adjust these SVC gains as more insitu data becomes available. As a matchup analysis with the L2 operational data form 5 July 2017 onwards would not yield meaningful EANs, the qualification of the introduced OLCI datasets will be based on the comparison with Multi products currently in the CMEMS catalogue. The following qualification analysis was thus performed only on data produced in the July-September 2017 (5/7/ /9/2017 time range) over each of the regional basins. III.3 Online Validation The aim of the online validation is to assess the temporal consistency of current day satellite observations through the use of current day climatological satellite data. The current day data temporal consistency is evaluated, for each product, as Quality Index, which is computed at the scale of the pixel as: QI= CurrentDataPixel - ClimatologyDataPixel STDDataPixel QI indicates the difference between current data and relevant climatological field in terms of the number of climatological standard deviations. This QI field is included in the relevant product NetCDF file. The QI is computed at daily resolution along with its distribution histogram that is stored and mapped as time series product, available on the CMEMS web portal ( Figure 13 shows the Quality Index of the multi-sensor daily L3 Chl image of the 1 st April Page 20/ 37

21 Figure 13 Quality Index of the multi-sensor daily L3 Chl image of the 1 st April Page 21/ 37

22 IV VALIDATION RESULTS IV.1 Estimated Accuracy Numbers Product Mode Area Level X M X E S I r 2 N Chl NRT/DT MED L Chl REP MED L Chl NRT/DT MED L Rrs412 NRT/DT MED L Rrs443 NRT/DT MED L Rrs490 NRT/DT MED L Rrs510 NRT/DT MED L Rrs555 NRT/DT MED L Rrs670 NRT/DT MED L Kd490 NRT/DT MED L Bbp443 NRT/DT MED L Chl NRT/DT BS L Chl REP BS L Rrs412 NRT/DT BS L Rrs443 NRT/DT BS L Rrs490 NRT/DT BS L Rrs510 NRT/DT BS L Rrs555 NRT/DT BS L Rrs670 NRT/DT BS L Table 6 Estimated Accuracy Numbers as defined in section III.1. IV.2 Offline Validation This section provides the offline validation analysis for products covering both MED and BS. The validation statistics associated with the analysis, the Estimated Accuracy Numbers, are shown in Table 6. Page 22/ 37

23 IV.2.1 CHL Figure 14 Satellite (y axis) versus in situ Chl concentration for MED. Satellite Chl is taken from NPP-VIIRS (panels a and b), from MODIS-AQUA (panels c and d). Panel e shows the L3 daily multi-sensor Chl product against the in situ observations. The spatial distribution is shown by the red dots in Figure 12. In panels b and d Chl is computed via the OC3 algorithm from NASA, while in panels a and c is computed through the MedOC3 algorithms (Santoleri et al., 2008), which was the algorithm formerly used before the development of the multi-sensor Chl product. Common points are marked in red and corresponding statistics are shown in Table 7 for comparison. Figure 14 shows the matchups of L3 daily multi-sensor Chl product over MED to be regularly distributed around the line of best agreement for the entire CHL dynamic range. As a matter of comparison the previous Chl products from MODIS-AQUA and NPP-VIIRS along with their standard counterparts from space agencies are also shown. The statistics in Table 7 refer to the common points (red dots in Figure 14), while those for the entire matchup are reported in Table 6, for the new product only. The regional algorithm retrieves chlorophyll more accurately than the standard counterpart from space agencies for both MODIS-AQUA and NPP-VIIRS. The multi-sensor Chl shown in Figure 14e supersedes those shown in Figure 14a and Figure 14c. Sensor Algorithm X M X E S I r 2 AQUA MedOC AQUA OC Page 23/ 37

24 VIIRS MedOC VIIRS OC Multi MedOC Table 7 EAN for the common 111 matchup points highlighted in red in Figure 14. Figure 15 NRT L4 daily interpolated multi-sensor Chl product against the in situ observations. The spatial distribution is shown in Figure 12. Figure 15 shows the matchups of L4 daily interpolated Chl product over MED to be regularly distributed around the line of best agreement for the entire CHL dynamic range. Statistics associated with this product are shown in Table 1. Figure 16 REP L3 daily interpolated multi-sensor Chl product against the in situ observations for MED. The spatial distribution is shown by the black dots in Figure 12. Figure 16 shows the matchups of the L3 daily REP Chl product over MED to be regularly distributed around the line of best agreement for the entire CHL dynamic range. Statistics associated with this product are shown in Table 1. As well as for the MED, Figure 17 shows the matchups of L3 daily multi-sensor Chl product over BS. In addition to the new Chl product, the figure shows the previous Chl products from MODIS-AQUA and Page 24/ 37

25 NPP-VIIRS along with their standard counterparts from space agencies. The statistics in Table 8 refer to the common points (red dots in Figure 17), while those for the entire matchup are reported in Table 6, for the new product only. The regional algorithms retrieve chlorophyll more accurately than the standard counterparts from space agencies for both MODIS-AQUA and NPP-VIIRS. The multisensor Chl shown in Figure 17e supersedes those shown in Figure 17a and Figure 17c. Figure 17e shows that there is a very good agreement between multi sensor product and the in situ measurements for value below 5 mg m -3 and that satellite observations above this value should be considered with care. Figure 17 Satellite (y axis) versus in situ Chl concentration for BS. Satellite Chl is taken from NPP-VIIRS (panels a and b), from MODIS-AQUA (panels c and d). Panel e shows the L3 daily multi-sensor Chl product against the in situ observations. The spatial distribution is shown by the red dots in Figure 12. In panels b and d Chl is computed via the OC3 algorithm from NASA, while in panels a, c and e is computed through the BSAlg algorithms (Kopelevich et al. (2013)). Common points are marked in red and corresponding statistics are shown in Table 8 for comparison. Sensor Algorithm X M X E S I r 2 AQUA BSAlg AQUA OC VIIRS BSAlg VIIRS OC Multi BSAlg Table 8 EAN for the common 245 matchup points highlighted in red in Figure 17. Page 25/ 37

26 Figure 18 shows the matchups of the L3 daily REP Chl product over BS. Statistics associated with this product are shown in Table 6 As well as for the NRT product, also the REP Chl product shows a very good agreement between satellite and the in situ measurements for values below 5 mg m -3 while for higher concentrations the satellite seems to have an important underestimation. Figure 18 REP L3 daily interpolated multi-sensor Chl product against the in situ observations for BS. The spatial distribution is shown by the black dots in Figure 12. IV.2.2 RRS Page 26/ 37

27 Figure 19 L3 daily multi-sensor spectral Rrs product against the in situ observations for MED. The spatial distribution is shown by the red dots in Figure 12. Figure 19 shows the multi-sensor Rrs matchups over the Mediterranean Sea. Mismatch is highest at the extreme bands, 412 nm and 670 nm. In the first case, this is due to increased uncertainty in the extrapolation of the aerosol optical thickness from the near infrared to this band. In the second case, the reason is given by the very low Rrs values at this band in the open waters of the Mediterranean Sea. This poses a challenge both in the in-situ and satellite determination of the Rrs at this band. Fortunately, Chl is derived using the central bands (443 nm to 555 nm), thus benefiting from reduced uncertainties here. At these central bands, Rrs suffers of a slight underestimation. Figure 20 Comparison over the same in situ dataset of the Rrs taken from MODIS-AQUA and NPP-VIIRS (the former two products) and the new Rrs multi-sensor product against in situ Rrs spectra, over MED. From Figure 20 it is clear that the new multi-sensor product does not add any significant source of uncertainty, but rather simplifies by reducing the number of spectra (in this case one multi-sensor versus two single sensor spectra) per pixel. Page 27/ 37

28 Figure 21 L3 daily multi-sensor spectral Rrs product against the in situ observations for BS. The spatial distribution is shown by the red dots in Figure 12. Figure 21 shows the multi-sensor Rrs matchups over the Black Sea. As well as for the MED, mismatch is highest at the extreme bands, 412 nm and 670 nm. Fortunately, for the bands used for the Chl product (510 and 55 nm), the agreement between satellite and in situ observations is very good (see also Table 6). Figure 22 Comparison over the same in situ dataset of the Rrs taken from MODIS-AQUA and NPP-VIIRS (the former two products) and the new Rrs multi-sensor product against in situ Rrs spectra, over BS. Page 28/ 37

29 Figure 22 remarks the good results of the new multi-sensor product over the BS which closely mirrors those obtained in the MED region. IV.2.3 Kd490 Figure 23 L3 daily multi-sensor light attenuation coefficient at 490 nm product against the in situ observations. The spatial distribution is shown by the red dots in Figure 12. Figure 23 provides matchup assessment of the KD490 product for NRT/DT data from multi sensor. It is shown how agreement is very good for high values. However, for low values, a slight overestimation appears. IV.2.4 IOPs Figure 24 L3 daily multi-sensor particulate backscattering coefficient at 443 nm against the in situ observations. The spatial distribution is shown by the red dots in Figure 12. Page 29/ 37

30 Figure 24 provides the matchup assessment of the particulate backscattering coefficient at 443 nm, showing a general good agreement for low values and a slight underestimation towards the higher end of the range of variability. IV.3 OLCI datasets qualification This section provides an initial qualification for the OLCI datasets introduced in October 2017 in the CMEMS catalogue, following the operational release of the L2 data stream by EUMETSAT on 5 July The following qualification analysis was thus performed only on data produced in July- September 2017 (5/7/ /9/2017 time range) over each of the regional basins. The three figures (Figure 25, Figure 26, Figure 27) show the time-series of the histogram of the ratio between the OLCI datasets and the corresponding datasets from the currently operational multi-sensor products. For the Mediterranean Sea, the OLCI L3 daily Rrs datasets over estimate of the corresponding operational multi-sensor datasets in the spectral range as the median value of the ratio ranges , with most values of the ratio are within 20-30% of unity (Figure 25). For the 555nm spectral band the median value of the ratio is very close to 1, but the histogram shows a wider range of variability (+- 50%). For the 670 spectral band the ratio is highly variable, mostly due to the low range of Rrs in the Mediterranean Sea. For the Black Sea, the median value of the ratio is very close to 1 for the spectral range with most values of the ratio are within 20-30% of unity (Figure 26) These results are consistent with the qualifications provided by EUMETSAT in July for oligotrophic and mesotrophic waters. For the Mediterranean Sea, the Chlorophyll product, retrieved with the regional algorithm, underestimates the operational multi-sensor product, ( median value of 0.8 for the ratio and a large variability (Figure 27). This is due both to the monotonic decreasing functional form of the MedOC4 algorithm (Figure 7) and the wider range of the ratio between OLCI and the multi-sensor product for the Rrs 555nm spectral band. For the Black Sea, the median value of the ratio ranged , with most values of the ratio within 20-30% of unity and the Chlorophyll product, retrieved with the regional algorithm, underestimates the operational multi-sensor product. Please note that the results presented in this section are likely to change when EUMETSAT will apply new SVC gains on the L2 operational data streams. Page 30/ 37

31 Figure 25 The time-series of the histogram of the ratio between the L3 daily Rrs datasets of the OLCI product (O) and the corresponding L3 daily Rrs datasets from the currently operational multi-sensor products (X) for the Mediterranean Sea. The median of the histogram density is represented by the back line. Page 31/ 37

32 Figure 26 The time-series of the histogram of the ratio between the L3 daily Rrs datasets of the OLCI product (O) and the corresponding L3 daily Rrs datasets from the currently operational multi-sensor products (X) for the Black Sea. Page 32/ 37

33 Figure 27 The time-series of the histogram of the ratio between the L3 daily Chlorophyll datasets of the OLCI product (O) and the corresponding L3 daily Chlorophyll datasets from the currently operational multi-sensor products (X) for the Mediterranean and Black Sea. IV.4 Online Validation Quality Index time series (as described in section III.3) is produced for all products and updated quarterly on the CMEMS web portal: oceancolour. Since the frequency of update on the web portal is higher than the one of this document, these statistics are not included into this document. As an example, the online validation statistics is here shown for the single-sensor daily MODIS-AQUA L3 Chl product over the Mediterranean Sea. The upper panel is a plot with the time on the X-axis and the bins of the histogram of the Quality Index on Y-axis; colour shows the frequency of occurrence with respect to the total number of valid pixels, whose time series is shown in the lower panel. Horizontal dotted lines mark each of the bin histogram. Figure 28 shows the time series of the Quality Index histogram for the period January 2013 to January This plot serves to control how much the produced data deviates from the (expected) climatological values. Possible sensor degradations are thus expected to reflect in this plot. Figure 28 Quality Index time series for the single-sensor daily MODIS-AQUA L3 Chl product over the Mediterranean Sea. Page 33/ 37

34 V SYSTEM S NOTICEABLE EVENTS, OUTAGES OR CHANGES In this version, the only change is the addition of OLCI processing and assessment to the document. No changes were carried out on the MODIS+VIIRS multi-sensor operational production. Page 34/ 37