SSALTO/DUACS User Handbook:

Transcription

1 Reference : CLS-DOS-NT Nomenclature : SALP-MU-P-EA CLS Issue : 4rev 4 Date : 2015/06/30

2 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS i.1 Chronology Issues : Issue : Date : Reason for change : /11/ /01/01 Modification of the commentary on the flags and of the part "Product generation" /03/29 Addition of Mozambique products and temporal update of DT products /04/16 Addition of daily DT products /09/01 New version of SLA NetCDF files Addition of RT products /01/10 NRT: Addition of Arctic and Europe products /01/21 Cnes MOE GDR-D orbit on Envisat /02/06 Addition of Cryosat-2 mission in NRT products /05/25 End of Envisat mission and addition of Jason-1 on its geodetic orbit /07/20 New version "D" of IGDR for Jason /10/20 Addition of Cryosat-2 mission in DT products /12/04 NRT: Improvement in the processing of Cryosat-2, DT:integration of Jason-1 geodetic mission and new version D of Jason /01/29 Global SLA NRT: Integration of "OGDR on the fly" data /07/01 NRT: Integration of Saral/AltiKa and OGDR Cryosat-2 data /09/04 NRT: Improvement of DAC correction /04/15 NRT and DT: DUACS 2014 version of products /05/20 NRT: Addition of HY-2A mission /11/15 DT: Addition of HY-2A mission /05/18 NRT and DT: New orbit standards GDR-E (Jason-2, Cryosat-2, AltiKa) /06/30 NRT and DT: Implementation of the Saral/AltiKa geodetic orbit

3 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS i.2 List of Tables 1 SL-TAC Near-Real Time Input data overview Corrections and models applied in SL-TAC NRT products produced from IGDRs Corrections and models applied in SL-TAC NRT products produced from OGDRs Near-Real Time Filtering and sub-sampling values Measurement noise error before/ after spatial filtering for Mediterranean and Black Sea products (cms rms) Corrections and models applied to SL-TAC DT products for TOPEX/Poseidon, Jason-1, Jason Corrections and models applied in SL-TAC DT products for ERS-1, ERS-2, Envisat Corrections and models applied in SL-TAC DT products for Cryosat-2, GFO, AltiKa and HY-2A Delayed Time Filtering and sub-sampling values Measurement noise error before/ after spatial filtering for Mediterranean and Black Sea products (cms rms) List of the Along-track products in NRT List of the Gridded products in NRT, 1/4 cartesian resolution for global and 1/8 cartesian resolution for regional List of the Along-track products in DT List of the Gridded products in DT, 1/4 cartesian resolution for global and 1/8 cartesian resolution for regional List of Figures 1 DUACS and AVISO, a user-driven altimetry service Example of a map of Sea Level Anomalies (MSLA) where tracks of HY-2A are superimposed 5 3 SL-TAC processing sequences Overview of the near real time system data flow management Merging pertinent information from IGDR and OGDR processing Impact in cm of the reference change from 7 years to 20 years ([ ] to [ ]) 14 7 Noise Level in 1hz Jason-2 SLA estimated from mean wavenumber spectra over 2011 in 10 x10 boxes (cm rms) before 65km filtering (left) and after (right) Example with the key performance indicator on 2009/06/ Impact in cm of the reference change from 7 years to 20 years ([ ] to [ ]) Noise Level in 1hz Jason-2 SLA estimated from mean wavenumber spectra over 2011 in 10 x10 boxes (cm rms) before 65km filtering (left) and after (right) Three merged maps are produced daily: final map (d-6), intermediate map (d-3) and prliminary map (d0) List of satellites in all-sat-merged products List of satellites in two-sat-merged products

4 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS i.3 Applicable documents / reference documents RD 1: AVISO User Handbook for Merged Topex/Poseidon products (M-GDR) Ref: AVI-NT CN RD 2: AVISO and PO.DAAC User Handbook - IGDR and GDR Jason-1 Products Ref: SMM-MU-M5-OP CN RD 3: CERSAT, 1996: Altimeter and Microwave Radiometer ERS Products User manual Ref: C2-MUT-A-01-IF RD 4: CLS, 1996: Validation of ERS-1/2 OPR cycles Ref: CLS.OC/NT/ (ERS-1) and CLS.OC/NT/ (ERS-2) (one report per cycle) RD 5: CLS, 2006: Envisat RA2/MWR ocean data validation and crosscalibration - Activities Yearly report - Contract N 03/CNES/1340/00/DSO310 Ref: SALP-RP-MA-EA CLS RD 6: CLS, 2006: Jason-1 validation and crosscalibration activities - Contract N 03/CNES/1340/00/DSO310 Ref: SALP-RP-MA-EA CLS RD 7: OSTM /Jason-2 Products Handbook Ref: SALP-MU-M-OP CN

5 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS i.4 List of acronyms AL AltiKa ATP Along-Track Product ADT Absolute Dynamic Topography AVISO Archiving, Validation and Interpretation of Satellite Oceanographic data BGLO Biais Grande Longueur d Onde Cal/Val Calibration - Validation CERSAT Centre ERS d Archivage et de Traitement CMA Centre Multimission Altimetry center CORSSH CORrected Sea Surface Height C2 Cryosat-2 DAC Dynamic Atmospheric Correction DT Delayed Time DTU Mean Sea Surface computed by Technical University of Danemark DUACS Data Unification and Altimeter Combination System E1 ERS-1 E2 ERS-2 EN Envisat ENN Envisat on its non repetitive orbit (since cycle 94) ECMWF European Centre for Medium-range Weather Forecasting ENACT ENhanced ocean data Assimilation and Climate prediction G2 Geosat Follow On GIM Global Ionospheric Maps GDR Geophysical Data Record(s) HY-2A Haiyang-2A IERS International Earth Rotation Service IGDR Interim Geophysical Data Record(s) J1 Jason-1 J1N Jason-1 on its new orbit (since cycle 262) J1G Jason-1 on its geodetic orbit (since May 2012) J2 Jason-2 JPL Jet Propulsion Laboratory LAS Live Access Server LWE Long Wavelength Errors MADT Map of Absolute Dynamic Topgraphy MDT Mean Dynamic Topography MOE Medium Orbit Ephemeris MP Mean Profile MSLA Map of Sea Level Anomaly MSS Mean Sea Surface NRT Near-Real Time OE Orbit Error OER Orbit Error Reduction Opendap Open-source Project for a Network Data Access Protocol PF Polynom Fit PO.DAAC Physical Oceanography Distributed Active Archive Centre

6 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS i.5 POE Precise Orbit Ephemeris RD Reference Document SAD Static Auxiliary Data SARAL Satellite with ARgos and ALtika SI Signed Integer SLA Sea Level Anomaly SSALTO Ssalto multimission ground segment SSH Sea Surface Height T/P Topex/Poseidon TPN Topex/Poseidon on its new orbit (since cycle 369)

7 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS i.6 Contents 1. Introduction Data policy and data access Geodetic orbit for AltiKa (June 2015 for NRT products) 4 3. Integration of the HaiYang-2A (HY-2A) Mission in NRT products (May 2014) and in DT products (November 2014) 5 4. SSALTO/Duacs system Introduction Near Real Time processing steps Input data, models and corrections applied Acquisition Homogenization Input data quality control Multi-mission cross-calibration Product generation Change of Reference Computation of raw SLA Cross validation Filtering and sub-sampling Computation of ADT Products Generation of gridded Sea Level Anomalies (MSLA) products Merging process Quality control Final quality Control Performance indicators Delayed Time processing steps Input data, models and corrections applied Acquisition Homogenization Input data quality control Multi-mission cross-calibration Product generation Change of Reference Computation of raw SLA Cross validation Filtering and sub-sampling Computation of ADT Products Generation of gridded Sea Level Anomalies (MSLA) products Merging process Quality control Final quality Control

8 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS i.7 5. SSALTO/DUACS Products Near Real Time Products Delay of the products Temporal availibility Delayed Time Products Delay of the products Temporal availibility Data format NetCdf Structure and semantic of NetCDF along-track (L3) files Structure and semantic of NetCDF maps (L4) files Structure and semantic of NetCDF Gridded Noise on Sea Level Anomaly files Structure and semantic of NetCDF Gridded Change reference files Accessibility of the products Folders on the ftp server Gridded Delayed-Time and Near-Real-Time products (SLA, ADT, Geostrophic currents) Along-track Delayed Time and Near Real Time SLA and ADT files Gridded Change of Reference folders Gridded Noise estimations folders Nomenclature of files Gridded Delayed-Time and Near-Real-Time products (SLA, ADT, Geostrophic currents) Along-track Delayed Time and Near Real Time SLA and ADT files News and Updates [Duacs] Operational news Updates

9 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 1 1. Introduction DUACS processes data from all altimeter missions: HY-2A, Saral/AltiKa, Cryosat-2, OSTM/Jason-2, Jason- 1, Topex/Poseidon, Envisat, GFO, ERS-1&2. Figure 1: DUACS and AVISO, a user-driven altimetry service DUACS provides a consistent and homogeneous catalogue of products for varied applications, both for near real time applications and offline studies. Some DUACS gridded products are available free of charge. Commercial use of some gridded products is subject to separate agreement and licence (Contact aviso@altimetry.fr).

10 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 2 The data provided to users have a global coverage and regional products are also computed over specific areas: Mediterranean Sea, Black Sea, Mozambique, Europe and Arctic. Since April 15 th 2014, a new version of products are now distributed: in the former version, the products were based on a 7-year reference period [1993, 1999]. As 20 years of altimeter measurements are now available it is of high interest to change the reference period for a longer period: in the 2014 version we thus change to a 20-year reference period [1993, 2012], as presented by Pujol and Faugere, 2013 [54] at the last Ocean Surface Topography Science Team meeting and in Aviso+ Newsletter9 by Maheu et al., 2013 [46]. Depending on your application, you may wish to use this improved 20-year reference or you may wish to keep the original 7-year reference period. This is the reason why, new "ref20yto7y" products are also distributed. They consist of corrective maps that allow the user to change the reference of the SLA and MDT products. Three corrective maps are available, for the Mediterranean Sea, Black Sea and Global Ocean. See sections and Moreover noise estimations for Near-real-Time and Delayed-Time are also distributed. These are gridded products containing the noise of the filtering of SLA Global Ocean products and are described in section for NRT and for DT. For Mediterranean and Black seas, only one value has been calculated and is given in the same sections. This document describes the along-track and gridded products generated by the SSALTO/DUACS near-real time and delayed time altimeter data processing software. The updates and reprocessing are presented in first part. The SSALTO/DUACS system is then introduced: after a description of the input data, an overview of the processing steps is given. Then complete information about the SSALTO/DUACS output data (i.e user products, in Near-Real Time and in Delayed Time) is provided, giving nomenclature, format description, and software routines. More information regarding the SSALTO/DUACS project and products may be found on the AVISO web site at the respective addresses:

11 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Data policy and data access All SSALTO/DUACS product users need an account on FTP, whether for Near-Real-Time or for Delayed- Time data, for along-track and gridded products. The SSALTO/DUACS along-track data (level 3) are now shared with the MyOcean catalogue. MyOcean is an European project dedicated to operational oceanography. MyOcean Service provides the best set of information available on the Ocean for the large and regional scales (European seas), based on the combination of space and in situ observations, and their use into models: temperature, salinity, currents, ice extent, sea level, primary ecosystems... (see When users choose these SSALTO/DUACS along-track data, their personal information can be transmitted by Aviso/CNES to the MyOcean European project for their user databases, with the user s agreement. Duacs gridded products (level 4) are available free of charge for scientific studies or non-profit projects only. Commercial use of gridded products or applications not in line with the standard license agreement is subject to separate agreement and licence (Contact aviso@altimetry.fr). Please, subscribe to get access to SSALTO/DUACS products by filling the registration form on:

12 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 4 2. Geodetic orbit for AltiKa (June 2015 for NRT products) Due to reaction wheel issues, SARAL station keeping maneuvers have not been performed nominally since March 31st, As a result, SARAL/AltiKa s ground track has been drifting from the nominal track with deviations overtaking 10km depending on the latitude, instead of +/-1km usually. This drift has an impact on the SSALTO/Duacs AL products since April 1st, Indeed, SARAL/AltiKa has been currently processed as a repetitive mission (like Jason-2). This processing includes the projection of the measurement into a theoretical track position in order to benefit from the precise Mean Profile estimated from past missions (ERS1/2, Envisat) (see ). However, as the distance between the theoretical track and the true track position is now large, this projection processing induces an additional error in the product. Since mid may 2015, we indeed observed an increase of the variability of the SARAL/AltiKa SLA at short wavelengths (< about 200km). In order to limit the degradation of the quality of the product, the SARAL/AltiKa mission is processed since June 30th, 2015 as a geodetic mission, i.e. avoiding projection on a theoretical track, as currently done for Cryosat-2 or HY-2A processing. This implies that the measurement positions change from one cycle to another one. Moreover, a short wavelength error is induced on the raw SARAL/AltiKa measurement, due to the use of a gridded Mean Sea Surface solution rather than a more precise Mean Profile for SLA computation. However, SSALTO/Duacs processing includes an along-track filtering that strongly reduces this error signature on filtered products (see and ).

13 SSALTO/DUACS User Handbook: CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 5 3. Integration of the HaiYang-2A (HY-2A) Mission in NRT products (May 2014) and in DT products (November 2014) HY-2A is the first altimetric and radiometric satellite launched by the China National Space Administration (CNSA) dedicated to oceanography. It has been developped with the support from the National Satellite Ocean Application Service (NSOAS). The mission HY-2A has been launched in August Thanks to CNES/NSOAS agreement on HY-2A, both agencies decided to collaborate and a processing prototype (HPP) with specific evolutions on the ground processing of the altimeter data (retracking of S-IGDRs) has been developed. The Jason-1/Jason-2 retracking (MLE4, Amarouche et al., 2004 [1]) is used to recompute the altimeter parameters. Complete information can be found in Picot et al., 2013 [51]. As the level of HY-2A HPP is high, it has been decided to implement it in the SSALTO/Duacs multimission system. It will thus complement the sampling and will provide valuable information on the ocean mesoscale variability. Figure 2: Example of a map of Sea Level Anomalies (MSLA) where tracks of HY-2A are superimposed

14 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 6 4. SSALTO/Duacs system 4.1. Introduction This chapter presents the input data used by the system and an overview of the different processing steps necessary to produce the output data. SSALTO/DUACS system is made of two components: a Near Real Time one (NRT) and a Delayed Time (DT) one. In NRT, the system s primary objective is to provide operational applications with directly usable high quality altimeter data from all missions available. In DT, it is to maintain a consistent and user-friendly altimeter database using the state-of-the-art recommendations from the altimetry community. Following figure gives an overview of the system, where processing sequences can be divided into 7 main steps: acquisition homogenization input data quality control multi-mission cross-calibration product generation merging final quality control.

15 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 7 Figure 3: SL-TAC processing sequences

16 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Near Real Time processing steps Input data, models and corrections applied To produce along-track (L3) and maps (L4) products in near-real time, the SL-TAC system uses two flows, based on the same instrumental measurements but with a different quality: The IGDRs that are the latest high-quality altimeter data produced in near-real-time. The OGDRs that include real time data (SARAL/AltiKa, OSTM/Jason-2 and Cryosat-2) to complete IGDRs. These fast delivery products do not always benefit from precise orbit determination, nor from some external model-based corrections (Dynamic Atmospheric Correction (DAC), Global Ionospheric Maps (GIM)). Integration of OGDR data increased the resilience and precision of the system. A better restitution of ocean variability is observed, especially in high energetic areas. Altimetric product Source Availability Type of orbit Jason-2 IGDR CNES ~24h CNES MOE GDR-E since May 25, 2015 OGDR NOAA/EUMETSAT ~3 to 5 h Cryosat-2 IGDR-like ESA/CNES ~48 h CNES MOE GDR-E OGDR-like ESA/CNES best effort Saral/AltiKa IGDR CNES ~48 h CNES MOE GDR-D until June 30, 2015 CNES MOE GDR-E afterwards OGDR ISRO/EUMETSAT ~3 to 5 h HY-2A IGDR CNES/NSOAS best effort (with a mean delay of 72 h) CNES MOE GDR-D Table 1: SL-TAC Near-Real Time Input data overview See Figure 4: Overview of the near real time system data flow management.

17 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 9 NRT product from IGDR 1 j2 c2 al h2 Product version D CPP [4] version T patch 2 HPP [51] Orbit Ionopheric Dry tropo Wet troposphere DAC Ocean tide CNES MOE GDR-E since May 25, 2015 bi-frequency altimeter range measurements CNES MOE GDR-E CNES MOE GDR-D until June , CNES MOE GDR-E afterwards GIM model (Iijima et al,1998[31]) Model computed from ECMWF Gaussian grids (new S1 and S2 atm tides applied) AMR radiometer (enhancement in coastal regions) Model computed from ECMWF Gaussian grids MOG2D High Resolution forced with ECMWF pressure and wind fields (S1 and S2 were excluded) (Carrere and Lyard, 2003[6])+ inverse barometer. Filtering temporal window is recentered using forecasts GOT4v8 (S1 and S2 are included) and TPX 07.2 [22] for Arctic products Pole tide [Wahr, 1985 [69]] Solid tide earth Loading tide Sea bias state Orbit error Elastic response to tidal potential [Cartwright and Tayler, 1971[8]], [Cartwright and Edden, 1973[9]] Non Parametric SSB [N. Tran et al., 2012[65]] (with cycles J using GDR-D GOT4v8 (S1 and S2 are included) Non parametric SSB from J1 with unbiased sigma0 Hybrid SSB (method from Scharroo et al, 2004 [63] applied to al) CNES MOE GDR-D Model computed from ECMWF Gaussian grids Calculated from HPP [51]: -3.45% of SWH Global multi-mission crossover minimization (Le Traon and Ogor, 1998[40]) LW errors Optimal Interpolation [Le Traon et al., 1998[39]] Intercalibration Reference from cycle 20 Mean profile (see and ) Computed with 20 years of TP/J1/J2 data; referenced [1993,2012] MSS_CNES_CLS11 [48] referenced [1993,2012] Until June 30th, 2015: Computed with 15 years of E1/E2/EN data; referenced [1993,2012]; Since July 1st, 2015: MSS_CNES_CLS11 [48] referenced [1993,2012] MSS_CNES_CLS11 [48] referenced [1993,2012] (1) A flag included in the along-track files indicates the source of the production (OGDR/FDGDR or IGDR). If flag=0, the processed data comes from OGDRs/FDGDRs; if flag=1, the processed data comes from IGDRs. Table 2: Corrections and models applied in SL-TAC NRT products produced from IGDRs. The period of reference has changed since April 2014: it is now 20 years instead of 7 years. Please refer to section

18 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 10 Product standard ref Orbit Ionopheric Dry troposphere Wet troposphere DAC Ocean tide NRT product from OGDR 2 j2 c2 al version D CPP [4] version T patch 2 bi-frequency altimeter range measurements Navigator GIM model (Iijima et al,1998[31]) Model computed from ECMWF Gaussian grids (new S1 and S2 atm tides applied) AMR radiometer (enhancement in coastal regions) Model computed from ECMWF Gaussian grids MOG2D High Resolution forced with ECMWF pressure and wind fields (S1 and S2 were excluded) (Carrere and Lyard, 2003[6])+ inverse barometer. Filtering temporal window is decentered using forecasts GOT4v8 (S1 and S2 are included) and TPX 07.2 [22] for Arctic products Pole tide [Wahr, 1985 [69]] Solid tide earth Loading tide Sea bias state Elastic response to tidal potential [Cartwright and Tayler, 1971[8]], [Cartwright and Edden, 1973[9]] Non Parametric SSB [N. Tran et al., 2012[65]] (with cycles J using GDR-D GOT4v8 (S1 and S2 are included) Non parametric SSB from J1 with unbiased sigma0 Hybrid SSB (method from Scharroo et al, 2004 [63] applied to al) Orbit error Specific filtering of long-wavelength signal 3 LW errors Optimal Interpolation [Le Traon et al., 1998[39]] Intercalibration Reference from cycle 20 Mean profile (see and ) Computed with 20 years of TP/J1/J2 data; referenced [1993,2012] MSS_CNES_CLS11 [48] referenced [1993,2012] Until June 30th, 2015: Computed with 15 years of E1/E2/EN data; referenced [1993,2012]; Since July 1st, 2015: MSS_CNES_CLS11 [48] referenced [1993,2012] (2) A flag included in the along-track files indicates the source of the production (OGDR or IGDR). If flag=0, the processed data comes from OGDRs; if flag=1, the processed data comes from IGDRs. (3) Specific data processing was applied on long wave-length signal ( of the user manual) Table 3: Corrections and models applied in SL-TAC NRT products produced from OGDRs. The period of reference has changed since April 2014: it is now 20 years instead of 7 years. Please refer to section

19 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Acquisition The acquisition process is twofold: straightforward retrieval and reformatting of altimeter data and dynamic auxiliary data (pressure and wet troposphere correction grids from ECMWF are provided by Meteo France, TEC grids from JPL, NRT MOG2D corrections,...) from external repositories. synchronisation process. To be homogenized properly, altimeter data sets require various auxiliary data. The acquisition software detects, downloads and processes incoming data as soon as they are available on remote sites (external database, FTP site). Data are split into passes if necessary. If data flows are missing or late, the synchronisation engine put unusable data in waiting queues and automatically unfreezes them upon reception of the missing auxiliary data. This processing step delivers "raw" data, that is to say data that have been divided into cycles and passes, and ordered chronologically. The acquisition step uses two different data flows in near-real time: the OGDR flow (within a few hours), and the IGDR flow (within a few days). For each OGDR input, the system checks that no equivalent IGDR entry is available in the data base before acquisition; for each IGDR input, the system checks and delete the equivalent OGDR entry in the data base. These operations aim to avoid duplicates in SL-TAC system. Figure 4: Overview of the near real time system data flow management

20 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Homogenization The homogenization process consists of applying the most recent corrections, models and references homogeneously for all missions and recommended for altimeter products. Each mission is processed separately as its needs depend on the base input data. The list of corrections and models currently applied is provided in tables 2 and 3 for NRT data. The system includes SLA filtering to process OGDR data. The SL-TAC processing extracts from these data sets the short scales (space and time) which are useful to better describe the ocean variability in real time, and merge this information with a fair description of large scale signals provided by the multi-satellite observation in near real time (read: IGDR-based data). Finally an "hybrid" SLA is computed. Figure 5: Merging pertinent information from IGDR and OGDR processing Input data quality control The Input Data Quality Control is a critical process applied to guarantee that SL-TAC uses only the most accurate altimeter data. Thanks to the high quality of current missions, this process rejects a small percentage of altimeter measurements, but these erroneous data could be the cause of a significant quality loss. The quality control relies on standard raw data editing with quality flags or parameter thresholds, but also on complex data editing algorithms based on the detection of erroneous artefacts, mono and multi-mission crossover validation, and macroscopic statistics to edit out large data flows that do not meet the system s requirements.

21 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Multi-mission cross-calibration The Multi-mission Cross-calibration process ensures that all flows from all satellites provide a consistent and accurate information. It removes any residual orbit error (OE, Le Traon and Ogor, 1998[40]), or long wavelength error (LWE, Le Traon et al., 1998[39]), as well as large scale biases and discrepancies between various data flows. This process is based on two very different algorithms: a global multi-mission crossover minimization for orbit error reduction (OER), and Optimal Interpolation (OI) for LWE. Multi-satellite crossover determination is performed on a daily basis. All altimeter fields (measurement, corrections and other fields such as bathymetry, MSS,...) are interpolated at crossover locations and dates. Crossovers are then appended to the existing crossover database as more altimeter data become available. This crossover data set is the input of the Orbit Error Reduction (OER) method. Using the precision of the reference mission orbit (TP/J1/J2), a very accurate orbit error can be estimated. This processing step does not concern OGDR data. LWE is mostly due to residual tidal, high frequency ocean signals remaining errors and residual orbit error. The OI used for LWE reduction uses precise parameters derived from: accurate statistical description of sea level variability regional correlation scales mission-specific noise and precise assumptions on the long wavelength errors to be removed (from a recent analysis of one year of data from each mission) Product generation The product generation process is composed of four steps: computation of raw SLA, cross-validation, filtering&sub-sampling, and generation of by-products.

22 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Change of Reference As from April 2014, an important modification has been implemented in SL-TAC products. Indeed, the period of reference was 7 years in older version (from 1993 to 1999) and it is now 20 years (from 1993 to 2012). This takes into account the variations of the oceans in the last years, as shown on Figure 6. The detailed informations can be found in the document: fr/fileadmin/documents/data/duacs/duacs2014.pdf In order to give the users the time to adapt to this new reference period, three gridded products are delivered for global ocean, Mediterranean and Black seas called "ref20yto7y" If you may wish to come back to the 7 year reference period (=SLA 7years ): for each measurement of SLA 20years from the new products, since the REF20YTO7Y products are gridded, you need to interpolate at the location of the SLA measurement the variable ref20yto7y (see 6.5. for information about the format of the file) calculate SLA 7years =SLA 20years from the new products - ref20yto7y previously calculated The absolute Dynamic Topography field is unchanged (the ADT products have been calculated with consistent SLA and MDT fields). Figure 6: Impact in cm of the reference change from 7 years to 20 years ([ ] to [ ])

23 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Computation of raw SLA The SSH anomalies are used in oceanographic studies. They are computed from the difference of the instantaneous SSH minus a temporal reference. This temporal reference can be a Mean Profile (MP) in the case of repeat track or a gridded MSS when the repeat track cannot be used. The errors affecting the SLAs, MPs and MSS have different magnitudes and wavelengths. The computation of the SLAs and their errors associated are detailed in Dibarboure et al, 2010 [14]. Use of a Mean Profile In the repeat track analysis at 1 Hz (when the satellites flies over a repetitive orbit), measurements are resampled along a theoretical ground track (or mean track) associated to each mission. Then a Mean Profile (MP) is subtracted from the re-sampled data to obtain SLA. The MP is a time average of similarly re-sampled data over a long period. The Mean Profile used for Saral/Altika until June 30th, 2015 is computed with 15 years of ERS-1, ERS-2 and Envisat, referenced to the period [1993, 2012]. Since July 1st, 2015, no Mean Profile can be used for AltiKa because the orbit is drifting. The MSS is used instead of the MP (see below). The Mean Profile used for Jason-2 is computed with 20 years of T/P, Jason-1 and Jason-2, referenced to years [1993, 2012]. No Mean Profile can be used for Cryosat-2 mission (c2). The MSS must be used instead (see below). No Mean Profile can be used for HY-2A mission (h2) because there are not enough data to calculate it. The MSS must be used instead (see below). Computation of a Mean Profile The computation of a Mean Profile is not a simple average of similarly co-located SSH data from the same ground track on the maximum period of time as possible. Indeed, as the satellite ground track is not perfectly controlled and is often kept in a band of about 1km wide, precise cross-track projection and/or interpolation schemes are required to avoid errors. The ocean variability is removed to minimize the seasonal/interannual aliasing effects. The mesoscale variability error is eliminated with an iterative process using a priori knowledge from Sea Level maps derived from previous iterations or from other missions.this process enables us to reference the mean profiles for all missions to a common period (reference period) for the sake of consistency with other missions. The reference period is [1993, 2012] and is thus independent from the number of years used to compute mean profiles. Moreover, the inter-annual variabilty error is accounted for by using the MSS. Finally, for these Mean Profiles, the latest standards and a maximum of data were used in order to increase as much as possible the quality of their estimation. Note that a particular care was brought to the processing near coasts. Use of a MSS The repeat track analysis is impossible for Cryosat-2 mission (c2) and AltiKa mission after July 1st, 2015 because the satellite is not in a repetitive orbit phase. Moreover, it is not possible for the moment to compute a mean profile for HY-2A because there is not enough data to compute it.. The alternative is to use the MSS instead. The gridded MSS is derived from along track MPs and data from geodetic phases. Thus any error

24 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 16 on the MP is also contained in the MSS. There are essentially 4 types of additional errors on gridded MSS which are hard to quantify separately: To ensure a global MSS coherency between all data sets, the gridding process averages all sensorspecific errors and especially geographically correlated ones. The gridding process has to perform some smoothing to make up for signals which cannot be resolved away from known track, degrading along-track content. There are also errors related to the lack of spatial and temporal data (omission errors). The error stemming from the geodetic data: the variability not properly removed before the absorption in the MSS. The MSS used in the products is MSS_CNES_CLS11 [48], referenced [1993,2012] Cross validation After the repeat track analysis, the cross-validation technique is used as the ultimate screening process to detect isolated and slightly erroneous measurements. Small SLA flows are compared to previous and independent SLA data sets using a- 12 year climatology and a 3 sigma criteria for outlier removal.

25 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Filtering and sub-sampling Residual noise and small scale signals are then removed by filtering the data using a Lanczos filter. As data are filtered from small scales, a sub-sampling is finally applied. Along-track SLA are then produced (those products are along-track *vfec* files). The filtering and sub-sampling is adapted to each region and product as a function of the characteristics of the area and of the assimilation needs. Details were presented in Dufau et al., 2013 [21]. Please, refer to the technical note fileadmin/documents/data/duacs/duacs2014.pdf for comparison between DUACS 2014 and former version. The values taken into account are the following: Filtering Sub-sampling Global 65 km 14km Mediterranean Sea about 40km 14km Black Sea about 40km 14km Europe about 35km No (7km) Arctic about 35km No (7km) Table 4: Near-Real Time Filtering and sub-sampling values Noise estimation Since April 2014, a new filtering level takes into account the mesoscale capability computed for the Jason- 2 1hz SLA. Users recommendations from the TAPAS working group motivated the choice of a spatially uniform cut-off length to filter SLA in association with a more accurate measurement error description. Therefore, a cut-off length of 65km is applied everywhere to filter SLA. Compared to the previous SLA filtering applied, it changes drastically the SLA resolution at low latitudes. In these areas, cut-off lengths are reduced from more than 100km. In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below 20km in term of wave length). The 1hz noise level spatial distribution (Figure 7) follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013 [12]) still remain to be achieved and overcome with new retracking, new editing strategy or new technology. Users of non-filtered SLA will have to use the 1hz estimated white noise (Figure 7 left) whereas users of filtered SLA the remaining error level after filtering (Figure 7 right).

26 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 18 Figure 7: Noise Level in 1hz Jason-2 SLA estimated from mean wavenumber spectra over 2011 in 10 x10 boxes (cm rms) before 65km filtering (left) and after (right) The regional products as Mediterranean Sea, Black Sea cover too small areas to be concerned by such a map of 1hz measurement error. In this case, a mean wavenumber spectrum has been computed over each area and the noise level estimated from it. A constant value is then prescribed for the 1hz noise level as well as the remaining error after filtering: Before Filtering After Filtering Cryosat Jason Saral/AltiKa HY-2A Table 5: Measurement noise error before/ after spatial filtering for Mediterranean and Black Sea products (cms rms)

27 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Computation of ADT Products Along-track ADT products are obtained as follows: ADT = SLA + MDT where MDT is the Mean Dynamic Topography. The Mean Dynamic Topography is the part of Mean Sea Surface Height due to permanent currents, so MDT corresponds to the Mean Sea Surface Height minus Geoid. Since DUACS 2014 version, a new MDT has been used: it takes into account the recent geoid mean field (GOCE DIR-R4) and in-situ dataset, as well as improved processing method. Details are presented in Rio et al, 2013 [59]. Note that the ADT products have been computed with consistent SLA and MDT fields: ADT = SLA 20years + MDT 20years The regional ADT product is computed using a specific regional MDT (Mediterranean Sea only) given in Rio et al., 2014 [60]: ADT Med = SLA Med + MDT Med The product generation processing step is activated daily in near real time Generation of gridded Sea Level Anomalies (MSLA) products Merging process The Merging process is twofold: mapping and generation of by-products. A mapping procedure using optimal interpolation with realistic correlation functions is applied to produce SLA and ADT maps (MSLA and MADT or L4 products) at a given date. The procedure generates a combined map merging measurements from all available altimeter missions (Ducet et al., 2000[20]). The mapping process takes into account an updated suboptimal Optimal Interpolation parameterization to minimize transition artefacts. More accurate correlation scales are used compared to Ducet et al., taking into account optimally the spatial variability of the signal. Combining data from different missions significantly improves the estimation of mesoscale signals (Le Traon and Dibarboure, 1999[41]), (Le Traon et al., 2001[42]), (Pascual et al., 2006[49]). Several improvements were made compared to the version used by (Le Traon et al., 1998[39]). An improved statistical description of sea level variability, noise and long wavelength errors is used. Covariance functions including propagation velocities that depend on geographical position were thus used. For each grid point, the zonal and meridional spatial scales, the time scale and the zonal and meridional propagation velocities were adjusted from five years of TP+ERS combined maps. In addition to instrumental noise, a noise of 10% of the signal variance was used to take into account the small scale variability which cannot be mapped and should be filtered in the analysis. In the NRT DUACS processing, contrary to DT case, the products cannot be computed with a centred computation time window for OER, LWE and mapping processes: indeed, as the future data are not available yet, the computation time window is not centered (For each day of production, 3 maps are computed: for

28 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 20 the maps of date D, D-3 and D-6 are using respectively the data in the time intervall of [D-42, D], [D-3-42, D+3] and [D-6-42, D+6]). Change of resolution In DUACS 2014 version, after the feedback from users, the Mercator grid projection with 1/3 x1/3 spatial resolution (Global product) is abandoned. The DUACS 2014 Global products are directly computed on a Cartesian 1/4 x1/4 spatial resolution. Please, refer to the technical note altimetry.fr/fileadmin/documents/data/duacs/duacs2014.pdf for comparison between DUACS 2014 and former version. Formal mapping error The formal mapping error represents a purely theoretical mapping error. It mainly represents errors induced by the constellation sampling capability and consistency with the spatial/temporal scales considered, as described in Le Traon et al (1998) [39] or Ducet et al (2000) [20]. In the former version, the formal mapping error was delivered as the "err" product, and expressed in version, it is expressed in meters and is delivered in the same NetCDF file as the SLA. SLA computation from OGDR is based on the same algorithms, only parameters are different to take into account OGDR specificity. LWE and mapping process are based on IGDR and GDR available residuals, also with specific parameters. The combined map is used to generate by-products such as geostrophic currents or absolute dynamic topography. Geostrophic currents Since DUACS 2014 version, the geostrophic current computation is improved with: The use of the 9-point stencil width method (Arbic et al, 2012, [3]) at latitudes appart from ±5. It contributes to reduce the impact of the anisotropy introduced by the Cartesian 1/4 grid resolution. The SLA computation is Largerloef et al, 1999 [36] in the equatorial band is improved in order to smooth the transition at ±5 and improve the consistency between altimeter products and drifter observations. More information can be found in data/duacs/duacs2014.pdf.

29 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Quality control The production of homogeneous products with a high quality data and within a short delay is the key feature of the SL-TAC processing system. But some events (failure on payload or on instruments, delay, maintenance on servers), can impact the quality of measurements or the data flows. A strict quality control on each processing step is indispensable to appreciate the overall quality of the system and to provide the best user services Final quality Control The Quality Control is the final process used by SL-TAC before product delivery. In addition to daily automated controls and warnings to the operators, each production delivers a large QC Report composed of detailed logs, figures and statistics of each processing step. Altimetry experts analyse these reports twice a week (only for internal validation, those reports are not disseminated). A shorter report is delivered to DUACS users upon each product delivery. This QC activity is used as a modest Cal/Val activity on NRT products. It provides level2 product centres with a detailed feedback on potential anomalies for a fast reprocessing of erroneous IGDR flows. The Quality Reports are now on the authenticated server. To access them via a browse, please type your login and password in the following address: ftp://login:password@ftp.aviso.altimetry.fr/quality_report/ Performance indicators To appreciate the quality situation of the DUACS system, performance indicators are computed daily. They aim at evaluate the status of the main processing steps of the system: the input data availability, the input data coverage, the input data quality and the output product quality. These indicators are computed for each and every currently working satellite, and combined to obtain the overall status. Figure 8: Example with the key performance indicator on 2009/06/27

30 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 22 See the description, the latest and previous indicators on Aviso website: ssaltoduacs-multimission-altimeter-products/key-performance-indicators.html

31 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Delayed Time processing steps Input data, models and corrections applied Delayed Time products are generated: from Aviso GDR products for Saral/AltiKa, Jason-1 (historical, tandem track and geodetic track), Jason-2 from ESA GDR products for Cryosat-2, Envisat (historical, new orbit and drifting track), ERS-1 (historical and geodetic track) and ERS-2 from NOAA GDR for GFO. from NSOAS GDR for HY-2A. The GDR products are computed with updated orbit and corrections as described in the following table.

32 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 24 Delayed-Time product j2 j1;j1n;j1g tp;tpn Product GDR-D GDR-D GDR-C Orbit Cnes POE (GDR-D standards for cycles 253 and GDR-E afterwards) Cnes POE (GDR-D standards) GSFC (ITRF 2005,GRACE last standards) Ionopheric Dual frequency altimeter range measurements Dual frequency (Topex), DORIS (POSEIDON) Dry troposphere Wet troposphere DAC Model computed from ECMWF Gaussian grids (new S1 and S2 atm. tides applied) JMR/AMR radiometer Model computed from ECMWF rectangular grids (new S1 and S2 atm. tides included) MOG2D High Resolution forced with ECMWF pressure and wind fields (S1 and S2 excluded) + inverse barometer computed from rectangular grids Model computed from ERA interim Gaussian grids (new S1 and S2 atm. tides included) TMR radiometer (Scharroo et al [62]) MOG2D High Resolution forced with ERA-Interim pressure and wind fields (S1 and S2 excluded) + inverse barometer computed from rectangular grids Ocean tide GOT4v8 (S1 and S2 are included) Pole tide [Wahr, 1985[69]] Solid earth Elastic response to tidal potential Cartwright and Tayler, 1971[8],Cartwright and Edden, 1973[9] tide Loading tide GOT4v8 (S1 parameter is included) Sea state Non Parametric SSB [N. Tran bias et al., 2012[65]] (with c J using GDR-D Non Parametric SSB [N. Tran et al., 2012[65]](with c J using GDR-C standards and GDR-D orbit Non parametric SSB [N. Tran and al. 2010[67]] (using c with GSFC orbit for TP-A; c with GSFC orbit for TP-B) Orbit error Global multi-mission crossover minimization (Le Traon and Ogor, 1998 [40]) LW errors Optimal Interpolation (Le Traon et al., 1998[39]) Intercalibration Reference from cycle 11 Reference from cycle 1 to 354 Global Bias MSL zero reference in early 1993 Calibrated on J1+TP Mean Sea Level (using c 1-11 J2); MSL zero reference in early 1993 Calibrated on TP Mean Sea Level (using c 1-11 J1); MSL zero reference in early 1993 Regional Bias Calibration on J2 Calibration on J1+J2 Mean profile Computed with 20 years of TP/J1: Computed with 20 years of TP/J1/J2 data; referenced (see TP/J1/J2 data; referenced [1993,2012]; and ) [1993,2012] TPN/J1N: computed with 6 years of TPN/J1N; referenced [1993,2012] Period of use from cycle 9 from cycle 9 cycles 1 to 481 Table 6: Corrections and models applied to SL-TAC DT products for TOPEX/Poseidon, Jason-1, Jason-2. The period of reference has changed since April 2014: it is now 20 years instead of 7 years. Please refer to section

33 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 25 DUACS DT product e1 e2 en;enn Product OPR GDR-V2.1+ Orbit Reaper (Rudenko et al., 2012[61]) Cnes POE (GDR-D standards) Ionopheric Dry troposphere Wet troposphere DAC Reaper (NIC09 model, Scharro and Smith, 2010, [64]) Bent model (c 36), GIM model (c 37) ((Iijima et al, 1998 [31]) Model computed from ERA interim Gaussian grids (new S1 and S2 atm. tides included) MWR radiometer MWR corrected for 23.6GHz TB drift (Scharroo et al., 2004[62])+ Neural Network algorithm (Tran and Obligis, 2003[66] MOG2D High Resolution forced with ERA-Interim pressure and wind fields (S1 and S2 excluded) + inverse barometer computed from rectangular grids Bi-frequency altimeter range measurement (c 64); GIM model c 65 (Iijima et al., 1999[31]) corrected for 8 mm bias Model computed from ECMWF Gaussian grids (new S1 and S2 atm. tides applied) Cycles 94: MWR (dist 50km from the coasts), ECMWF model (50 dist 10km) Cycles 95:MWR. MOG2D High Resolution forced with ECMWF pressure and wind fields (S1 and S2 excluded) + inverse barometer computed from rectangular grids Ocean tide GOT4v8 (S1 and S2 are included) Pole tide [Wahr, 1985[69]]] Solid earth Elastic response to tidal potential Cartwright and Tayler, 1971[8],Cartwright and Edden, 1973[9] tide Loading tide GOT4v8 (S1 parameter is included) Sea state BM3 [Gaspar and Ogor Non parametric SSB [Mertz et Non Parametric SSB [N. Tran et bias 1994[24]] al., 2005[47]] al., 2012[65]] compatible with enhanced MWR Orbit error Global multi-mission crossover minimization (Le Traon and Ogor, 1998 [40]) LW errors Optimal Interpolation (Le Traon et al., 1998[39]) Global Bias Calibrated on TP/J1/J2 Mean Sea Level Mean profile Computed with 15 years of E1/E2/EN data; referenced EN: Computed with 15 years of (see [1993,2012] E1/E2/EN data; referenced and ) [1993,2012] ENN: use of MSS_CNES_CLS11 [48], referenced [1993,2012] Period of use cycles 15 to 43 Including E-F geodetic phases cycles 1 to 83 from cycle 9 Table 7: Corrections and models applied in SL-TAC DT products for ERS-1, ERS-2, Envisat The period of reference has changed since April 2014: it is now 20 years instead of 7 years. Please refer to section

34 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 26 DT product c2 g2 al h2 Product CPP CNES [4] GDR (NOAA) GDR-T patch2 GDR (NSOAS) Orbit CNES POE (GDR-D standards for cycles 66 and GDR-E afterwards) GSFC (ITRF 2005, GRACE last standards Cnes POE (GDR-D standards for cycles 23 and GDR-E afterwards) Ionopheric From GIM model (Iijima et al, 1998 [31]) Dry troposphere Wet troposphere DAC Model computed from ECMWF Gaussian grids (new S1 and S2 atm. tides applied) Model computed from ECMWF rectangular grids (new S1 and S2 atm. tides included) Cnes POE (GDR-D standards) Model computed from ECMWF Gaussian grids (new S1 and S2 atm. tides applied) ECMWF Model GFO radiometer radiometer ECMWF Model MOG2D High Resolution forced with ECMWF pressure and wind fields (S1 and S2 excluded) + inverse barometer computed from rectangular grids GOT4v8 (S1 and S2 are included) Ocean tide Pole tide [Wahr, 1985[69]]] Solid earth tide Loading tide GOT4v8 (S1 parameter is included) Sea state Non parametric SSB bias from J1 with unbiased sigma0 Elastic response to tidal potential Cartwright and Tayler, 1971[8],Cartwright and Edden, 1973[9] Non parametric SSB Hybrid SSB (method [N. Tran et al., from Scharroo et al, 2010[67]] (using c 2004 [63] applied to 130 to 172 with GSFC al) orbit) Orbit error Global multi-mission crossover minimization (Le Traon and Ogor, 1998 [40]) Linear model LW errors Optimal Interpolation (Le Traon et al., 1998[39]) Global Bias Calibrated on TP/J1/J2 Mean Sea Level Mean profile Use of Computed with 8 Until March 31st, Use of (see MSS_CNES_CLS11 years of G2 ; 2015 (during c 22): MSS_CNES_CLS11 and ) [48], referenced [1993,2012] referenced [1993,2012] Computed with 15 years of E1/E2/EN [48], referenced [1993,2012] data; referenced [1993,2012]; Since April 1st, 2015 (c 22): Use of MSS_CNES_CLS11 [48], referenced [1993,2012] Period of use from cycle 14 cycles 37 to 222 from cycle 1 from cycle 67 Table 8: Corrections and models applied in SL-TAC DT products for Cryosat-2, GFO, AltiKa and HY-2A The period of reference has changed since April 2014: it is now 20 years instead of 7 years. Please refer to section

35 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Acquisition The acquisition process in Delayed time consists in a synchronisation process of all the auxiliary data required to homogenize propely the altimeter data sets. The acquisition step uses the GDRs provided by the agencies Homogenization The homogenization process consists of applying the most recent corrections, models and references homogeneously for all missions and recommended for altimeter products. Each mission is processed separately as its needs depend on the base input data. The list of corrections and models currently applied is provided in tables 6 for DT data Input data quality control The Input Data Quality Control is a critical process applied to guarantee that SL-TAC uses only the most accurate altimeter data. Thanks to the high quality of current missions, this process rejects a small percentage of altimeter measurements, but these erroneous data could be the cause of a significant quality loss. The quality control relies on standard raw data editing with quality flags or parameter thresholds, but also on complex data editing algorithms based on the detection of erroneous artefacts, mono and multi-mission crossover validation, and macroscopic statistics to edit out large data flows that do not meet the system s requirements.

36 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Multi-mission cross-calibration The Multi-mission Cross-calibration process ensures that all flows from all satellites provide a consistent and accurate information. It removes any residual orbit error (OE, Le Traon and Ogor, 1998[40]), or long wavelength error (LWE, Le Traon et al., 1998[39]), as well as large scale biases and discrepancies between various data flows. This process is based on two very different algorithms: a global multi-mission crossover minimization for orbit error reduction (OER), and Optimal Interpolation (OI) for LWE. Multi-satellite crossover determination is performed on a daily basis. All altimeter fields (measurement, corrections and other fields such as bathymetry, MSS,...) are interpolated at crossover locations and dates. Crossovers are then appended to the existing crossover database as more altimeter data become available. This crossover data set is the input of the Orbit Error Reduction (OER) method. Using the precision of the reference mission orbit (TP/J1/J2), a very accurate orbit error can be estimated. LWE is mostly due to residual tidal, high frequency ocean signals remaining errors and residual orbit error. The OI used for LWE reduction uses precise parameters derived from: accurate statistical description of sea level variability regional correlation scales mission-specific noise and precise assumptions on the long wavelength errors to be removed (from a recent analysis of one year of data from each mission) Product generation The product generation process is composed of four steps: computation of raw SLA, cross-validation, filtering&sub-sampling, and generation of by-products.

37 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Change of Reference As from April 2014, an important modification has been implemented in SL-TAC products. Indeed, the period of reference was 7 years in older version (from 1993 to 1999) and it is now 20 years (from 1993 to 2012). This takes into account the variations of the oceans in the last years, as shown on Figure 9. The detailed informations can be found in the document: fr/fileadmin/documents/data/duacs/duacs2014.pdf In order to give the users the time to adapt to this new reference period, three gridded products are delivered for global ocean, Mediterranean and Black seas called "ref20yto7y" If you may wish to come back to the 7 year reference period (=SLA 7years ): for each measurement of SLA 20years from the new products, since the REF20YTO7Y products are gridded, you need to interpolate at the location of the SLA measurement the variable ref20yto7y (see 6.5. for information about the format of the file) calculate SLA 7years =SLA 20years from the new products - ref20yto7y previously calculated The absolute Dynamic Topography field is unchanged (the ADT products have been calculated with consistent SLA and MDT fields). Figure 9: Impact in cm of the reference change from 7 years to 20 years ([ ] to [ ])

38 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Computation of raw SLA The SSH anomalies are used in oceanographic studies. They are computed from the difference of the instantaneous SSH minus a temporal reference. This temporal reference can be a Mean Profile (MP) in the case of repeat track or a gridded MSS when the repeat track cannot be used. The errors affecting the SLAs, MPs and MSS have different magnitudes and wavelengths. The computation of the SLAs and their errors associated are detailed in Dibarboure et al, 2010 [14]. Use of a Mean Profile In the repeat track analysis at 1 Hz (when the satellites flies over a repetitive orbit), measurements are resampled along a theoretical ground track (or mean track) associated to each mission. Then a Mean Profile (MP) is subtracted from the re-sampled data to obtain SLA. The MP is a time average of similarly re-sampled data over a long period. The Mean Profile used for Saral/Altika until March 31th, 2015 (during cycle 22) is computed with 15 years of ERS-1, ERS-2 and Envisat, referenced to the period [1993, 2012]. Since April 1st, 2015 (during cycle 22), no Mean Profile can be used for AltiKa because the orbit is drifting. The MSS is used instead of the MP (see below). The Mean Profile used for T/P (cycles 1 to 364), Jason-1 (cycles 1 to 259) and Jason-2 is computed with 20 years of T/P, Jason-1 and Jason-2, referenced to years [1993, 2012]. The Mean Profile used for T/P (cycles 368 to 481) and from Jason-1 cycle 262 to 374 (where satellites are on interleaved ground-tracks) is computed with 6 years of T/P and Jason-1, referenced to the period [1993, 2012]. The Mean Profile used for ERS-1 in its 35 days repetitive orbit mission, ERS-2, and Envisat (only for the first orbit, before November 2010) is computed with 15 years of ERS-1, ERS-2 and Envisat, referenced to the period [1993, 2012]. No Mean Profile can be used for Envisat on its new orbit (enn), Jason-1 on its geodetic orbit (j1g) nor for Cryosat-2 mission (c2). The MSS must be used instead (see below). No Mean Profile can be used for HY-2A mission (h2) because there are not enough data to calculate it. The MSS must be used instead (see below). The Mean Profile used for GFO is computed with 8 years of G2, referenced to the period [1993,2012]. Computation of a Mean Profile The computation of a Mean Profile is not a simple average of similarly co-located SSH data from the same ground track on the maximum period of time as possible. Indeed, as the satellite ground track is not perfectly controlled and is often kept in a band of about 1km wide, precise cross-track projection and/or interpolation schemes are required to avoid errors. The ocean variability is removed to minimize the seasonal/interannual aliasing effects. The mesoscale variability error is eliminated with an iterative process using a priori knowledge from Sea Level maps derived from previous iterations or from other missions.this process enables us to reference the mean profiles for all missions to a common period (reference period) for the sake of consistency with other missions. The reference period is [1993, 2012] and is thus independent from the number of years used to compute mean profiles.

39 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 31 Moreover, the inter-annual variabilty error is accounted for by using the MSS. Finally, for these Mean Profiles, the latest standards and a maximum of data were used in order to increase as much as possible the quality of their estimation (see tables 6 to 8: Corrections and models applied in SSALTO/DUACS Delayed-Time products). Note that a particular care was brought to the processing near coasts. Use of a MSS The repeat track analysis is impossible for ERS-1 for its 168 days geodetic mission (phases E-F from April 1994 to March 1995) or for Envisat since November 2010, for Jason-1 on its geodetic orbit (j1g), for Cryosat- 2 mission (c2) and AltiKa mission after April 1st, 2015 because the satellite is not in a repetitive orbit phase. Moreover, it is not possible for the moment to compute a mean profile for HY-2A because there is not enough data to compute it.. The alternative is to use the MSS instead. The gridded MSS is derived from along track MPs and data from geodetic phases. Thus any error on the MP is also contained in the MSS. There are essentially 4 types of additional errors on gridded MSS which are hard to quantify separately: To ensure a global MSS coherency between all data sets, the gridding process averages all sensorspecific errors and especially geographically correlated ones. The gridding process has to perform some smoothing to make up for signals which cannot be resolved away from known track, degrading along-track content. There are also errors related to the lack of spatial and temporal data (omission errors). The error stemming from the geodetic data: the variability not properly removed before the absorption in the MSS. The MSS used in the products is MSS_CNES_CLS11 [48], referenced [1993,2012] Cross validation After the repeat track analysis, the cross-validation technique is used as the ultimate screening process to detect isolated and slightly erroneous measurements. Small SLA flows are compared to previous and independent SLA data sets using a- 12 year climatology and a 3 sigma criteria for outlier removal.

40 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Filtering and sub-sampling Residual noise and small scale signals are then removed by filtering the data using a Lanczos filter. As data are filtered from small scales, a sub-sampling is finally applied. Along-track SLA are then produced (those products are along-track *vfec* files). Note that in Delayed-time, files *vxxc* are also produced: they are not filtered and not sub-sampled). The filtering and sub-sampling is adapted to each region and product as a function of the characteristics of the area and of the assimilation needs. Details were presented in Dufau et al., 2013 [21]. Please, refer to the technical note duacs/duacs2014.pdf for comparison between DUACS 2014 and former version. The values taken into account for the filtering are the following: Filtering Sub-sampling Global 65 km 14km for filtered, 7km for unfiltered Mediterranean Sea about 40km 14km for filtered, 7km for unfiltered Black Sea about 40km 14km for filtered, 7km for unfiltered Table 9: Delayed Time Filtering and sub-sampling values Noise estimation Since April 2014, a new filtering level takes into account the mesoscale capability computed for the Jason- 2 1hz SLA. Users recommendations from the TAPAS working group motivated the choice of a spatially uniform cut-off length to filter SLA in association with a more accurate measurement error description. Therefore, a cut-off length of 65km is applied everywhere to filter SLA. Compared to the previous SLA filtering applied, it changes drastically the SLA resolution at low latitudes. In these areas, cut-off lengths are reduced from more than 100km. In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below 20km in term of wave length). The 1hz noise level spatial distribution (Figure 10) follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013 [12]) still remain to be achieved and overcome with new retracking, new editing strategy or new technology. Users of non-filtered SLA will have to use the 1hz estimated white noise (Figure 10 left) whereas users of filtered SLA the remaining error level after filtering (Figure 10 right).

41 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 33 Figure 10: Noise Level in 1hz Jason-2 SLA estimated from mean wavenumber spectra over 2011 in 10 x10 boxes (cm rms) before 65km filtering (left) and after (right) The regional products as Mediterranean Sea, Black Sea cover too small areas to be concerned by such a map of 1hz measurement error. In this case, a mean wavenumber spectrum has been computed over each area and the noise level estimated from it. A constant value is then prescribed for the 1hz noise level as well as the remaining error after filtering: Before Filtering After Filtering Cryosat Jason Saral/AltiKa HY-2A ERS ERS Envisat Geosat Follow-on Jason Topex/Poseidon Table 10: Measurement noise error before/ after spatial filtering for Mediterranean and Black Sea products (cms rms)

42 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Computation of ADT Products Along-track ADT products are obtained as follows: ADT = SLA + MDT where MDT is the Mean Dynamic Topography. The Mean Dynamic Topography is the part of Mean Sea Surface Height due to permanent currents, so MDT corresponds to the Mean Sea Surface Height minus Geoid. Since DUACS 2014 version, a new MDT has been used: it takes into account the recent geoid mean field (GOCE DIR-R4) and in-situ dataset, as well as improved processing method. Details are presented in Rio et al, 2013 [59]. Note that the ADT products have been computed with consistent SLA and MDT fields: ADT = SLA 20years + MDT 20years The regional ADT product is computed using a specific regional MDT (Mediterranean Sea only) given in Rio et al., 2014 [60]: ADT Med = SLA Med + MDT Med Generation of gridded Sea Level Anomalies (MSLA) products Merging process The Merging process is twofold: mapping and generation of by-products. A mapping procedure using optimal interpolation with realistic correlation functions is applied to produce SLA and ADT maps (MSLA and MADT or L4 products) at a given date. The procedure generates a combined map merging measurements from all available altimeter missions (Ducet et al., 2000[20]). The mapping process takes into account an updated suboptimal Optimal Interpolation parameterization to minimize transition artefacts. More accurate correlation scales are used compared to Ducet et al., taking into account optimally the spatial variability of the signal. Combining data from different missions significantly improves the estimation of mesoscale signals (Le Traon and Dibarboure, 1999[41]), (Le Traon et al., 2001[42]), (Pascual et al., 2006[49]). Several improvements were made compared to the version used by (Le Traon et al., 1998[39]). An improved statistical description of sea level variability, noise and long wavelength errors is used. Covariance functions including propagation velocities that depend on geographical position were thus used. For each grid point, the zonal and meridional spatial scales, the time scale and the zonal and meridional propagation velocities were adjusted from five years of TP+ERS combined maps. In addition to instrumental noise, a noise of 10% of the signal variance was used to take into account the small scale variability which cannot be mapped and should be filtered in the analysis. In the DT processing, the products can be computed optimally with a centred computation time window for OER, LWE and mapping processes (if D is the date of the map, the time intervall of the data taken into account for the computation of the map is [D-6 weeks, D+6 weeks]). Number of satellites to compute the maps Two series of maps are computed in Delayed-Time: two-sat-merged and all-sat-merged. You will find the

43 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 35 list of missions in figures 13 and 12 respectively. Note that when there are only one or two satellites flying, the two series have the same content. The list of satellites taken into account to compute the maps are also provided in the gridded file in the global attribute "platform". two-sat-merged series takes into account 2 satellites maximum in the computation of the map. It is homogeneous and stable in time dataset thanks to a stable sampling but it might not be the best quality at a given time. The use of this series is mainly for applications in need of great stability. all-sat-merged takes into account the all the satellites available (up to 4 satellites). Thus it has the best possible sampling. This series is better in quality but not homogeneous over the time period, because it is based on the best sampling available in time. Change of resolution In DUACS 2014 version, after the feedback from users, the Mercator grid projection with 1/3 x1/3 spatial resolution (Global product) is abandoned. The DUACS 2014 Global products are directly computed on a Cartesian 1/4 x1/4 spatial resolution. Please, refer to the technical note altimetry.fr/fileadmin/documents/data/duacs/duacs2014.pdf for comparison between DUACS 2014 and former version. Formal mapping error The formal mapping error represents a purely theoretical mapping error. It mainly represents errors induced by the constellation sampling capability and consistency with the spatial/temporal scales considered, as described in Le Traon et al (1998) [39] or Ducet et al (2000) [20]. In the former version, the formal mapping error was delivered as the "err" product, and expressed in version, it is expressed in meters and is delivered in the same NetCDF file as the SLA. The combined map is used to generate by-products such as geostrophic currents or absolute dynamic topography. Geostrophic currents Since DUACS 2014 version, the geostrophic current computation is improved with: The use of the 9-point stencil width method (Arbic et al, 2012, [3]) at latitudes appart from ±5. It contributes to reduce the impact of the anisotropy introduced by the Cartesian 1/4 grid resolution. The SLA computation is Largerloef et al, 1999 [36] in the equatorial band is improved in order to smooth the transition at ±5 and improve the consistency between altimeter products and drifter observations. More information can be found in data/duacs/duacs2014.pdf.

44 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Quality control The production of homogeneous products with a high quality data and within a short delay is the key feature of the SL-TAC processing system. But some events (failure on payload or on instruments, delay, maintenance on servers), can impact the quality of measurements or the data flows. A strict quality control on each processing step is indispensable to appreciate the overall quality of the system and to provide the best user services Final quality Control The Quality Control is the final process used by SL-TAC before product delivery. In addition to daily automated controls and warnings to the operators, each production delivers a large QC Report composed of detailed logs, figures and statistics of each processing step. Altimetry experts analyse these reports twice a week (only for internal validation, those reports are not disseminated). A shorter report is delivered to DUACS users upon each product delivery. The Quality Reports are now on the authenticated server. To access them via a browse, please type your login and password in the following address: ftp://login:password@ftp.aviso.altimetry.fr/quality_report/

45 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS SSALTO/DUACS Products 5.1. Near Real Time Products The purpose of the NRT component is the acquisition of altimeter data from various altimeter missions in near-real time (i.e. within a few days at most) and in real-time for global area on all satellites (i.e. within a few hours), the validation and correction of these altimeter data sets (i.e edition and selection, update of corrections and homogenization, orbit error reduction) in order to produce each day along-track products and gridded products. Exploitation of real time OGDR/FDGDR data allows the DUACS system to produce multi-mission maps with 0-day and 3-day delay whereas historical NRT (IGDR-based) production have a 6-day delay (induced by historical trade-off in terms of timeliness vs quality). The quality measurements in the NRT processing is more sensitive to the number of altimeter missions involved in the system. This is mainly due to the orbit error and the non-centered processing time-window (in NRT case, "future" data are not available; the computation time window takes into account only the 6 weeks before the date). If two altimeters are acknowledged as the bare minimum needed to observe mesoscale signals in DT maps, three or even four missions are needed to obtain equivalent accuracy in NRT (Pascual et al., 2006[49]). As described in and time invariant products are also disseminated: The noise level of along-track measurements is delivered. This is a gridded product. One file is provided for the global Ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given, as described in the following table. The "REF20YTO7Y" product is provided temporarily for the needs of users who wish to work with the reference period of 7 years instead of 20 years (see ). This is a gridded product. There are three zones provided: global Ocean, Mediterranean and Black seas. For Arctic and Europe areas, the file to be taken into account is the global product.

46 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 38 Along-track products NRT SLA NRT ADT Gridded Noise on SLA NRT NOISE SLA Gridded Change of reference CHANGE REF SLA Filtered Filtered Global X X X X Mediterranean X X see table 5 X Black Sea X - see table 5 X Arctic X - Same as global Same as global Europe X - Same as global Same as global Mozambique X - Same as global Same as global Table 11: List of the Along-track products in NRT Gridded products NRT MSLA NRT MADT Gridded Change of reference CHANGE REF SLA h+err uv h uv Global all sat all sat all sat all sat X Mediterranean all sat all sat all sat all sat X Black Sea all sat all sat - - X Mozambique all sat all sat - - Same as global Table 12: List of the Gridded products in NRT, 1/4 cartesian resolution for global and 1/8 cartesian resolution for regional

47 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Delay of the products The availability of the products in near real time is for along-track products: three to twelve hours after the measurement for regional products and 2 hours for global Saral, and Jason-2 products and 3 hours for global Cryosat-2 products. for gridded products: day-0, day-3 and day-6 days. Those products are delivered every day. Three merged maps are produced daily, each with a different delay and quality: A 6-day delay, which represents a final NRT map production, A 3-day delay, which represents an intermediate map production, and a 0-day delay, which represents a preliminary map production, based on IGDR+OGDR production. Then, these maps are replaced when a better quality data is available: At d 0+3, the intermediate map replaces the preliminary map which was produced at d 0. At d 0+3, the final NRT map replaces the intermediate map which was produced at d 0. At d 0+6, the intermediate map replaces the preliminary map which was produced at d 0+3. At d 0+6, the final NRT map replaces the preliminary map which was produced at d 0. Figure 11: Three merged maps are produced daily: final map (d-6), intermediate map (d-3) and prliminary map (d0)

48 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Temporal availibility The following table presents the available products by mission and by data period: Near real time products: NRT Jason-2 Cryosat-2 Saral/AltiKa HY-2A Merged Temporal availability Time y/m ongoing 2012/01/01 ongoing 2013/07/01 ongoing 2014/04/29 ongoing NRT-SLA X X X X NRT-MSLA UV anomalies ( ) NRT-ADT X X X X NRT-MADT Absolute UV ( ) y/m ongoing X X X X y/m: the temporal coverage for NRT products is fluctuating and is updated regularly (3 to 4 times per year when DT products are updated). The users are advised thanks to the Aviso web at: en/data/product-information/duacs/presentation/updates/index.html UV anomalies are Geostrophic velocities anomalies derived from NRT-MSLA Absolute UV are absolute Geostrophic velocities derived from NRT-MADT

49 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Delayed Time Products The Delayed Time component of SL-TAC system is responsible for the production of processed HY-2A, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, T/P, Envisat, GFO, ERS1/2 data in order to provide a homogeneous, inter-calibrated and highly accurate long time series of SLA and MSLA altimeter data. DT products are more precise than NRT products. Two reasons explain this quality difference. The first one is the better intrinsic quality of the POE orbit used in the GDR processing. The second reason is that in the DT DUACS processing, the products can be computed optimally with a centred computation time window for OER, LWE and mapping processes (6 weeks before and after the date). On the contrary in NRT case, "future" data are not available so the computation time window is not centred and therefore not optimal. As for NRT products, improved altimeter corrections and processing algorithms are used: ocean tide model to correct altimeter data, improved methods for orbit error reduction and mapping. Two types of madt and msla gridded products are delivered: all-sat-merged (for "Combining all satellites available"): this is an up-to-date series using the whole missions available (up to 4 satellites at a given time). Thus it has the best possible sampling. All-sat-merged series is better in quality but not homogeneous over the time period, because it is based on the best sampling available in time. two-sat-merged (for "Combining two satellites"): this set is based on only two missions at most: Jason-2/AltiKa or Jason-2/Cryosat-2 or Jason-2 / Envisat or Jason-1 / Envisat or Topex/Poseidon / ERS, with the same groundtrack. Thus it is homogeneous all along the available time period. thanks to a stable sampling, but might not be the best in quality at a given time. The use of the two-sat-merged series is mainly for application in need of great stability (but it must be kept in mind that the data might not be of the best possible quality). The difference between these two processing series is thus the number of missions as key input of Optimal Interpolation (OI) Software for LWE (4.3.5.). Note that when there are only one or two satellites flying, the two series have the same content. The list of satellites taken into account to compute the maps are also provided in the gridded file in the global attribute "platform". As described in and time invariant products are also disseminated: The noise level of along-track measurements is delivered. This is a gridded product. One file is provided for the global Ocean. For Mediterranean and Black seas, values have been computed (see ). The "REF20YTO7Y" product is provided temporarily for the needs of users who wish to work with the reference period of 7 years instead of 20 years (see ). This is a gridded product. There are three zones provided: global Ocean, Mediterranean and Black seas. The final delivering of Delayed Time are: All the gridded Delayed-Time products (merged global/regional - twosat/allsat) have been computed with a daily temporal resolution. This means that the maps are reprocessed for every day. This ambitious reprocessing was motivated by the fact that the weekly resolution used so far for DT maps in former version was insufficient wherever the time decorrelation scale is close to 15 days, especially when end-users want to perform a simple linear time interpolation between consecutive maps.

50 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 42 Along-track products DT SLA DT ADT Gridded Noise on SLA DT NOISE SLA Gridded Change of reference CHANGE REF SLA Filtered Unfiltered Filtered Uniltered Filtered Unfiltered Filtered Unfiltered VFEC VXXC VFEC VXXC Global X X X X X X X X Mediterranean X X X X see table 10 X Same as filtered Black Sea X X - - see table 10 X Same as filtered Table 13: List of the Along-track products in DT Gridded products DT MSLA DT MADT Gridded Change of reference CHANGE REF SLA h+err uv h uv Global all sat all sat all sat all sat X two sat two sat two sat two sat Mediterranean all sat all sat all sat all sat X two sat two sat two sat two sat Black Sea all sat all sat - - X two sat two sat - - Table 14: List of the Gridded products in DT, 1/4 cartesian resolution for global and 1/8 cartesian resolution for regional Delay of the products Daily products are delivered. The availability of the products in delayed time is at the best two months after the date of the measurement. The product generation needs all the GDR data of all the missions to take into account the best corrections as possible. The time delay can be longer in the case of a missing mission.

51 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS Temporal availibility The following table presents the available products by mission and by data period: Delayed time SSALTO/DUACS all-sat-merged products: all sat Temporal availibility DT-SLA DT- MSLA begin date end date UV DT-ADT anomalies 3 HY-2A 2014/04 y/m 1 X X Saral/AltiKa 2013/03 y/m 1 X X Cryosat /04 y/m 1 X X Jason /10 y/m 1 X X Jason-1 geodetic / /04 X X Jason-1 new / /03 X X Jason / /10 X X GFO 2000/ /09 X X Envisat new / /04 X X Envisat 2002/ /10 X X ERS-1 2 /ERS / /04 X X Topex new / /10 X X Topex 1993/ /04 X X DT- MADT Merged 1993/01 y/m 1 X X X X Absolute UV 4 1 y/m: the temporal coverage for DT products is fluctuating and is updated regularly (3 to 4 times per year). The users are advised thanks to the Aviso web at: updates-and-reprocessing/ssaltoduacs-delayed-time-reprocessing.html 2 Jason-1 geodetic orbit: starting 2012/05, Jason-1 new orbit : starting 2009/02, Envisat new orbit : starting 2010/10, T/P new orbit : starting 2002/09, ERS-1 geodetic phases (E-F) are included. No ERS-1 data between December 23, 1993 and April 10, 1994 (ERS-1 phase D - 2 nd ice phase). Note that, during that time, merged products are based only on Topex/Poseidon data. 3, 4 UV are Geostrophic velocities derived from NRT-MSLA or NRT-MADT. The merged products were obtained with the satellites given in figure 12. Note that when there are one or two satellites to compute the map, the two series two-sat-merged and all-sat-merged are identical. Moreover, the global attribute in the gridded file called "platform" gives the list of satellites used to compute the map.

52 CLS-DOS-NT Issue Date : 2015/06/30 - Nomenclature : SALP-MU-P-EA CLS 44 Figure 12: List of satellites in all-sat-merged products