1. An Overview of the L2OS ESL WorkPlan NR & ESLs

2. A document as been Prepared by L2OS/ESL For the work activities To be berformed 2014-2016 Delivered mid-april

3. Content of that Doc Review of current status of L2OS What shall be the L2OS results in 2016 -Consolidated L2OS products definition- What is the associated strategy for the development of the Level 2 OS processor for 2014-2016? What is our performance metrics for L2OS data quality and how do we demonstrate progress ? How does this strategy fits the SMOS mission Programmatic?

5. From Level 1: Strong (from the oceanographer perspective ±1 psu) and systematic brightness temperature data contamination over the oceans by land masses within an about 800 kms-width band along the world coasts, Seasonal and latitudinal unexpected variations (from the oceanographer perspective) in the Brightness temperature data, Inaccuracies in the Radio Frequency Interferences (RFI) contamination filtering, Remaining noise in the Brightness temperature associated with solar radiation impacts on the reconstructed images which impact retrieved SSS data quality, Systematic spatial biases in the reconstructed TB images (partially mitigated at L2 by the OTT), Inaccuracies in the Total Electronic Content, Uncertainties in the polarization purity of the L1C data Remaining issues in L2 SSS quality From L1

6. At Level 2: Non Optimal Radio Frequency Interferences (RFI) contamination filtering, Decreased sensitivity of the L-band signal to SSS in cold seas, Remaining sea water dielectric constant modelling uncertainties, Inaccuracies in the corrections for the sea surface roughness effects (wave & currents impacts), Inaccuracies in the corrections for extraterrestrial radiation glints (galactic and solar) Non yet accounted for geophysical effects in the forward models (rain impact, diurnal cycle of SST,..) Inaccuracies in the geophysical auxiliary data sets used as priors in the retrieval scheme to characterize the oceanic and meteorological conditions in the observed scenes, Non yet fully optimal iterative inversion methodology and data filtering (quality control) strategies Remaining issues in L2 SSS quality From L2

10. O1) -fill the gap of badly sampled zones by in situ (e.g. ARGO floats are generally not crossing the oceanic fronts and cannot provide a space-time monitoring capability for across-front salt exchanges), O2) -provide SSS variability monitoring on time scales < 1 month and <200 km, ideally 10 days 50 km., O3) -provide an interfacial estimate for SSS and a better indirect gauge of large scale and meso-scale processes involved into the E-P-R balance over the ocean (fresh-water fluxes), O4) -combined with SST, improve altimeter-based estimates of surface currents (e.g. density, water masses-tracking, salt exchanges across natural boundaries,..) and thermohaline circulation, O5) -In synergy with other sensors (in situ and satellite EO), improve our understanding of air-sea interactions (Wind induced mixing and stirring of the upper ocean layers, barrier-Layers related processes, salt-driven ocean stratification, Tropical Instability waves,. Fresh water fluxes.) 2014-2017= SMOS+AQUARIUS+SMAP

15. Pre TEP Pre-TEP ?

16. © Craig Donlon QUALITY MANAGEMENT TOOL - “PRE-TEP” OCEAN  One platform combining different functionalities  Data quality control: where does information come from for SSS at particular grid point, what went into this particular SSS value  Match-up data base: satellite versus in-situ data  Ancillary data: Collocation with “other” ocean data (SST, altimetry etc)  A tool for anyone to use: ESLs and external users alike, i.e. accessible for SSS (and beyond) community  Implementation  Needs to be located at existing data hub (for me)  Start regionally/Pilot-pre-TEP: SPURS, our favourite area in the Pacific, Atlantic  Use existing systems (FELIX) and ATBDs (IFREMER) as start up Pre-TEP

17. A First View on SMOS/OS Match-Up Database Craig’s plot NB: Central to is view is the L3 OS point

18. Profilers from the Argo network Thermo-salinographs Installed onboard reasearch Vessels and ships of opportunity Permanent Moorings Surface ‘Drifters’ Gliders Equipped Mammals

19. =>In a first step, MDB structure shall be driven by in situ data and not by SMOS because of the sparser in situ data sampling distribution

20. A revisit of Craig’s plot as first step for the Pre-TEP NB: Central to His view is the L3 OS point Central to my view is in situ data Over any available & Qualified in situ obs Seek for SMOS L1, L2, L3 and Aux data

21. Qualified In situ SSS data SSS in situ (lat i,lon i,t i, z i ) ARGO Floats upper level data TSG onboard VOS Tropical Moored Buoy System: TAO, TRITON, PIRATA, RAMA Scientific Campaign Data: CTD Others: Surface drifters Gliders Equipped mamals +Additional info SST in situ source (lat i,lon i,t i, z i ) S in situ source (lat i,lon i,t i, d i, z i ) T in situ source (lat i,lon i,t i, d i, z i ) Rain in situ source (lat i,lon i,t i, d i, z i ) wind in situ source (lat i,lon i,t i, d i, z i ) QCFlags in situ source (lat i,lon i,t i, d i, z i ) In situ SSS data Types In situ SSS Database 1: Real Time Mode In situ SSS Database 2: Delayed Time Mode: Coriolis Data Assembly CenterGOSUDLabos In situ SSS data Sources Step 1: In Situ DataBases collection

22. Real Time DBs daily per sensor type: ARGO, TSG, Moorings, Drifters,.. Delayed Time DBs daily per sensor type: ARGO, TSG, Moorings, Drifters,.. Step 1: In Situ DataBases structure SSS i ARGO (lat i,lon i,t i,z i ) SSS i TSG (lat i,lon i,t i,z i ) +Additional info when available SST in situ source (lat i,lon i,t i, z i ) S in situ source (lat i,lon i,t i, d i, z i ) T in situ source (lat i,lon i,t i, d i, z i ) Rain in situ source (lat i,lon i,t i, d i, z i ) wind in situ source (lat i,lon i,t i, d i, z i ) QCFlags in situ source (lat i,lon i,t i, d i, z i )

23. Step 2: Apply QC flags In situ SSS Database 1: Real Time Mode In situ SSS Database 2: Delayed Time Mode: Apply quality control flags Ex for ARGO: PSAL_QC <=2 In situ SSS Database 1: Real Time Mode Basic Quality Controlled DB In situ SSS Database 2: Delayed Time Mode: Best available Quality Controlled DB

24. In situ SSS Database 1: Real Time Mode Basic Quality Controlled DB In situ SSS Database 2: Delayed Time Mode: Best available Quality Controlled DB Step 3: In Situ DataBases Space time Sampling filtering Original TSG and drfiter SSS data at High resolution Filtered SSS data at spatial resolution comparable to SMOS pixel sizes ~40 km to 80 kms (steps of 10 kms) TSG Drifters Filters specification !

25. Rain at AQ Freshening / surface splash Rain at AQ Freshening / surface splash dry fresh bias ≈ - 0.1 psu dry fresh bias ≈ - 0.1 psu North Pacific rainy zone (Grodksy & Carton) * Rainy zones in the open ocean produce a few psu fresh layers on top of the classical mixed layer. Due to stable haline stratification remaining diurnal warming or nocturnal cooling layers are present in local night. * Near frontal zones (like the Gulf Stream) advective surface fresh layers may be up to 1 psu fresh. SMOS SSS d=1cm ARGO SSS d=5m Step 4: Vertically stratified SSS classification

26. Qualified In situ SSS data SSS in situ (lat i,lon i,t i, z i ) Rainy or Dry point ? Rain Rates for the last hours before t i at lat i,lon i (satellite, GPCP,..?) Rainy Flag (Intense Rain very likely rainy, Possible rain, Uncertain,..) Dry Point Flag Wind speed for the last days before t i at lat i,lon i (satellite, ECMWF,..?) Mixed or stratified surface Auxilliary data Rain Rates Climatology Mixed Layer Depth Climatology Stratification Flag Well mixed rainy Stratified rainy River Plume or open ocean ? River Plume Flag TBD ? Stratified In river plume Open ocean Flag

27. Step 5: In Situ SSS-SMOS data co-localization Configuration of Spatial Radius R=Δx of co-localization Search for DGG grid points around the in situ data within radius of research  List of DGG grid points DGG i=1,…,n Configuration of co-localisation Time lag Δt => Determine all SMOS passes at time t o intercepting DGG i=1,…,n within t o ±Δt

28. In situ SSS data SSS in situ (lat i,lon i,t i, z i ) +Additional in situ info when available DB1 ARGODB1 TSGDB1 MODB1 CTDDB1 DRIFTERDB1 othersReal time Databases DB2 ARGODB2 TSGDB2 MODB2 CTDDB2 DRIFTERDB2 othersDelayed time Databases Step 2: Apply QC Flags Step 1: In Situ DataBases Collections In situ SSS Database 1: Real Time Mode Basic Quality Controlled DB In situ SSS Database 2: Delayed Time Mode: Best available Quality Controlled DB Step 3: Apply Spatio-temporal filtering Step 4: Vertical Stratification Classification Step 5: Co-localisation with SMOS data L1C & L2 MatchUp Databases 1-Real Time- DBs ARGO, TSG,etc.. Classif: Rain stratif, Plume stratif, Open ocean well mixed.. SSS SMOS,SSS in situ, aux geophys, Tb L1C, L2 flags.. Match Up Databases 2-Delayed Time ARGO, TSG, etc.. Classif Rain stratif, Plume stratif, Open ocean well mixed.. SSS SMOS,SSS in situ, aux geophys, Tb L1C,L2 flags.. Rain & wind Auxilliary Geophys Data Conf file 1 Conf file 2 Radius, Δt SMOS ½ orbits L1C & L2 t o,lat(t o ),lon(t o ) on DGG

29. MatchUp Databases 1-Real Time- DBs ARGO, TSG,etc.. Classif: Rain stratif, Plume stratif, Open ocean well mixed.. SSS SMOS,SSS in situ, L1 & L2 aux data & flags, Tb L1C,.. Match Up Databases 2-Delayed Time ARGO, TSG, etc.. Classif Rain stratif, Plume stratif, Open ocean well mixed.. SSS SMOS,SSS in situ, L1 & L2 aux data & flags, Tb L1C,.. Step 6: Co-localisation with other Geophysical Datasets Other Wind & Waves SST Rain Altimetry Other Data (Aquarius, SMAP)… Augmented Match Up Databases 2 -Delayed Time- ARGO, TSG, etc.. Classif Rain stratif, Plume stratif, Open ocean well mixed.. SSS SMOS,SSS in situ, L1 & L2 aux data & flags, Tb L1C,.. + Other Wind & Waves,SST,Rain, Altimetry, Color … Augmented Match Up Databases 1 -Real Time- ARGO, TSG, etc.. Classif Rain stratif, Plume stratif, Open ocean, well mixed.. SSS SMOS,SSS in situ, L1 & L2 aux data & flags, Tb L1C,.. + Other Wind & Waves,SST,Rain, Altimetry, Color …

30. Augmented MDB1 Real Time Augmented MDB1 Real Time Augmented MDB2 Delayed Time Augmented MDB2 Delayed Time Additional Qualification Tools: OA in situ … Additional Qualification Tools: OA in situ … L1/L2 Statistics Bulletin … L3 Statistics Bulletin … L1/L2 Statistics Bulletin …

31. SMOS In situ OI data (ISAS) SMOS-ISAS Additional Qualification Tools at L3: OA in situ

32. User Interface for the Pre-TEP -Web & ftp User access to MDBs -L2 & user defined Forward models predictions at MDB points based on SSS in situ -L1 & L2 Processor version monitoring facilities -Global metrics -Regional analyses & statistics