An Overview of the L2OSESL WorkPlan

NR & ESLs

A document as beenPrepared by L2OS/ESLFor the work activitiesTo be berformed2014-2016

Delivered mid-april

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?

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

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

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

Pre TEP

Pre-TEP ?

© 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

A First View onSMOS/OS Match-Up Database

Craig’s plotNB: Central to is view is the L3 OS point

In Situ measurements of S

Profilers from the Argo network

Thermo-salinographsInstalled onboard reasearch Vessels and ships of opportunity

Permanent Moorings

Surface ‘Drifters’

Gliders

Equipped Mammals

=>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

A revisit of Craig’s plot as first step for the Pre-TEPNB: Central to His view is the L3 OS pointCentral to my view is in situ data

Over any available & Qualified in situ obsSeek for SMOS L1, L2, L3 and Aux data

Qualified In situ SSS data SSSin situ(lati,loni,ti, zi )

ARGO Floats upper level

data

TSG onboard

VOS

Tropical Moored Buoy System: TAO, TRITON,

PIRATA, RAMA

Scientific Campaign

Data:CTD

Others:Surface drifters

GlidersEquipped mamals

+Additional infoSSTin situ source (lati,loni,ti, zi )Sin situ source(lati,loni,ti, di, zi)Tin situ source(lati,loni,ti, di, zi)Rainin situ source(lati,loni,ti, di, zi)windin situ source(lati,loni,ti, di, zi)QCFlagsin situ source (lati,loni,ti, di,zi)

In situ SSS data Types

In situ SSS Database 1:Real Time Mode

In situ SSS Database 2:Delayed Time Mode:

Coriolis Data Assembly Center GOSUD Labos In situ SSS data Sources

Step 1: In Situ DataBases collection

• 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

SSSiARGO(lati,loni,ti,zi )

SSSiTSG(lati,loni,ti,zi )

+Additional info when availableSSTin situ source (lati,loni,ti, zi )Sin situ source(lati,loni,ti, di, zi)Tin situ source(lati,loni,ti, di, zi)Rainin situ source(lati,loni,ti, di, zi)windin situ source(lati,loni,ti, di, zi)QCFlagsin situ source (lati,loni,ti, di,zi)

Step 2: Apply QC flags

In situ SSS Database 1:Real Time Mode

In situ SSS Database 2:Delayed Time Mode:

Apply quality control flagsEx 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

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 resolutionFiltered SSS data at spatial resolution comparable to SMOS pixel sizes ~40 km to 80 kms (steps of 10 kms)

TSG

Drifters

Filters specification !

Rain at AQFreshening / surface

splash

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 SSSd=1cm

ARGO SSSd=5m

Step 4: Vertically stratified SSS classification

Step 4: Vertically stratified SSS classification

Qualified In situ SSS data SSSin situ(lati,loni,ti, zi )

Rainy or Dry point ?Rain Rates for the last hours before ti at lati,loni

(satellite, GPCP,..?)Rainy Flag(Intense Rain very likely rainy, Possible rain, Uncertain,..)

Dry Point Flag

Wind speed for the last days before ti at lati,loni

(satellite, ECMWF,..?)

Mixed or stratified surface

Auxilliary data

Rain Rates Climatology

Mixed Layer Depth Climatology

StratificationFlag

Well mixed rainyStratified rainy

River Plume or open ocean ?

River Plume FlagTBD ?

Stratified In river plume

Open ocean Flag

Step 5: In Situ SSS-SMOS data co-localization

Configuration of Spatial Radius R=Δx of co-localizationSearch for DGG grid points around the in situ data within radius of researchÞList of DGG grid points DGGi=1,…,n

Configuration of co-localisation Time lag Δt

=> Determine all SMOS passes at time to intercepting DGGi=1,…,n within to±Δt

In situ SSS data SSSin situ(lati,loni,ti, zi )

+Additional in situ info when available

DB1 ARGO DB1 TSG DB1 MO DB1 CTD DB1 DRIFTER DB1 others Real time DatabasesDB2 ARGO DB2 TSG DB2 MO DB2 CTD DB2 DRIFTER DB2 others Delayed 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..

SSSSMOS,SSSin situ, aux geophys, Tb L1C, L2 flags..

Match Up Databases 2-Delayed TimeARGO, TSG, etc..

Classif Rain stratif, Plume stratif, Open ocean well mixed..

SSSSMOS,SSSin situ, aux geophys, Tb L1C,L2 flags..

Rain & wind Auxilliary

Geophys Data

Conf file 1

Conf file 2Radius, Δt

SMOS ½ orbits L1C & L2

to,lat(to),lon(to) on DGG

MatchUp Databases 1-Real Time-DBs ARGO, TSG,etc..

Classif: Rain stratif, Plume stratif, Open ocean well mixed..

SSSSMOS,SSSin situ, L1 & L2 aux data & flags, Tb L1C,..

Match Up Databases 2-Delayed TimeARGO, TSG, etc..

Classif Rain stratif, Plume stratif, Open ocean well mixed..

SSSSMOS,SSSin situ, L1 & L2 aux data & flags, Tb L1C,..

Step 6: Co-localisation with other Geophysical Datasets

Other Wind & Waves

SSTRain

Altimetry Other

Data (Aquarius, SMAP)…

Augmented Match Up Databases 2-Delayed Time-ARGO, TSG, etc..

Classif Rain stratif, Plume stratif, Open ocean well mixed..SSSSMOS,SSSin 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..

SSSSMOS,SSSin situ, L1 & L2 aux data & flags, Tb L1C,..+

Other Wind & Waves,SST,Rain, Altimetry, Color …

AugmentedMDB1

Real Time

AugmentedMDB2

Delayed Time

AdditionalQualification

Tools:OA in situ

…

L1/L2 StatisticsBulletin…

L3 StatisticsBulletin…

L1/L2 StatisticsBulletin…

SMOS

In situ OI data(ISAS)

SMOS-ISAS

Additional Qualification Tools at L3:OA in situ

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