an overview of the l2os esl workplan nr & esls. a document as been prepared by l2os/esl for the...
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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