investigating the impact of assimilating satellite sss ...godae-data/oceanview/events/... · godae...

GODAE DA and OSEval TT, La Spezia, 12 October 2017

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Investigating the impact of assimilating satellite SSS during the

2015/2016 El-Niño

B. Tranchant, M. Martin, E. Remy, R. King, E. Greiner, K. Wilmer-Becker & C. Donlon

@smosnino15

https://www.godae-oceanview.org/projects/smos-nino15/

https://twitter.com/smosnino15


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Sea Surface SalinityThe salinity is an ocean variable playing a key role in the ocean variability.

Observing this Sea Surface Salinity (SSS) is crucial for monitoring, understanding and forecasting the ocean circulation, water cycle and climate changes.

The launch of ESA’s SMOS mission (2009-Now) as well as NASA’s Aquarius (2011-2015) and SMAP missions (2015-Now) provide for the first time satellite measurements of SSS.

Several studies already demonstrated the ability of SMOS and Aquarius missions to provide useful and new information on SSS.

BUT, these data are not widely used by the ocean modelling community and in particular by the ocean forecasting centers represented by the GODAE Ocean view Science Team (GOV-ST). Land contamination, RFI, latitudinal bias, galactic noise and so onTechnical challenges of assimilating SSS data and assessing the impact of these data in the forecasting systems.

The SSS products are still in development and constantly evolving toward an increased quality.


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Outline of the talk

• Objectives of the SMOS-Nino 2015

• Planned experiments

• First data assimilation results

• Summary and next steps


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Objectives of the SMOS-Nino2015 ESA project

• Design, implement and perform an OSE of the SSS with different operational ocean forecasting systems.

• To produce an Observing System Impact Statement on behalf of the GOV OSEval-TT

• Provide feedbacks to the operational forecasting community (GODAE Ocean View) • Observation Impact Statement Report, publications, workshop to promote and enhance the use the SSS SMOS products (https://www.godae-

oceanview.org/projects/smos-nino15/).

• To refine the requirements for SSS (Sea Surface Salinity) after the analysis of the errors associated with these experiments

• To analyse the onset and evolution of the 2015/16 El Niño event (use case)

https://www.godae-oceanview.org/projects/smos-nino15/


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Tools

A coordinated Observing System Experiment from two different global ocean forecasting systems: UK MetOffice system: global ¼° (ORCA) with a 3DVar – FGAT DA

(NEMOVAR) and a bias correction (NEMOVAR) Mercator Ocean: global ¼° (ORCA) with SAM2 DA and a 3DVar large

scale bias correction

Data sets: Daily/weekly products from: ESA’s SMOS Aquarius (V4.0) SMAP (V2.0)

SSS data assimilation between 40°S and 40°N. All other observations that are routinely assimilated in those systems are kept (SLA, SST and in-situ observations).


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Planned

experiments

Main Characteristics:

Data assimilated

Time Duration

REF Current networks without satellite

SSS.

Jan 2014- March 2016

SMOS Current network plus daily/weekly

SMOS satellite SSS observations

Jan 2015 - March 2016

AQUA/SMAP Current networks plus daily/weekly

SMAP/Aquarius satellite SSS

observations.

Jan 2015 - March 2016

ALL Current networks plus all SSS

satellite observations (using

datasets giving the best

improvement, if any).

Jan 2014- March 2016

Planned experiments at

Mercator Ocean and MetOfficePlanned


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Focus on the El Niño 2015 event

Hovmöller diagrams of salinity along the Equator in the Pacific

The freshening increases in the West from 2013 to 2015. The onset of the El-Niño 2015/16 event is evident in the SSS from both

systems.

Mercator

Ocean

MetOffice


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Experiments (Mercator Ocean system)

• REFERENCE simulation (January 2014 to March 2016) : Assimilation of SLA (alongtracks), satellite SST (OSTIA), in situ profiles (no salinity TAO/TRITON data)

• SMOS simulation (2015-March 2016): REFERENCE+SMOS CEC-LOCEAN v2-18km-25km, 4 days (debiased)

• AQUARIUS/SMAP (2015-March 2016): REFERENCE + Aquarius V4.0 (1°x1°) fromJPL, 7-day running mean (until May 2015) + SMAP V2.0 (0.25°x0.25°) from RSS, 8-day running mean (from June 2015)

The observation operator H produces the model counterpart of weekly mean SSS– No spatial filtering for SMOS and SMAP (0.25°x0.25°)– Spatial smoothing (shapiro filter) for Aquarius (1°x1°)

BIAS CORRECTION:

•Mercator Ocean is using a 3Dvar scheme for large scale bias correction of in-situ data and for SSS data observation errors of SSS from an adaptative method

•MetOffice is using the same bias correction than the SST bias correction (Donlon et al, 2010)


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SSS innovation vs in-situ innovation before bias corr.Monthly SSS innovation

Monthly in-situ SSS innovation


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SSS innovation vs in-situ innovation after bias corr.Monthly debiased SSS innovation

Monthly in-situ SSS innovation


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Data assimilation of debiased SSS: Observation errors for a DA cycle

SSS error for in-situ data SSS error for SMOS data diagnosed by the

bias and used in the DA scheme

SSS error specified by LOCEAN

for SMOS


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First results of the SMOS DA experiment (CLS/Mercator)

Forecast error statistics over the global domain (2015-March 2016)

Reduction of ~0.03 and ~0.07 pss when assimilating

<0.28pss

Ave

rag

eR

MS

REF SMOS

<0.21pss


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RMS of (Obs. - model fcst) from salinity insitu profile

averaged over the global domain (2015-March 2016)

REF SMOS DA

First results of the SMOS DA experiment (CLS/Mercator)


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Impact on in-situ SSS (5 m depth) in Niño areas

Time evolution of (Obs. - model fcst) averaged over the Nino 1+2,Nino3 and Nino4

domains (2015-March 2016)


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SSS and SST mean differences vs REFS

MO

SA

QU

A/

SM

AP


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TAO mooringsWithheld from the analysis


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Reduction of the SLA and Dyn Height increments

REF

SMOS


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Summary DA of SSS from SMOS seems to have a positive impact and the bias

correction is necessary• Reduction of 0.03 to 0.05 pss of the RMS of SSS innovation close to the

in-situ

DA of SSS from SMAP (rss product at 0.25°x0.25°): the RMS of SMAP innovation is too high:⇒ data are noisy: discussions with the data provider are ongoing.

The SST is significantly impacted

Misfits to insitu T/S profiles• S: unchanged for Aquarius/SMAP and slight improvement for SMOS at sea

surface• T: unchanged at sea surface, slightly degraded in sub-surfaceThe sub-surface T/S equilibrium has changed, may be we correct too much the sea surface (SST and SSS)?

SLA: slight improvement the mean SLA increment (2015) has been reduced in the tropical Pacific


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Continue the analysis of the simulations with, in addition to the classical O-A and O-B statistics, physically relevant diagnostics for this region:

• Maps of SSS and time series: from different SMOS products and the OSE estimates

-> Freshwater pool extension (34.8 or 35.0 isohaline)• Maps of SST and time series -> Warm water pool (29°C isotherm)• Subsurface T and S fields and changes in the OSEs:

-> Depth of the 20°C isotherm • Barrier layer thickness: it isolates the deeper layer from the atmospheric

influence-> Could lead to in ENSO event.

Comparison with MetOffice experiments: document the common features and differences between the OSEs with the different systems.

“The SMOS Nino2015 is a contribution to GODAE OV. If other groups wanted to contribute to the evidence put into the Observing System Impact Statement they are welcome.”

Next steps

Bruce Howe

ITU/WMO/IOC Joint Task ForceandUniversity of Hawaii at Manoa

Presented byElisabeth Remy, Mercator

T1.4-04

SMART SubSea Cable Systems

search: itu jtf www.itu.int/en/ ITU-T/climatechange/ task-force-sc/

[email protected]

ITU/WMO/UNESCO IOC Joint Task ForceScience Monitoring And Reliable Telecommunication (SMART)

SubSea Cable Systems

Chair

Bruce M. Howe , PhD

SMART Subsea Cables Sensing the Pulse of the Planet

JOINT DA-TT & OSEval-TT MeetingCMRE, La Spezia, Italy11-13 October 2017

SMART: Science Monitoring And Reliable Communications

• Telecom + science• Cable repeaters host sensors• Potential: global spanning, trans-ocean, 1

Gm, ~10,000 repeaters (~100 km) 10-20 year refresh cycle

• Initially: bottom pressure, temperature and acceleration; supplement later

SMART cables: first order addition to the ocean-earth observing system, with unique contributions that will strengthen and complement satellite and in-situ systems

Climate, OceansEarthquakes, Tsunamis – Global Array

SMART Subsea Cables - The basic idea

~20 m

Install routinely on new cablesDeploy by cable ship, no maintenance

Repeater

Sensor Module

John You, Nature, 2010 – Harnessing telecoms cables for science

Next steps – GODAE OceanView - GOV

• SMART Cables need GOV help for OSSEs (Observing System Simulation

Experiments)

• Ocean bottom temperature and pressure from repeaters along subsea cables,

starting with smaller systems, then transoceanic

• Anticipate pressure provides significant information

– N-S eqn; ρ, u, P; absolute, barotropic velocity, high frequency to climate

• Use “standard” metrics, Stand alone and relative to other GOOS components

• Results needed to motivate demo/pilot; in time to influence OceanObs’19

• Future sensors: inverted echosounder (depth averaged temperature) and cable

voltages (barotropic currents) – include now in simulations

• Howe will present SMART Cables and Deep Ocean Observing Strategy (DOOS)

at GOV annual meeting, 9 November 2017, Bergen


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Additional slides


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Data assimilation of debiased SSS: Salinity innovation for a DA cycle

Innovation of native SSS Innovation of debiased SSS

Innovation (Observation – model forecast(1-7 days)

) of SMOS SSS (ex : 20120912)

The bias is reduced but there are still some residual biases (North Pacific Gyre and

North Atlantic)

! We know that there are some problems with SSS data during fall 2012


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Bias correction for T/S profiles in MO operational system

• Mercator Ocean applies a general 3D temperature and salinity slowly-evolving large-scale bias correction.

• Use a 3DVAR:• Innovations are binned and averaged on a 1°x1° grid over the 1 previous

months.• T and S are treated separately (T and S biases are not necessarily

correlated).• Covariances are modeled by means of an anisotropic gaussian recursive filter.

• Bias correction of T, S and dynamic height:• Computed and interpolated on the model grid• Bias corrections are applied as tendencies in the model prognostic equation

with a 1-month time scale

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