A-TSCV – ESA project
Assimilation of Total Surface Current Velocity Measurements (A-TSCV)
Total surface current velocity (TSCV) is an important variable for users of operational ocean forecasts and coupled ocean-atmosphere forecasts. In the next few years we will see a number of new satellite mission concepts being studied which will address the need for surface waves and TSCV data from satellites. These include ESA’s EE-9 SKIM and EE-10 Harmony concepts, as well as NASA’s WaCM concept. The aim of this project is to develop requirements from the international ocean data assimilation and forecasting community for such missions. The OceanPredict Science Team (the follow on to the GODAE OceanView Science Team) provides a forum for setting out such requirements through its Observing System Evaluation Task Team (OSEval-TT), and its Data Assimilation Task Team (DA-TT) provides a forum for collaborating on the development of methods to assimilate new and upcoming data such as satellite TCSV measurements.
Our understanding of the overarching objectives of the project is the following:
- Assess, implement and test methods for assimilating TSCV measurements in Numerical Ocean Prediction (NOP) data assimilation systems.
- Design, implement and perform an Observing System Simulation Experiment (OSSE) to test the impact of satellite derived TSCV data on ocean data assimilation and forecasting systems.
- Assess the results of the OSSE with a focus on areas of interest including the Equatorial Atlantic, the Gulf Stream, the Marginal Sea Ice Zone, and the Mediterranean Sea.
- Based on the results of the OSSE, refine the requirements for TSCV measurements and provide feedback to the operational forecasting and research communities through various routes including an Observation Impact Statement Report, peer-reviewed journal articles, a website, and the organisation of an international workshop.
This project is sponsored by the European Space Agency (ESA) through ESA Express Procurement Plus – EXPRO+ and combines the efforts of the OceanPredict Data Assimilation Task Team (DA-TT) and the Observing System Evaluation Task Team (OSEval-TT) with other European partner.
Measurements of the powerful, complex and highly variable ocean surface currents and surface waves are fundamental to our understanding of ocean circulation and air—sea interaction. This is because they influence the Earth system at time scales from wind waves, weather through to climate (GCOS, 2015). The motivation for better knowledge and understanding of ocean surface currents has its foundation in traditional maritime activities serving society at all levels including: shipping, maritime safety, marine operations, increasing maritime activities with sea-ice, fisheries, renewable energy, pollution events, environmental management, resource exploitation, ports and harbour operations, recreation, numerical weather prediction, ocean forecasting, and climate monitoring, amongst others. The actual velocity of a water parcel in contact with the atmosphere at any given location and time (i.e. the surface ocean current) is called the Total Surface Current Velocity (TSCV). TSCV is intimately related to waves (and thus wind stress) that through the wave dispersion relationship, reveal the fingerprint of TSCV.
A fundamental interface of the Earth system is between the ocean and the atmosphere through which all exchange of heat, momentum, gas and mass must be accomplished. On either side of this interface boundary layers are present, in which friction and mixing processes dominate where motion is not primarily horizontal, but also involves vertical exchange. In the ocean, this boundary layer typically has a dimension of the order of 10 m, characterised by a vertically homogeneous slab that moves horizontally (Price et al., 1986). However, the actively mixed layer is variable due to diurnal and seasonal stratification (Fig. 2.11), freshwater from rain and land run-off, and can be shallower than 1 m. The TSCV is characterised by the co-existence of different flow patterns and phenomena that have a physical expression on the sea surface. These expressions are embedded in the surface roughness of the ocean surface that can be measured.
The TSCV, denoted as vector U, has a markedly different impact on long-term drift behaviour compared to its usually defined components, the geostrophic current Ug, derived from altimetry, the mean wind-driven current UEk and the extremely important wave-induced Stokes drift, US (OOPC 2017). The long-term average velocity of particles at the ocean surface is well described by the sum of the three terms, U≈Ug+UEk+US. Each of the three terms plays an important and specific role. In the presence of waves, it is useful to separate the TSCV in a quasi-Eulerian current (as measured by a fixed current-meter located below the wave troughs) and the Stokes drift, the average motion due to waves alone (Jenkins, 1989). The vertical profile of the quasi-Eulerian current, its magnitude and surface angle, will depart from the simplified theory of Ekman (1905). In particular, the surface angle can range between 45 and 80°, depending on wind speed and stratification. As a result, the drift of floating material at the surface has very different patterns when one considers only geostrophic currents, with very weak convergence, Ekman currents, Stokes drift or the combination of the three (Fig. 1).
Figure 1. Numerical simulation of the distributions of surface particles (for example representing marine plastic debris) after 10 years, starting from a uniform distribution. The four panels show the effect of three components of the surface current combined, or their effect taken separately. (Adapted from Onink et al., 2019)
The Stokes drift has a direct contribution to the TSCV, but also a very strong influence on the other component by influencing mixing (Ardhuin and Jenkins 2006, Belcher et al., 2012). It is the ageostrophic motions (i.e. those not in balance with the pressure gradients), in this case UEk+US, that are important for surface convergence, and thus the aggregation of floating material such as plastic litter, but also define cross-shelf exchanges, including the export of carbon from land to the deep ocean. Owing to the properties of the mixing layer, the TSCV may be representative of a layer that varies in thickness between 0.10 m and tens of meters depending on stratification, and it has been very difficult to measure routinely. Stratification of the ocean refers to its density separation into layers owing to the dependence of the density on temperature, salinity and pressure. Stratification can lead to effective de-coupling of the upper-ocean water from that at depth (e.g. during strong diurnal heating or shallow halocline
conditions leading to a ‘slippery’ shallow layer where water at the surface can flow freely over that at depth (see Kudryavtsev and Soloviev, 1990). Only the measurements in the top few metres of the ocean, such as provided by un-drogued and shallow-drogued drifters (e.g. Lumpkin et al., 2017) that follow the surface-water motion (Novelli et al., 2017) or coastal HF-radars, can be representative of the surface mixed layer in most conditions, providing an estimate of the TSCV.
Alternative measurement techniques, at a fixed position or with drifting instruments drogued below the depth where the wave-induced Stokes drift US becomes negligible, measure the Eulerian mean flow UE, i.e. the total flow U-US (Jenkins, 1989, Niiler et al., 1995). By measuring the sea-level gradients, altimeter measurements miss the difference between the geostrophic current that often dominates below the mixed layer: the difference between the geostrophic current that often dominates below the mixed layer and the TSCV is 25 cm s-1 in the daytime.
The TSCV can be formally defined as the Lagrangian mean velocity at the sea surface (Andrews and McIntyre, 1978): the TSCV is the actual velocity of water parcels that are right at the ocean surface in contact with the atmosphere. The depth of the surface layer representative of the TSCV in the upper ocean depends on its vertical stratification. It includes a multiscale continuum of variability across all space and time scales governed by quasi-random forcing perturbations and nonlinear interactions at a given time and location. Motion at the ocean surface is the result of a superposition of different forces acting on the same ocean surface and those connected to the deeper ocean layers. Only in recent years have numerical ocean models started to include the necessary large range of space and time scales to represent these and the reader is referred to https://bit.ly/2XsokAe for one example realisation (D. Menemenlis personal communication, see Torres et al., 2018 for analyses). Thus, the processes resolved today in state-of-the-art models are only part of the full range of motions that contribute to the upper-ocean velocity field (e.g. Fox-Kemper et al., 2019).
Today, circulation models are capable of simulating ocean currents in response to atmospheric wind, tides, and buoyancy forcing. A question remains: How well do such simulations represent reality?
Their full validity in dynamical regimes readily accessible through potential future satellites missions (e.g. EE-9 candidate mission SKIM, EE-10 candidate mission Harmony), has never been tested except at a few locations (e.g. Scott et al., 2010) or using the sparse global Surface Velocity Program drifter array (Elipot et al., 2016). A synthesis of the Copernicus Marine Environmental Monitoring System (CMEMS) model performance on surface current was made by Rémy et al. (2019). For the mean error, the modelled zonal current is typically within 5 cm s-1 of the SVP drifter data, but up to 40 cm s-1 along the equator for the western half of the Pacific. Random errors typically lead to 60 km distances of three-day drift end-points, corresponding to a constant 23 cm s-1 error over 3 days.
Providing high-resolution maps of the surface current will reveal details that will help test theories and models, involving especially the wind-driven currents and wave-driven drift and mixing. On the other hand, model results will also be essential for interpreting such measurements that are being sampled irregularly for some of the relevant time scales, with ‘entangled’ contributions of rapid physical processes in snapshots measured at each satellite pass. Artificial intelligence or machine learning can be used to identify patterns and statistical descriptions embedded in datasets with applications to Earth-system science that will certainly emerge in unforeseen areas. Essential aspects here involve: response of the upper ocean to wind forcing, the vertical structure of resulting flow fields, the dissipation of wind energy, resulting mixing and transports and their convergences.
Of particular importance is the revolution offered by fully coupled ocean–atmosphere model systems from climate to weather forecasting time scales (e.g. Williams et al., 2019). It is the TSCV and wave spectrum that are most relevant to such systems because wind–wave–current interaction governs, to first order, all exchanges of heat, gas, mass and momentum between the atmosphere and the ocean. A particular new aspect of modelling involve Stokes drifts and the impact on momentum and matter transports (Belcher et al., 2012). While global NOP traditionally were not coupled to surface-wave models, great efforts have been made recently towards a consistent ocean–wave–atmosphere coupling (Harris 2018, Lemarié et al., 2015, 2019, Beljaars et al., 2017), also including sea ice (Boutin et al., 2019). Respective parameterisations show success with respect to improving tracer fields and transport characteristics. However, only very limited tests could be done owing to the lack of relevant observations. For the first time in the history of oceanography and ocean modelling, missions such as SKIM/Harmony could supply these observations and thus will allow for further improvement of existing parameterisation. This is expected to lead to a new modelling framework for ocean and coupled climate models.
Models are usually used alone or in combination with observations to study ocean or environmental processes and mechanisms as well as to predict changes. However, they can also be improved by combining them with the observations in a formal sense through data assimilation. Today, significant incremental progress has been made in ocean forecasting (e.g. Schiller et al., 2015) with the inclusion of tides, improved surface forcing/surface fields and waves/current interactions and many models stand ready to assimilate wave-current data such as that from SKIM/Harmony (e.g. Tonani et al., 2015, Chassignet and Sandery, 2013, Villas Bôas et al., 2019). Satellite data are being assimilated on a regular basis, including sea surface temperature, ocean colour and, particularly, altimetry – providing a dynamical pressure boundary condition. SKIM/Harmony data would be entirely complementary by providing a surface layer horizontal velocity field. Impacts have to be determined, though, as this type of data were never assimilated before. To assimilate Stokes drift data requires further developments before the full potential can be inferred. We envision that along with the next generation of ocean models containing active Stokes drift modules, also the assimilation capabilities of these models will be extended SKIM/Harmony data will be an essential driver and benefit to such an evolution.
Given the acute responsibility of teams working in extremely challenging situations such as oil, chemical spill or harmful algal bloom response, marine search and rescue activities (where TSCV estimates are a critical input for success) access to regular repeat-coverage TSCV is essential. Figure 2 shows the progress envisaged 30 years ago in climate models for integrating a dedicated ocean-wave model (Hasselmann, 1990), which is now a reality for some of the climate models used in the IPCC AR6.
Figure 2. Left climate model integration over the past 30 years to (right) the development of operational integrated ocean modelling system (CMEMS). The steady increase in the resolution of models requires a strong dynamic coupling of ocean and atmosphere, via the TSCV for the momentum and mechanical energy equations. (After K. Hasselmann, University of Hamburg, and CMEMS)
The same type of effort, after combining biogeochemistry and sea ice with ocean circulation, is taking place in the Copernicus Marine Environment Monitoring Service. In that context, given the higher resolution (CMEMS will operate a 3-km global ocean model by 2025), a proper representation of surface layer processes is much more critical (e.g. Renault et al., 2017, 2019). Ocean—atmosphere coupling, including TSCV, is one of the major research and development efforts both for CMEMS and ECMWF because it is critical for both the oceanic and atmospheric dynamics. Data assimilation for coupled systems is one particular area of intense research, and TSCV data, when available, would be a key variable to control the state of the coupled model. New measurements of TSCV SKIM/Harmony will certainly accelerate and contribute to these endeavours in a significant capacity.
Satellite TSCV measurements are not yet available to the ocean modelling community since no satellite system has been implemented. In the next few years the NASA/CNES SWOT mission will be launched and the EE-9 SKIM, EE-10 Harmony and NASA WaCM concepts are being studied in detail. However, The technical challenges of assimilating satellite TSCV and the challenging task of assessing the quality of satellite TSCV measurements using limited in situ reference data have not been developed to a satisfactory level. Furthermore, assessing the impact of the assimilation using objective tools and reporting requires significant effort.
The GODAE OceanView Science Team (GOVST) convened the Observing System Evaluation Task Team (OS-Eval TT) to evaluate the impact of different measurement systems. This is achieved by designing, implementing and reporting the results of specific Observing System Experiments (OSE). Each OSE produces an Observation Impact Statement Report (OISR) which describes in detail the end-to-end process of the OSE and the results obtained. OISR were proposed to the OS-Eval TT by ESA at the 3rd GOVST meeting held at ESA HQ, Paris November 2011. Through dedicated OSE experiments, GOVST is able to inform Space Agencies on the utility and performance of satellite data products and formulate specific requirements for ocean observations on the basis of improved understanding of data utility.
This A-TSCV study is focused on the design, implementation and reporting of an Observing System Evaluation of satellite Doppler-derived waves and TSCV using synthetic waves and TSCV measurements (e.g. from the SKIM L2 SKIMulator system). The primary output will be a GOV-ST Observation Impact Statement Report focused on satellite waves and TSCV, journal publications and a workshop dedicated to the findings and approach taken by the study team. In addition, it is anticipated that the study will include the development of a better understanding of suitable observation operators required by the assimilation system(s) and a more complete understanding of how to utilise Doppler/Along-Track Interferometry (ATI) derived waves and TSCV in ocean modelling systems.
Against this background, the aim of the A-TSCV study is:
To demonstrate the potential impact of Doppler/ATI-derived waves and TSCV measurements using coupled-ocean-atmosphere data assimilation systems.
The Objectives for the A-TSCV study are:
OBJ-1: Critically review different research and operational NOP (Numerical Ocean Prediction) data assimilation systems and determine an optimal approach to assimilation of satellite waves and TSCV measurements.
OBJ-2: Design and document an Observing System Experiment (OSE) to test the impact of satellite Doppler/ATI derived waves and TSCV following OceanPredict community consensus best practices.
OBJ-3: Design, implement and test at least two different observation operators using research NOP data assimilation configurations.
OBJ-4: Implement the OSE and produce a complete set of metrics (based on OceanPredict best practice) to evaluate the impact of satellite Doppler/ATI derived waves and TSCV.
OBJ-5: Refine the requirements for satellite Doppler/ATI derived waves and TSCV measurements through OSE experiments.
OBJ-6: Prepare an Observation Impact Statement Report together with the OceanPredict Task Team OS-Eval TT for future satellite Doppler/ATI derived waves and TSCV.
OBJ-7: Create a database of all data (input and output) relating to the OSE (including model outputs, satellite and in situ data etc.) that is publicly available for further use in other OSE.
OBJ-8: Prepare and submit at least five peer-reviewed journal articles based on the work of A-TSCV.
OBJ-9: In collaboration with ESA, organise and implement an “International Ocean Currents from Space” meeting in the second year of the study.
OBJ-10: Promote the A-TSCV study outcomes at scientific and other international meetings and via web/social media communication tools.
The consortium is composed of four companies: Met Office, Mercator Océan International, OceanDataLab and OceanNext. The Met Office, the prime contractor and sole interface to ESA, will be responsible for WP1, WP4 and WP6. Mercator Océan International (sub-contractor) will be responsible for WP2 and WP5. OceanDataLab (sub-contractor) will be responsible for WP3. OceanNext (sub-contractor) will also be involved in WP3. In addition to the main consortium, we also include two sets of scientific advisors from Ifremer and Thales Alenia Space who will not be funded in the project.
Dr. Matt Martin (M) is the project manager. He is a Met Office Science Fellow and the manager of the Marine Data Assimilation group within the Ocean Forecasting R&D team at the Met Office. He is co-chair of the Data Assimilation Task Team in OceanPredict.
Dr. Andrew Saulter (M) will contribute to WPs 2, 3 and 4. He is the manager of the Met Office Surge, Waves and Met Ocean Projects team. He has developed the Met Office’s global and regional wave and storm surge forecasting systems for many years and is a member of the CMEMS waves working group.
Dr. Jennifer Waters (F) is assigned WP leader for WP4. She is a senior scientist in the Marine Data Assimilation Group within the Ocean Forecasting R&D team at the Met Office. She has developed the Met Office’s global ocean data assimilation capabilities over a number of years, prior to which she completed a PhD in the assimilation of wave data from HF radar.
Dr. Robert King (M) is to work on WP4.2. He is a senior scientist in the Marine Data Assimilation group within the Ocean Forecasting R&D team at the Met Office. He has been involved in various international projects focussed on assessing the impact of observations in operational ocean-forecasting systems, including E-AIMS, ERA-Clim2, AtlantOS and ESA SMOS-NINO15.
Kirsten Wilmer-Becker (F) is assigned WP leader for WP1.2. She is the OceanPredict Programme Office Coordinator, responsible for coordinating OceanPredict communication and activities, organising OceanPredict events, developing and maintaining the OceanPredict website and managing the OceanPredict budget.
Mercator Océan international
Dr. Stéphane Law Shune (M) is working in the “Forecasting and analysis systems evolution” team of Mercator Ocean on several projects related to waves. Stéphane has a B.Sc. in Applied Physics, a M.Sc. in Meteorology/Oceanography and a thesis in Physical Oceanography. His area of expertise covers (1) surface layer dynamics and ocean-wave interactions in numerical models, (2) implementation and validation of wave reanalysis and (3) surface current assessment for Lagrangian dynamics.
Dr. Jean-Michel Lellouche (M) is at the head of the “Forecasting and analysis systems evolution” team, within the Operational Oceanography department. He is responsible of the preparation of the future real time forecasting systems. He has a perfect knowledge of the systems configuration and often run short OSEs to test the operational systems.
Dr. Elisabeth Rémy (F) is at the head of the “Observations for ocean analysis and forecasting systems” team within the Department. She has a long experience in ocean modelling, data assimilation and more recently observation impact studies. She coordinates activities on the benefit of future observation deployment for the Mercator Océan operational ocean analysis and forecasts. She is currently involved in the EuroSea H2020 project, SWOT science team and GODAE OSE/OSSE task team.
Dr Isabelle Mirouze (F) is a consultant for Mercator Océan International and will mainly contribute to WP4. She has a long experience in ocean data assimilation on different systems. She had taken part in various projects working on error covariance estimate and modelling, observation operators, OSSEs, and atmosphere-ocean coupled data assimilation.
Dr. Lucile Gaultier (F) is a Research Engineer, working on improving the reconstruction of ocean surface current using various remote sensing data as well as on the use of the synergy of data and Lagrangian diagnostics to improve our understanding of the ocean surface dynamics. She is also involved in SKIM proposal to simulate skim-like data and evaluate the ocean surface current products performance.
Dr. Fabrice Collard (M) is president of OceanDataLab IFREMER spinoff at CERSAT, working on ocean remote sensing multi-sensor synergy methods and tools with a special focus on ocean surface wind, wave and currents. He has a long experience in ocean surface current measurements from his post-poc on HF radars to the development of SAR Doppler techniques (ENVISAT and now Sentinel-1) but also drifters development/deployments and analysis (DRIFT4SKIM). He participated in several projects related to upper ocean circulation such as ESA Globcurrent project, member of ESA SKIM Mission advisory group. ESA radial velocity assessment from Sentinel-1 Doppler and platform Gyros, EU TOPVOYS for ship routing optimisation.
Dr. Clément Ubelmann (M) is a research scientist at Ocean Next, Grenoble, France. He holds a PhD in Oceanography with a strong expertise on data assimilation. His main interests are the inversion of Ocean surface satellite data to reconstruct mesoscale and sub-mesoscale fields, involving data assimilation or data-driven techniques. He is strongly involved in the SWOT mission to develop new algorithms for calibration and mapping the future high-resolution altimetry. Firstly as a postdoc and research scientist at JPL (2009-2011) and after at CLS (2011-2019). He is going to join Ocean Next (march 2020). During the recent years, he also focused on surface current reconstruction including high-frequency ageostrophic processes. He contributed to the SKIM-phase A , designing total surface current reconstruction algorithms combining Doppler and Altimetry.
Schedule and milestones
The proposed schedule and main milestones of the project are given below. The project will run for two years, divided into six phases:
- Task 1: Outreach, Communication and Promotion
- Task 2: OSE design
- Task 3: Data preparation and collection
- Task 4: OSE implementation
- Task 5: OSE output processing, validation and analysis
- Task 6: International workshop and final reporting
The A-TSCV web portal will be implemented during the first 3 months of the project. The A-TSCV database will be ready at T0+12. WPs 2, 3, 4 and 5 are mainly sequential with some overlap allowing the next task to start before the end of the current task. Task 6 will start at T0+18 and mainly consists in the organisation of the international workshop.
|KO||Q4 2020||WebEx||Meeting||Kick-off Meeting||All|
|PM-1||KO+3||WebEx (Covid)||Milestone||Project Management Meeting||All|
|PM-2||KO+6||WebEx||Milestone||Project Management Meeting||All|
|PM-3||KO+9||Exeter||Milestone||Project Management Meeting||All|
|PM-4||KO+12||WebEx||Milestone||Project Management Meeting||All|
|PM-5||KO+15||Plouzane||Milestone||Project Management Meeting||All|
|PM-6||KO+18||ESTEC||Milestone||Project Management Meeting||All|
|PM-7||KO+21||Toulouse||Milestone||Project Management Meeting||All|
Work package descriptions
WP1: Project management & communication (Met Office)
- Project management (Met Office)
- Communication (Met Office)
WP2: OSE design (MOI)
- TSCV data assimilation design (Met Office)
- OSE design (MOI)
WP3: Data collection (OceanDataLab)
- SKIM data generation (OceanDataLab)
- Standard observations generation (OceanDataLab)
WP4: OSE implementation (Met Office)
- Met Office OSE implementation (Met Office)
- MOI OSE implementation (MOI)
WP5: Data processing and analysis (MOi)
- Writing papers (MOI)
- OISR (Met Office)
WP6: International workshop and final reporting (Met Office)
- Final reporting (Met Office)
- International workshop (MOi)
|Ref||Short name||Title||Date Due||Resp||WP|
|D-10||WWW||A-TSCV web portal updated during the contract and remaining live for at least 2 years after the end of the project.||KO+2 (updated every month)||MetO||WP1|
|D-20||FIG||High quality graphics||KO+24 (final meeting, reviewed at each progress meeting)||MetO||WP1|
|D-30||WEBS||Web stories for the A-TSCV website||KO+6,
|D-40||TR-1||Technical Report (TR-1): “Observing System Experiment (OSE) design to test the impact of simulated satellite TSCV”||Draft at KO+9 Final version at Final Presentation||MOi||WP2|
|D-50||TR-2||Technical Report (TR-2): “Analysis and performance metrics for the A-TSCV Observing System Experiment (OSE)”||Draft at KO+9 Final version at Final Presentation||MOi||WP2|
|D-60||DB||A-TSCV database (DB) of all data products used in the project||Initial version by KO+6, final by contract closure.||ODL||WP3|
|D-70||TR-3||Technical Report (TR-3) “SKIMulator A-TSCV Simulation System Description, Configuration and Simulations”||KO+6||ODL||WP3|
|D-80||TR-4||Technical Report (TR-4)) “A-TSCV baseline product data set description, format and data access interface”||Draft at KO+9 and final version at Final Presentation||ODL||WP3|
|D-90||TR-5||Technical Report (TR-5) “Implementation and validation of the A-TSCV OSE to test the impact of simulated satellite TSCV following OceanPredict community consensus best practices”.||KO+18||MetO||WP4|
|D-100||OSE-DATA||Output from all OSEs for further analysis||V1 at KO+12 and final version at KO+18||MetO||WP4|
|D-110||TR-6||Technical Report (TR-6): “Observation impact statement report for simulated satellite TSCV”||KO+21||MetO||WP5|
|D-120||TR-7||Technical Report (TR-7): “Numerical ocean prediction requirements for satellite TSCV products”||KO+21||MOI||WP5|
|D-130||PAPER-1||Submitted A-TSCV journal article #1||KO+23||MetO||WP5|
|D-140||PAPER-2||Submitted A-TSCV journal article #2||KO+23||MOI||WP5|
|D-190||PROC||A-TSCV workshop proceedings||KO+23||MOI||WP6|
|D-200||SR||A-TSCV scientific roadmap||KO+23||MetO||WP6|
|D-210||FR||A-TSCV Final Report||KO+23||MetO||WP6|
|D-220||TDP||A-TSCV Technical Data Package||KO+23||MetO||WP6|
The project activities are described through web-stories which are published at least twice per year with input from project partners. The web-stories are linked to a collection of high-quality graphics produced by the project.
Idealised observation experiments and estimation of velocity forecast error covariances
Graphics produced during the project and linked to the web-stories can be downloaded from this page.
Graphics from WS-1: “Idealised observation experiments and estimation of velocity forecast error covariances”.
Click on image to view enlargement and/or download the file.
Surface increments for speed, temperature, salinity and SSH. From MOI (top) and FOAM (bottom).
Zonally averaged horizontal forecast error correlation length scales for unbalanced surface U and V. These are estimated by fitting a Gaussian function with two correlation scales to the NMC error covariance data. Black and blue lines are length scales in the x-direction and y-direction, respectively. Dashed and solid lines are the long and short scale, respectively.
U vertical forecast error correlations with the surface. Plot (a) shows the global mean correlations plotted against a normalising depth. For the green, blue and black line the normalising quantity is the global mean MldRho, MldZ and Ekman depth respectively. The horizontal red line shows where the normalised depth is 1 and the vertical red line is the value of a Gaussian function when the depth variable equals the correlation length scale. The shaded region shows the standard deviation of the error correlations. Plot (b) shows a latitudinal section of the zonal mean vertical correlations with the surface. The green, blue and black lines are the zonal mean MldRho, MldZ and Ekman depth, respectively.
A-TSCV Twitter page
Reference Documents Reports Presentations Publications Data
RD 1 Ardhuin, F. and A. D. Jenkins (2006). On the interaction of surface waves and upper ocean turbulence, J. Phys. Oceanogr., 36, 3, 551–557. doi:10.1175/jpo2862.1
RD 2 Ardhuin, F., Aksenov, Y., Benetazzo, A., Bertino, L., Brandt, P., Caubet, E., et al. (2018). Measuring currents, ice drift, and waves from space: the sea surface kinematics multiscale monitoring (SKIM) concept. Ocean Sci. 14, 337–354. doi: 10.5194/os-14-337-2018
RD 3 Ardhuin F, Brandt P, Gaultier L, Donlon C, Battaglia A, Boy F, Casal T, Chapron B, Collard F, Cravatte S, Delouis J-M, De Witte E, Dibarboure G, Engen G, Johnsen H, Lique C, Lopez-Dekker P, Maes C, Martin A, Marié L, Menemenlis D, Nouguier F, Peureux C, Rampal P, Ressler G, Rio M-H, Rommen B, Shutler JD, Suess M, Tsamados M, Ubelmann C, van Sebille E, van den Oever M and Stammer D (2019) SKIM, a Candidate Satellite Mission Exploring Global Ocean Currents and Waves. Front. Mar. Sci. 6:209. doi: 10.3389/fmars.2019.00209
RD 4 Belcher, S. E., Grant, A. L. M., Hanley, K. E., Fox-Kemper, B., Roekel, L. V., Sullivan, P. P., Large, W. G., Brown, A., Hines, A., Calvert, D., Rutgersson, A., Pettersson, H., Bidlot, J.-R., Janssen, P. A. E. M., and Polton, J. A. (2012). A global perspective on Langmuir turbulence in the ocean surface boundary layer, Geophys. Res. Lett., 39, p. L18605. doi:10.1029/2012GL052932
RD 5 Blockley, E. W., Martin, M. J., and Hyder, P.: Validation of FOAM near-surface ocean current forecasts using Lagrangian drifting buoys, Ocean Sci., 8, 551–565, https://doi.org/10.5194/os-8-551-2012, 2012.
RD 6 Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613-2638, doi:10.5194/gmd-7- 2613-2014, 2014.
RD 7 Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F., (2019). Toward a coupled model to investigate wave-sea ice interactions in the Arctic marginal ice zone. The Cryosphere Discuss. doi.org/10.5194/tc-2019-92
RD 8 Breivik, Ø., Mogensen, K., Bidlot, J.‐R., Balmaseda, M. A., and Janssen, P. A. E. M. ( 2015), Surface wave effects in the NEMO ocean model: Forced and coupled experiments, J. Geophys. Res. Oceans, 120, 2973– 2992, doi:10.1002/2014JC010565.
RD 9 Carrier, M.J., H. Ngodock, S. Smith, G. Jacobs, P. Muscarella, T. Ozgokmen, B. Haus, and B. Lipphardt, 2014: Impact of Assimilating Ocean Velocity Observations Inferred from Lagrangian Drifter Data Using the NCOM-4DVAR. Mon. Wea. Rev., 142, 1509–1524,
RD 10 Chassignet E, Sandery P. 2013. Joint GODAE OceanView –WGNE workshop on Short- to Medium-range coupled prediction for the atmosphere-wave-sea-ice-ocean: Status, needs and challenges. Modeling Whitepaper. GOV publication.
RD 11 Couvelard, X., Lemarié, F., Samson, G., Redelsperger, J.-L., Ardhuin, F., Benshila, R., and Madec, G.: Development of a 2-way coupled ocean-wave model: assessment on a global NEMO(v3.6)-WW3(v6.02) coupled configuration, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-189, in review, 2019.
RD 12 Davidson F, Alvera-Azcárate A, Barth A, Brassington GB, Chassignet EP, Clementi E, De Mey-Frémaux P, Divakaran P, Harris C, Hernandez F, Hogan P, Hole LR, Holt J, Liu G, Lu Y, Lorente P, Maksymczuk J, Martin M, Mehra A, Melsom A, Mo H, Moore A, Oddo P, Pascual A, Pequignet A-C , Kourafalou V, Ryan A, Siddorn J, Smith G, Spindler D, Spindler T, Stanev EV, Staneva J, Storto A, Tanajura C, Vinayachandran PN, Wan L, Wang H, Zhang Y, Zhu X and Zu Z (2019) Synergies in Operational Oceanography: The Intrinsic Need for Sustained Ocean Observations. Front. Mar. Sci. 6:450. doi: 10.3389/fmars.2019.00450
RD 13 Earth Explorer 9 Candidate Mission SKIM –Report for Mission Selection, ESA-EOPSM-SKIM-RP-3550, v1.0, 21/06/2019.
Ekman, V. W. (1905) On the influence of earth’s rotation on ocean currents. Arkiv for Matematik, Astronomioch Fysik, 2(10).
RD 14 Ekman, V. W. (1905) On the influence of earth’s rotation on ocean currents. Arkiv for Matematik, Astronomioch Fysik, 2(10).
RD 15 Elipot, S., R. Lumpkin, R. C. Perez, J. M. Lilly, J. J. Early, and A. M. Sykulski (2016). A global surface drifter data set at hourly resolution, J. Geophys. Res. Oceans, 121, 2937–2966. doi:10.1002/2016JC011716
RD 16 Gaultier, L., (2019), SKIMulator source code
RD 17 Gaultier, L., C. Ubelmann, and L. Fu, 2016: The Challenge of Using Future SWOT Data for Oceanic Field Reconstruction. J. Atmos. Oceanic Technol., 33, 119–126
RD 18 Gasparin F, Guinehut S, Mao C, Mirouze I, Rémy E, King RR, Hamon M, Reid R, Storto A, Le Traon P-Y, Martin MJ and Masina S (2019) Requirements for an Integrated in situ Atlantic Ocean Observing System From Coordinated Observing System Simulation Experiments. Front. Mar. Sci. 6:83. doi: 10.3389/fmars.2019.00083
RD 19 GCOS (2016). The Global Observing System for Climate: Implementation Needs, GCOS200:
RD 20 Harris, C. (2018). Coupled Atmosphere-Ocean Modelling, in New Frontiers in Operational Oceanography, Chassignet, E. P., A. Pascual, J. Tintoré, and J. Verron (Eds.). (2018). GODAE OceanView, 815pp. doi:10.17125/gov2018
RD 21 Hyder, P., D. Storkey, E. Blockley, C. Guiavarc’h, J. Siddorn, M. Martin and D. Lea, 2012. Assessing equatorial surface currents in the Forecast Ocean Assimilation Model (FOAM) Global and Indian Ocean models against observations, including the global tropical moored buoy array. J. of Operational Oceanography, 5, 25-39.
RD 22 Jenkins, A. D. (1989). The use of a wave prediction model for driving a near-surface current model, Deut. Hydrogr. Z., 42, 133–149.
RD 23 Le Traon, P.Y., Dibarboure, G., Jacobs, G., Martin, M., Remy, E., Schiller, A. Use of satellite altimetry for operational oceanography. In “Satellite Altimetry Over Oceans and Land Surfaces”. Stammer, D. (Ed.), Cazenave, A. (Ed.). (2018). Boca Raton: CRC Press
RD 24 Lea, D. J., Martin, M. J. and Oke, P. R. (2014), Demonstrating the complementarity of observations in an operational ocean forecasting system. Q.J.R. Meteorol. Soc., 140: 2037-2049. doi: 10.1002/qj.2281.
RD 25 Lea, D. J., I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow, 2015: Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model. Monthly Weather Review, 143, 4678–4694, doi: 10.1175/MWR-D-15-0174.1.
RD 26 Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° highresolution system, Ocean Sci., 14, 1093–1126, https://doi.org/10.5194/os-14-1093- 2018, 2018.
RD 27 Lumpkin, R., T. Özgökmen, & L. Centurioni (2017). Advances in the Application of Surface Drifters. Annual Review of Marine Science, 9(1), 59–81. doi:10.1146/annurevmarine-010816-060641
RD 28 Lumpkin, R. and Z. Garraffo, 2005: Evaluating the decomposition of tropical Atlantic drifter observations. J. Atmos. Ocean. Techn., 22 (9), 1403-1415, doi: 10.1175/JTECH1793.1.
RD 29 Manucharyan, G. E, & Thompson, A. F. ( 2017). Submesoscale sea ice‐ocean interactions in marginal ice zones. Journal of Geophysical Research: Oceans, 122, 9455– 9475. https://doi.org/10.1002/2017JC012895
RD 30 Martin, M.J., E. Remy, B. Tranchant , R. R. King, E. Greiner, C. Donlon, 2020. Observation impact statement on satellite sea surface salinity data from two operational global ocean forecasting systems. Submitted to Journal of Operational Oceanography.
RD 31 Martin MJ, King RR, While J, Aguiar AB. Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system. Q J R Meteorol Soc 2019;145:705–726.
RD 32 Moore AM, Martin MJ, Akella S, Arango HG, Balmaseda M, Bertino L, Ciavatta S, Cornuelle B, Cummings J, Frolov S, Lermusiaux P, Oddo P, Oke PR, Storto A, Teruzzi A, Vidard A and Weaver AT (2019). Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean. Front. Mar. Sci. 6:90. doi: 10.3389/fmars.2019.00090
RD 33 Niiler, P. P., Sybrandy, A. S., Bi, K., Poulain, P. M., & Bitterman, D. (1995). Measurements of the water-following capability of holey-sock and TRISTAR drifters. Deep Sea Research Part I: Oceanographic Research Papers, 42(11-12), 1951–1964. doi:10.1016/0967-0637(95)00076-3
RD 34 Novelli, G., C.M. Guigand, C. Cousin, E.H. Ryan, N.J. Laxague, H. Dai, B.K. Haus, and T.M. Özgökmen, (2017), A Biodegradable Surface Drifter for Ocean Sampling on a Massive Scale. J. Atmos. Oceanic Technol., 34, 2509–2532, doi.org/10.1175/JTECH-D-17-0055.1
RD 35 OOPC (2017). Surface Currents Specification. Report v. 5.2,
RD 36 Penny, S.G., S. Akella, O. Alves, C. Bishop, M. Buehner, M. Chevallier, F. Counillon, C. Draper, S. Frolov, Y. Fujii, A. Karspeck, A. Kumar, P. Laloyaux, J.-F. Mahfouf, M. Martin, M. Peña, P. de Rosnay, A. Subramanian, R. Tardif, Y. Wang, X. Wu. 2017. Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges and Recommendations. WWRP 2017-3
RD 37 Price, J. F., Weller, R. A., and Schudlich, R. R. (1987). Wind-driven ocean currents and Ekman transport, Science, 238, 1534–1538.
RD 38 Rémy, E., et al. (2019). Evaluation of ocean surface current in the CMEMS global 1/12° system, Mercator Ocean, internal report for CNES.
RD 39 Renault, L., J. C. McWilliams, and S. Masson, 2017, Satellite Observations of Imprint of Oceanic Current on Wind Stress by Ocean—atmosphere Coupling, Scientific reports, 7, 17 747.
RD 40 Renault, L., P. Marchesiello, S. Masson, J. C. McWilliams (2019). Remarkable Control of Western Boundary Currents by Eddy Killing, a Mechanical Air‐Sea Coupling Process. Geophys. Res. Lett., in press. doi:10.1029/2018GL081211
RD 41 Rio, M.‐H., Mulet, S., and Picot, N. (2014), Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, 8918– 8925, doi:10.1002/2014GL061773.
RD 42 Rio, M.‐H. (2012), Use of altimeter and wind data to detect the anomalous loss of SVP‐type drifter’s drogue, J. Atmos. Oceanic Technol., 29, 1663– 1674, doi:10.1175/JTECH-D-12-00008.1
RD 43 Rocha, C. B., and Chereskin, T. K., and Gille, S.-T., and Menemenlis, D. (2016), Mesoscale to Submesoscale Wavenumber Spectra in Drake Passage, J. Phys. Oceanogr., 46 (2), 601–620, doi:10.1175/JPO-D-15-0087.1.
RD 44 SKIM Algorithm Theoretical Basis Document (ATBD), ESA/SKIM-PE/D-60, v1.0, 16 June 2019, available from ESA/ESTEC Keplerlaan 1, the Netherlands.
RD 45 SKIMulator User Manual,https://github.com/oceandatalab/skimulator
RD 46 Tonani, M., M. Balmaseda, L. Bertino, E. Blockley, G. Brassington, F. Davidson, Y. Drillet, P. Hogan, T. Kuragano, T. Lee, Av. Mehra, F. Paranathara, C. A.S. Tanajura and H. Wang, (2015), Status and future of global and regional ocean prediction systems,Journal of Operational Oceanography, 8:sup2, s201- s220. doi: 10.1080/1755876X.2015.1049892
RD 47 Tranchant, B., Remy, E., Greiner, E., and Legalloudec, O.: Data assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the Mercator Ocean operational system: focus on the El Niño 2015 event, Ocean Sci., 15, 543–563,
RD 48 Villas Bôas, A. B., Ardhuin, F., Ayet, A., Bourassa, M. A., Chapron, B., Brandt, P., Cornuelle, B. D., Farrar, J. T., Fewings, M. R.,, Fox-Kemper, B., Gille, S. T., Gommenginger, C., Heimbach, P., Hell, M., C., Li, Q., Mazloff, M., Merrifield, S. T., Mouche, A., Rio,, M. Rodriguez, E., Shutler, J. D., Subramanian, A. C., Terrill, E. J., Tsamados, M., Ubelmann, C. and van Sebille, E.: Integrated observations and modeling of winds, currents, and waves (2019) requirements and challenges for the next decade, Frontiers in Marine Science, In Press.
RD 49 Waters, J., Bell, M. J., Martin, M. J. and Lea, D. J. (2017), Reducing ocean model imbalances in the equatorial region caused by data assimilation. Q.J.R. Meteorol. Soc., 143: 195–208. doi:10.1002/qj.2912
RD 50 Weaver, A.T., Deltel, C., Machu, E., Ricci, S. and Daget, N. (2005), A multivariate balance operator for variational ocean data assimilation. Q.J.R. Meteorol. Soc., 131: 3605-3625. doi:10.1256/qj.05.119
RD 51 Williams, K. D., Copsey, D., Blockley, E. W., Bodas‐Salcedo, A., Calvert, D., Comer, R., … Xavier, P. K. ( 2017). The Met Office Global Coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. Journal of Advances in Modeling Earth Systems, 10, 357– 380. doi.org/10.1002/2017MS001115
RD 52 Yu, P., Kurapov, A. L., Egbert, G. D., Allen, J. S., and Kosro, P. M. (2012). Variational assimilation of HF radar surface currents in a coastal ocean model off Oregon. Ocean Model. 49-50, 86–104. doi: 10.1016/j.ocemod.2012.03.001
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