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Joint CP-TT/DA-TT Workshop

Joint Task Team event
26 Oct 2026 – 30 Oct 2026, ECMWF, Reading, UK

Overview

The OceanPredict task teams on Coupled Predictions (CP-TT) and Data Assimilation (DA-TT) are organizing a

Joint CP-TT/DA-TT Workshop at ECMWF, UK

“Advances in coupled modeling, data assimilation, and predictions”

A 3 & 2 half-day event will bring together a wide range of scientists and experts in ocean coupled prediction and data assimilation.

 

—- Registration NOW OPEN —-

 

The Data Assimilation-Task Team (DA-TT) fosters the development and evaluation of data assimilation systems relevant to OceanPredict to support the coordination of the fundamental and challenging issues in the ocean forecasting process, of which data assimilation is a significant part.

The Coupled Prediction-Task Team (CP-TT) is engaged in the development and advancement of earth system coupled models to improve predictions across a range of time-scales. Particular focus is on the role of the ocean in coupled models, their initialization and predictions and how the ocean influences other components (e.g., atmosphere, land, sea ice, waves, bio-geo-chemistry, etc).

 

Date and time

  • In-person meeting (with possible hybrid option)
  • 26-30 October 2026 (4 – 5 days)
  • ECMWF, Reading, UK

Objectives

The DA-TT and the CP-TT are organizing a Joint workshop at ECMWF on Advances in coupled modeling, data assimilation, and predictions to

  • foster development and evaluation of coupled and ocean DA systems
  • develop and advance Earth system coupled models and prediction
  • develop and advance cross-cutting themes (DA, coupled modeling, initialization, AI/ML)

This meeting aims to bring together experts in the field of ocean, coupled data assimilation (DA) and coupled modeling and forecasting to discuss the latest progress and challenges in the field, and to share experiences in the development of ocean/coupled DA and predictions from the underlying algorithms to the details of implementation in operational forecasting systems.

A short “white” paper covering the issues and recommendations will be submitted for publication following the meeting.

Themes

The workshop covers themes in the areas of the OceanPredict Task Teams CP-TT (coupled prediction) and DA-TT (data assimilation) and also cross-cutting aspects. 

Please see details below:

  1. DA-TT (data assimilation)
  2. CP-TT (coupled predictions)
  3. Cross- cutting

 

  1. DA-TT:
  • 1.1 Status of ocean-focused DA in operational forecasting, reanalysis systems, digital twins and climate prediction from global to regional and coastal systems.
  • 1.2 Advances in DA methods (ensembles, algorithms, machine learning, downscaling, error covariance modeling, etc).
  • 1.3 Observing systems: requirements, evaluation, design and associated DA developments.
  • 1.4 Software infrastructure and efficient use of DA on HPCs for ocean/coupled DA.
  1. CP-TT:
  • 2.1 Science of coupling for improving predictions at different time scales: weather- medium, sub-, seasonal scales. Complexity and challenges involved in medium- and seasonal-range predictions.
  • 2.2 Physical and dynamical consistency in coupling across earth system models and analyses.
  • 2.3 Representation of coupling feedbacks in earth systems models with different levels of complexity of individual components (parameterized vs resolved processes). Clarification of the key choices for (ocean) model configurations and parameterizations.
  • 2.4 Advances in coupling software infrastructure on current and future HPCs.
  1. Cross- cutting:
  • 3.1 Use of DA to improve models and reduce model error. Initialization of Earth system models (algorithm advancements, status, applications for seasonal and climate projections). DA for estimation of model parameters. Appropriate covariances for coupled models at high resolutions.
  • 3.2 Role of coupling and/or DA in the representation of extreme events (hurricanes, cyclones, marine heat waves, etc) and their predictions.
  • 3.3 Impact of ocean observations on coupled forecasts. Novel use of observations to improve coupled predictions, e.g., how to best use observations sensitive to the interface between the ocean and the atmosphere (for instance satellite radiances sensitive to ocean skin salinity/temperature)?
  • 3.4 Changing role of DA in the face of AI models and digital twins and integration strategies of AI and DA/coupled modelling.
  • 3.5 Coupled models and DA (e.g., ocean/atmosphere/sea-ice, physics/biogeochemistry, physics/acoustics) in the representation of carbon cycle. From earth system to regional scale models: marine carbon dioxide removal and related physical and biogeochemical processes: current status and improvements.
  • 3.6 Use of AI/ML in the ocean prediction system (both DA and CP).
  • 3.7 Contributions from the coastal community.

Abstracts

All submitted abstracts are available in the table below in pdf format.

Sorted alphabetically by author.

Abstract no First name Last name Affiliation Abstract title
1 Santha Akella Office of Modeling and Development (NOAA) Temporal variability of air-sea fluxes and ocean surface variables under extreme events
2 Magdalena Alonso Balmaseda ECMWF (contractor), University of Reading ECMWF OSES for long time scales: reanalysis and seasonal forecasts
3 Jeffrey Anderson NSF-NCAR A General Nonlinear and Non-Gaussian Strongly Coupled Ensemble Data Assimilation Capability
4 Hernan G. Arango (1) Rutgers University Recent Developments in ROMS Data Assimilation and Applications
5 Hernan G. Arango (2) Rutgers University Introducing the ROMS-JEDI Interface
6 Dorukhan Ardag NASA/GMAO Coupled Marine DA at GMAO with a JEDI-based DA workflow
7 Ali Aydogdu CMCC Foundation Implementation of OceanVar2.0 in the Black and Mediterranean Seas analysis and forecasting systems
8 Joao Marcos Azevedo Correia de Souza CIRES / NOAA PSL An Evaluation of the impact of ocean observations on a weakly coupled data assimilative Earth System Model – Developing NOAAs next reanalysis.
9 Philip Browne ECMWF Coupled ocean-atmosphere DA in operations at ECMWF
10 Yumeng Chen University of Reading A post-process smoother for improving the past reanalyses with sparse observations
11 Andrea Cipollone CMCC Foundation Euro-Mediterranean Center on Climate Change Multivariate data assimilation across ocean and sea-ice interface
12 Francois Counillon (1) NERSC Norwegian Climate Prediction Model Version 2: New Developments and their impact
13 Francois Counillon (2) NERSC Assessing the Role of Soil Moisture and Land Memory in Northern European Extremes Using a Coupled Large-Ensemble Prediction System
14 Francois Counillon (3) NERSC Enhancing Subseasonal-to-Seasonal Prediction of Arctic Summer Sea Ice through hybrid Machine Learning-dynamical modelling
15 Eric de Boisseson ECMWF Impact of ocean observations on climate signals in ECMWF global ocean reanalysis system
16 Dmitry Dukhovskoy NOAA NWS OMD Updated Sea Ice Initialization in JEDI SOCA for the Global Forecast System
17 James Duncan The Allen Institute for Artificial Intelligence Stochastic ensemble coupled modeling of pre-industrial control and 1% increasing CO2 scenarios with physically conservative machine learning emulators
18 Sergey Frolov NOAA Ocean and Coupled Earth System Emulator Development
19 Yosuke Fujii JMA/MRI Improvement of SST analysis and near sea-surface atmospheric predictions in the coupled atmosphere-ocean data assimilation system in JMA/MRI
20 Olivier Goux CERFACS/CECI Modelling wide-swath altimeter observation error correlations with a regularised diffusion operator
21 Bafana Gweba South African Weather Service Influence of the Agulhas Current on crossing swells along South African coasts
22 Supreeth Hanumappa Ludwig-Franzius-Institute for Hydraulic, Estuarine, and Coastal Engineering, Leibniz University Hannover. Offshore Operations and Maintenance: A GNN-LSTM Wave Forecasting Framework for safe Marine Logistics Operations in the German Bight
23 Parisa Heidary NOAA Salinity improvements in the NOAA’s US West Coast Ocean Forecast System (WCOFS)
24 Shoji Hirahara (1) Meteorological Research Institute / Japan Meteorological Agency Recent developments in the JMA Sub-Seasonal and Seasonal Ensemble Prediction System
25 Shoji Hirahara (2) Meteorological Research Institute / Japan Meteorological Agency Historical High-Resolution Daily SST Analysis (COBE-SST3) with Consistency to Monthly   Land Surface Air Temperature
26 Sarah Keeley ECMWF Recent upgrades to the coupled ocean – sea ice prediction system at ECMWF Prediction System
27 Jiyoun Kim Korea Institute of Atmospheric Prediction Systems (KIAPS) Improving Air–Sea Consistency through Weakly Coupled Atmosphere–Ocean Data Assimilation in the KIM (Korean Integrated Model) Coupled Earth System
28 Jessica Knezha CIRES, University of Colorado Boulder NOAA-NASA Joint Archive (NNJA): Publicly Accessible, Curated Observations
29 Joshua Kousal ECMWF Development of Wave-Sea Ice Interactions in the ECMWF Earth System Model
30 Alexander Kurapov NOAA National Ocean Service Can 4DVAR effectively correct the mean pycnocline depth in an ocean model?
31 Daniel Lea Met Office Assessing the new Met Office high resolution global ocean ensemble hybrid-3DEnVar system
32 Jungwon Lee IMT-Atlantique, Lab-STICC, Brest ML-based ocean emulator as a surrogate for ocean forecasting and reanalysis systems
33 Tong Lee NASA Jet Propulsion Laboratory Ocean dynamic persistence provides significant predictability for sea surface temperature variation associated with El Niño and La Niña
34 Tom Louden-Cooke Met Office Hybrid ensemble/variational data assimilation in the North-West European shelf at 7km resolution.
35 Nabir Mamnun Imperial College London, Department of Physics, London, United Kingdom of Great Britain – England, Scotland, Wales A Multi-Tracer Data Assimilation Framework for Constraining Global Ocean Transport and Mixing
36 Gabriela Martinez Balbontin Mercator Ocean International Chlorophyll-Conditioned Bias Correction in a Machine-Learned Seasonal Biogeochemical Forecast
37 Davi Mignac Met Office Machine-learning bias correction of passive-microwave sea-ice concentration using SAR improves Arctic analyses and forecasts
38 Kristian Mogensen ECMWF Effects of Atmospheric Forcing Frequencies on Ocean Data Assimilation and Coupled Forecasts on NWP Time Scales
39 Andrew Moore (1) University of California Santa Cruz Air-Sea Coupling at the Ocean Mesoscale: Some Reflections on the Challenges for Coupled DA
40 Andrew Moore (2) University of California Santa Cruz Dynamical Influences on Observation Impacts for Ocean Data Assimilation
41 Sophia Moreton UK Met Office Advancing ocean data assimilation towards a JEDI-based framework at the UK Met Office
42 Lars Nerger (1) Alfred Wegener Institute PDAF – Recent Developments for Open-Source Community Data Assimilation
43 Lars Nerger (2) Alfred Wegener Institute Coupled and nonlinear data assimilation into a regional ocean-biogeochemical model
44 Lars Nerger (3) Alfred Wegener Institute Assessing the global ocean carbon sink with data assimilation
45 Jean-Philippe Paquin Environment and Climate Change Canada Coupled Atmosphere-Ice-Ocean Prediction Systems at Environment Canada
46 Phillip Pegion NOAA Physical Sciences Laboratory Development of the NOAA Unified Forecast System and Its Application to Subseasonal and Seasonal Predictions
47 Charles Pelletier European Centre for Medium-Range Weather Forecasts REPLAY: a practical resolution-flexible surrogate method for ocean initial condition generation
48 Andrew Peterson Environment and Climate Change Canada Impact of Ocean Sub-Surface Observations on Coupled Atmosphere-Ocean 15-day Predictions
49 Nils Risse ECMWF Exploring the coupled assimilation of sea surface salinity derived from SMAP radiances in ECMWF-IFS
50 Chris Roberts ECMWF The role of the oceans for subseasonal prediction: insights from eddy-permitting and eddy-rich coupled forecast systems
51 Jonah Roberts-Jones Met Office UK Met Office operational ocean-atmosphere coupled numerical weather prediction system: current status and future outlook.
52 David Russell University of Maryland / NASA Toward Assimilating SWOT Altimetry Data into the Current and Next-Generation GMAO Subseasonal-to-Seasonal Forecast System
53 Anna Shlyaeva UCAR/CPAESS at NOAA/OMD Towards strongly coupled ocean-sea ice-atmosphere data assimilation for the Global Forecasting System
54 Sergey Skachko Environment and Climate Change Canada Development of an Ensemble-Based Ocean Data Assimilation System at ECCC
55 Josef Skakala (1) Plymouth Marine Laboratory and National Centre for Earth Observation Combining machine learning with data assimilation to improve the quality of phytoplankton forecasting in a shelf sea environment
56 Josef Skakala (2) Plymouth Marine Laboratory and National Centre for Earth Observation Impact of surface remote sensing reflectance assimilation and its associated uncertainties on the forecast for the visibility and water quality indicators in the turbid coastal waters
57 Andrea Storto CMRE A North-Atlantic-focused ocean prediction system for operations and research: results and ongoing developments at CMRE
58 Anna Teruzzi Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS Impact of assimilating biogeochemical data from multiplatform observing systems on ecosystem indicators
59 Prasad Thoppil U.S. Naval Research Laboratory Prolonged Westerlies in the Equatorial Indian Ocean: Atmospheric Drivers and Upper-Ocean Responses
60 Polina Verezemskaya University of Liege From surface optics to subsurface ecosystem structure: how reflectance assimilation propagates beyond biogeochemistry
61 Jennifer Waters Met Office Assessing ocean observation impacts through the SynObs multi-system observing system experiments.
62 Anthony Weaver CERFACS Scale-dependent background-error covariance modelling for ensemble-variational data assimilation, with application to ECMWF’s Ocean ReAnalysis (ORA) system
63 James While Met Office Data assimilation in the Met Office’s 1.5km model of the North West European shelf 
64 Xiaobo Wu NMEFC Formation process of Kuroshio large meander in 2017 analyzed by a hybrid data assimilation system
65 Chunxue Yang CNR-ISMAR Evaluation of a Coupled Regional Reanalysis for the Mediterranean Region Covering the period 1993-2024
66 Hao Zuo ECMWF Impact of Ocean Observations on the ECMWF Ocean DA System and Medium-range Weather Forecasts

Format

  • In-person participation is encouraged for the sake of enhanced interaction.
  • In case one can not join in-person, virtual participation is possible- more details will be provided at a later stage.

Registration and abstract submission

Please note that everyone who is planning to attend the Joint CP-TT/DA-TT workshop must register.

If you like to submit an abstract you have to use the abstract submission form in addition.

REGISTRATION

NOW OPEN – closed at 11 September 2026

Participation in this workshop is free of charge. It will run from 26/10/2026 – 30/06/206 (lunchtimes).

The workshop will be an on-site event with the option to stream presentations. If you are participating remotely you are still required to register. Presenting work remotely will only be possible in exceptional circumstances.

 ABSTRACT SUBMISSION

NOW CLOSED

You can upload a maximum of 3 abstract. The abstract should be provided as a .doc or .docx file, be no longer than 300 words and should ideally not include a graphic.

Please view a simple template here.

Attendance

Members of the two task teams (CP-TT and DA-TT) are warmly invited to the meeting, but also

  • attendees of previous DA-TT or CP-TT events
  • Members of the wider OceanPredict community
  • researchers who work in the field of ocean, coupled data assimilation (DA) and coupled modeling and forecasting
  • Junior scientists and graduate students are very much encouraged to attend.

CP-TT and DA-TT members or their substitutes are expressly invited to attend.

Flyer

Important dates

Date Description
April 2026 Save the date announcement
7 May 2026 Opening of Call for abstracts
3 July 2026 Call for abstracts closes (extended deadline)
2 July 2026 Registration opens
21 July 2026 Abstract confirmation emails
11 September 2026 Registration deadline
26-30 October 2026 Workshop

Organising committee

Scientific Organising Committee

(in no particular order)

  • Kristian Mogensen (ECMWF)
  • Hao Zuo (ECMWF)
  • Phil Browne (ECMWF)
  • Sarah Keely (ECMWF)
  • Marcin Chrust (ECMWF)
  • Anthony Weaver (CERFACS)
  • Andrew Moore (UCSC)
  • Ann Kristin Sperrevik (Met.no)
  • Santha Akella (NOAA)
  • Sergey Frolov (NOAA)
  • Joao Souza (NOAA)
  • Matthew Martin (Met Office)
  • Chris Harris (Met Office)
  • Kirsten Wilmer-Becker (Met Office)
  • Anna Teruzzi (OGS)
  • Stephanie Cuven (MOI)

Organisation space (login needed)

We have set up a private organisation page to the Joint CP-TT/DA-TT meeting Organising Committee (login needed – please contact programme office from more details).

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