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Joint DA-TT/ECMWF workshop


 When: 17-20 May 2021
 Duration: 4 days
 Organisers: Andy Moore (UCSC)
Matt Martin (Met Office)
Marcin Chrust (ECMWF)
Anthony Weaver (CERFACS)
Magdalena Balmaseda (ECMWF)
Phil Brown (ECMWF)
Hao Zuo (ECMWF)
Patricia de Rosnay (ECMWF)
Massimo Bonavita (ECMWF)
Arthur Vidard (INRIA)
Kirsten Wilmer-Becker (Met Office)

Workshop objectives

This workshop aims to bring together experts in the field of ocean and coupled data assimilation to discuss the latest progress in the field, to outline the main challenges and to identify new directions for research.


Ocean data assimilation underpins many forecasting and reanalysis applications. Seasonal forecasts rely on ocean data assimilation to produce initial conditions for the ocean component of coupled forecasts. The production of multi-decadal ocean and coupled reanalyses has enabled the calibration of these forecasts and allows studies into the climate of the ocean’s recent past. Higher resolution global and regional short-range forecasts of the ocean also require initialisation through data assimilation which has been the focus of groups contributing to the Data Assimilation Task Team (DA-TT) of OceanPredict. With the advent of coupled NWP at many operational centres, including ECMWF, improving methods for ocean and coupled data assimilation is ever more important.


We continue to monitor the Covid-19 situation and have decided that the workshop will be organised as a virtual event. We will inform registered attendees and abstract submitters informed about access and participation arrangements in due course.

Hosted by the European Centre for Medium-Range Weather Forecasts (ECMWF)






Meeting format

The workshop will be a virtual event and take place over 4 days.

While the meeting will be physically located at ECMWF, attendance by video conference will also be possible.

The first 3 days will consist of invited and contributed oral presentations. Contributed talks will be selected through the abstract submission process.

Poster presentations will also be included with dedicated poster sessions.

A series of working group discussions will take place on the 4th day with a final plenary


The workshop will be structured around the following themes:

  1. Ocean and coupled reanalysis
  2. Coupled data assimilation (ocean, atmosphere, sea-ice, waves, biogeochemistry, etc)
  3. Data assimilation methods (algorithmic developments in variational, ensemble and hybrid DA, covariance modelling, etc)
  4. Applications of machine learning in data assimilation
  5. Model error (estimation, representation in variational and ensemble DA, etc)
  6. Assimilation of novel observations (i.e. under-utilized observations and upcoming missions)
  7. Recent assimilation infrastructure developments (e.g. OOPS, JEDI, future HPCs, etc)
  8. Development and assessment of data assimilation in forecasting applications (global and regional)


Registration and abstract submissions

Abstract submission is now closed.

Registration remains open until 31 March 2021.


Please visit the ECMWF website at: To complete the registration form, you first need to sign up to the ECMWF Indico account. Please follow the instructions on the link above.

To submit an abstract please go to the “Call for Abstracts” link. Please remember to read the submission instruction available via a link at the top of the form. Also please keep in mind:

  • Only one abstract can be submitted with the submission form at a time. If you wish to submit more than one, please repeat the submission process for additional abstracts.
  • Please identify the most relevant theme for your abstract. You are only able to link the abstract to one of the themes.

Participation in the event is free, and organizers will run an abstract acceptance process prior to the event with confirmation of abstracts in Feb 2021.

Important dates

1st July 2020 Call for abstracts and registration
7th February 2021 Abstract submission extended
28th February 2021 Abstract selection and agenda publication
31st March 2021 Registration deadline


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