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Project partners

RMI (Royal Meteorological Institute of Belgium)

The RMI fulfils a double mission. It provides valuable services to society through several meteorological products (general and specialised weather forecasts, near real time weather conditions, alerts for extreme weather). In order to guarantee the highest quality of these services, scientific research over many topics in meteorology and hydrology is carried out in the RMI departments.

The main expertise of the two units that compose the current team, the Dynamical Meteorology and Climatology Unit and the Risk Analysis Unit (Part of the Meteorological and Climatological Research Service), lies in the study of atmospheric and climate variability through dynamical systems theory and stochastic processes, in data assimilation, modelling of hydrological processes and validation of satellite products. The concept of error, which is of central importance in data assimilation problems, occupies a prominent place in the units’ activities.

A substantial contribution of the team to operational hydrology is the creation of the HEPS system, which is today the basis of the hydrological ensemble forecasts of the RMI. A novel statistical post-processing technique of hydrological data is at the core of this system. The long standing experience of the team in the validation of satellite data for precipitation, soil moisture and snow (H-SAF project of EUMETSAT), reached a milestone with the implementation of an improved version of EnKF in a hydrological model.


Scientific staff participitaging in the EODAHR project:

Stéphane Vannitsem, Stephane[dot]Vannitsem [at] meteo[dot]be

Emmanuel Roulin, Emmanuel[dot]Roulin [at] meteo[dot]be

Pierre Baguis, Pierre[dot]Baguis [at] meteo[dot]be

Joris Van den Bergh, jorisvdb [at] meteo[dot]be






KULeuven (Katholieke Universiteit te Leuven)

The Department of Earth and Environmental Sciences of the KU Leuven researches the functioning of geo- and ecosystems at different spatial and temporal scales, including the interaction between humans and the environment and the sustainable management of natural resources. The department further aims at providing attractive academic training and education at an international level.

Gabriëlle De Lannoy was appointed in the division of Soil and Water management the KU Leuven on 1 January 2016. She set up a new research group at the KU Leuven on land surface remote sensing, modelling and data assimilation. Her research involves soil and water processes at various spatial scales, ranging from the field scale to the global scale. Prior to her appointment at the KU Leuven, she worked at NASA/GSFC and was involved in the development of a new operational satellite-based data assimilation product for soil moisture estimation. Earlier, she performed research on soil moisture and snow data assimilation as an FWO postdoctoral researcher at the Institute of Global Environment and Society (IGES), and in collaboration with UGent and NASA.


Scientific staff participitaging in the EODAHR project:

Gabriëlle De Lannoy, gabrielle[dot]delannoy [at] kuleuven[dot]be

Alexander Gruber, alexander[dot]gruber [at] kuleuven[dot]be

Sara Modanesi, Sara[dot]Modanesi [at] kuleuven[dot]be






NERSC (Nansen Environemental and Remote Sensing Center)

Highlights of the Data Assimilation Group at NERSC:

  • international leadership in data assimilation, from theory to state-of-the-art applications within ocean and climate. Presently at the forefront of applications of the Ensemble Kalman Filter (EnKF);
  • contribute to operational oceanography (TOPAZ: forecast and reanalysis) and climate sciences (NorCPM: Seasonal to decadal predictions) with forecasts and reanalysis of the highest standard for the Arctic Ocean, sea ice, ecosystem and the coupled climate system;
  • contribute to the improvement of modelling and observation techniques, by testing hypotheses on their respective uncertainties;
  • provide validated reanalyses and forecasts for the operational oceanography and climate community with a focus on the Arctic and the Nordic Seas, e.g., a 30-years Arctic ice-ocean-ecosystem reanalysis and a global climate reanalysis (1850-present), both including residual uncertainty estimates;
  • provide a data assimilation framework able to take up new models and observations at the frontiers of technology, e.g., assimilation in the Lagrangian neXtSIM model, coupled data assimilation in NorCPM, observations with unconventional error characteristics such as satellite-borne sea ice thickness;
  • extend the theory of data assimilation for non-linear chaotic systems (assimilation in the unstable subspace, improved stochastic models). E.g., proposing new algorithms improving the performance of the Ensemble Kalman Filter.


Scientific staff participitaging in the EODAHR project:

Alberto Carrassi, alberto[dot]carrassi [at] nersc[dot]no