Annual Seminar 2026

Posters

This page lists all poster files provided to ECMWF for this event.

 
ML-Driven Observational Augmentation: Synthesizing High-Frequency Microwave Imagery of Tropical Cyclones for Enhanced Data Assimilation
Fan Meng (Nanjing University of Information Science and Technology)
 
A Lightweight Deep Learning Approach for Spatiotemporal Heat Prediction
Euan Marney (University of Glasgow)
 
Assimilating real Earth system observations with machine learning models
Laura Slivinski (NOAA)
 
Sub-footprint inhomogeneities can introduce representativeness errors in operational NWP data assimilation from global to regional scale
Lukas Muser (Deutscher Wetterdienst (DWD))
 
A robust ensemble Kalman filter under observation noise misspecification
Hans Reimann (University of Heidelberg)
 
All-sky Far-Infrared Deep Learning based Observation Operator
Simone Raciti (University of Bologna)
 
WeatherMesh: An All-in-One AI System for Data Assimilation and Weather Forecasting
Todd Huchinson (WindBorne Systems)
 
A Multi-Tracer Data Assimilation Framework for Constraining Global Ocean Transport and Mixing
Nabir Mamnun (Imperial College London, Department of Physics, London, United Kingdom of Great Britain – England, Scotland, Wales)
 
Towards a coupled data assimilation for atmospheric composition and numerical weather prediction in ARPEGE and AROME
Vincent GUIDARD (CNRM, Météo-France & CNRS)
 
Developments in 4D-Var assimilation of in situ observations
Bruce Ingleby (ECMWF)
 
A scalable approach for hierarchical non-Gaussian atmospheric trace gas inversions.
Stephen Pearson (University of Bristol)
 
Ensemble sensitivity analysis case study on observational impact of ground-based water vapor profiling networks and methodological questions
Nina Raabe (University of Cologne)
 
Forecast sensitivity to R matrix (FSR) implemented on ECCC's global prediction system
Daisuke Hotta (Environment and Climate Change Canada)
 
Exploiting cloud and moisture information from solar satellite channels
Leonhard Scheck (Deutscher Wetterdienst)
 
Using Analysis Increments to Investigate Systematic Biases in ERA5's Radiation Scheme
Jessica Matthew (University of Oxford)
 
A 4DVAR-aware method of decomposing geophysical model fields into eddy and background components
R. Keenan Forney (U.S. Naval Research Laboratory)