| 9:00-9:30 | Arrival and Coffee |
| 9:30-9:45 | Welcome |
| 9:45-11:15 | Talks Session 1 |
| 11:15-11:45 | Break |
| 11:45-12:45 | Talks Session 2 |
| 12:45-14:00 | Lunch |
| 14:00-15:30 | Talks Session 3 |
| 15:30-16:00 | Break |
| 16:00-16:45 | Panel Discussion |
| 16:45-17:00 | Introduction to breakout groups |
| 17:00-19:00 | Drinks / Dinner |
| 9:00-9:30 | Arrival and Coffee |
| 9:30-12:00 |
Breakout Groups
|
| 12:00-13:00 | Feedback Session |
| 13:00-14:00 | Lunch |
Hannah Christensen - University of Oxford
Abstract TBC.
Associate professor and head of the Atmospheric Processes group at the University of Oxford. Researching uncertainty quantification of parameterisation schemes in numerical models and leading the Model Uncertainty-Model Intercomparison Project (MUMIP). She is also exploring the application of machine learning methods in the domain.
Christopher Sprague - Alan Turing Institute
Abstract TBC.
Senior Research Associate at the Alan Turing Institute.
Cyril Morcrette - Met Office
Abstract TBC.
Cyril leads a team improving the way that clouds and radiation are represented in weather forecasts and climate simulations.
Alan Geer - ECMWF
Traditional numerical weather prediction (NWP) takes observations of the earth system, combines these observations with physical models using data assimilation in order to create "initial conditions", and then runs a physically-based model from these initial conditions to forecast future weather. Data-driven forecasts, based on machine learning, are now in some respects more accurate than those made by physical models. Attempts are also being made to replace the data assimilation process to create an "end-to-end" data-driven forecasting system that directly converts observations into weather forecasts. However, there are strong arguments for retaining the physical approach, including the Bayesian perspective that the most accurate posterior knowledge of the atmosphere should come from the combination of observationally-based knowledge with prior knowledge, meaning physical equations and other knowledge embedded in numerical models. But the quality of data-driven forecasts suggests that existing physical forecast models have substantial errors and need to be improved. This motivates a hybrid physical-empirical approach to weather forecasting, using empirical components to improve or augment the physical approach. One approach is to learn systematic error corrections that can be applied to the physical forecast model every few hours, or at the timestep level. A more "granular" hybrid approach targets the most uncertain components of a forecasting system, which are replaced or augmented by data-driven components, including more focused systematic error corrections, while other components may remain entirely physical. Both approaches are being explored inside the physically-based Integrated Forecasting System (IFS) of the European Centre for Medium-range Weather Forecasts. The error correction approach is in testing and significantly improves the quality of physically-based forecasts, bringing them closer in quality to data-driven equivalents, while retaining most benefits of the physical approach. A granular hybrid is already used operationally in the forward modelling of satellite radiance observations in sea ice areas, where no sufficiently accurate physical models and state information were previously available. This opens up a new area for physically-based NWP. In both cases, a major question is how best to train and maintain the empirical components, especially when the surrounding physical model versions are upgraded. This likely involves a combination of pre-training offline and fine-tuning online. The aim is to continue to optimise the machine learning components within the data assimilation system for weather forecasting, most likely decoupled from the process of obtaining initial conditions. From a technical point of view, the empirical components have been implemented in Python-based machine learning packages for the offline training and then rewritten in Fortran and C++ when used in NWP systems. As solutions to these issues become more developed, they may define the architecture of hybrid environmental prediction systems for decades to come.
Principal Scientist at ECMWF.
William Hornsby - UKAEA
Abstract TBC.
Scientific software engineer specialising in modern HPC architectures and machine learning for simulation in fusion applications.