The European Centre for Medium-Range Weather Forecasts (ECMWF) has launched its 2021-2030 strategy, which it says will guide its activities over the next decade. The strategy is updated every five years through a process of consultation and final approval by ECMWF’s Council of Member States.
According to the center, the key focus points of its strategy will include:
- Overcoming both computational and scientific challenges to achieve ensemble forecasts at three- to four-kilometer resolution;
- Extracting maximum value from observations to produce an accurate analysis of the Earth system, consistent across its components;
- Developing next-generation models to produce high-resolution digital twins of the Earth;
- Increased use of cloud technologies to enable efficient use of data;
- Integrated global reanalyzes and re-forecasts of weather and environmental hazards from 1950 onward;
- Estimation and monitoring of CO2 emissions;
- Contribution to the optimization of the global observing system;
- Moving toward open data.
It also notes that partnerships and collaborations with the European Meteorological Infrastructure (EMI), which includes key partners such as the member states, EUMETSAT and EUMETNET, but also with the World Meteorological Organization, the European Union and the European Space Agency, will remain crucial to its operations.
In terms of the computational challenges ahead, the ECMWF notes that artificial intelligence will play an important role, and in particular machine learning, which will become part of the whole numerical weather prediction and climate services workflow. To this end, alongside its strategy launch, the center also revealed a roadmap for machine learning activities which provides a framework to help channel the many activities in machine learning for weather and climate predictions into a coordinated effort.
Lead author of the paper Peter Dueben explained, “The ambition of the paper is to show how machine learning fits into, benefits or replaces existing developments to improve numerical weather prediction and climate services. With the help of this roadmap, we aim to collaborate with ECMWF ,ember and co-operating states and the weather and climate modeling community in Europe to make the most of machine learning. Our vision is that by 2031 machine learning is fully integrated into numerical weather prediction and climate services and has improved predictions and the use of predictions in many areas of the workflow.”
ECMWF states that it is already making use of machine learning in many parts of its work, including, for example, data assimilation. Here, observations and the forecast model are compared to derive the initial conditions for the next weather prediction. If differences between the model and the observations are diagnosed, machine learning tools can be used to learn to estimate model error for specific weather situations. This error representation can be used to analyze the behavior of the error, or to correct for the error within data assimilation, to improve initial conditions and therefore predictions. In recent years, this approach has also been integrated in the development of the Climate Change Service (C3S) and Atmosphere Monitoring Service (CAMS) components of the EU’s Copernicus Earth Observation Services.
ECMWF director-general Florence Rabier concluded, “The unprecedented volumes of data from sensors and satellites processed by ECMWF and the accuracy of Earth system models contribute to the protection of life and property on this planet in the face of environmental and climate change. ECMWF’s use of AI and machine learning will be a game-changer in continuing to make increasing amounts of data and information freely available for and, most importantly, usable by anyone well into the future.”