Identifying Earth System Features from Satellite Data

Reliable feature identification, long time series of identified features, and tools to explore them provide substantial benefits for weather forecasting, process understanding, climate information provision, and the evaluation of climate model outputs. Moreover, expert use of these features within an established feedback loop enables the creation of high-quality training datasets for further application development and for training machine learning (ML) models.  

EUMETSAT and its Member States are building a collaborative environment for joint manual or ML-assisted annotation, model development, and the database of identified features within the European Weather Cloud (EWC) to support these developments. The EWC is a cloud-based collaboration platform for meteorological application development and operations in Europe, and to enable the digital transformation of the European Meteorological Infrastructure.

EUMETSAT is compiling a database of long time series of meteorological features identified from various satellite datasets. This database will support the analysis of the development and interrelationships of these features, enabling new insights for both nowcasting and climate science. For example, the Deutscher Wetterdienst plans to use this environment to characterise convective storms using FCI-derived cloud-top features—such as overshooting tops—for nowcasting, with the primary aim of training an ML algorithm to automatically identify storm tops. 

Initial work has begun on identifying tropical storms from long Himawari time series, alongside a feasibility study on additional feature types. Early results from the feasibility study, demonstrating the potential for performing feature identification on long time series of Earth-observation data, will be presented. 

The joint working environment is available in the EWC, which is open to authorised users from ECMWF and EUMETSAT Member and Co-operating States for official duties and R&D projects. It consists of data-proximate cloud infrastructure, alongside the EWC Community Hub, which enables collaborative development, code and ML model sharing, and the exploitation of meteorological applications.