Identifying Earth System Features from Satellite Data for Nowcasting

Reliable feature identification, long time series of identified features, and tools to explore them provide substantial benefits for weather nowcasting—including warning generation—medium-range 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 machine learning (ML) model training.

With the advent of new methods enabled by cloud services and machine learning, the hydro-meteorological community has launched numerous projects to identify meteorological features from remote-sensing data, including satellite imagery. EUMETSAT and its Member States are building a collaborative environment for joint manual annotation, model development, and the Earth System Feature Database within the European Weather Cloud (EWC). 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. It consists of data-proximate cloud infrastructure, alongside with the EWC Community Hub which enables collaborative development, sharing of code and ML models and the exploitation of meteorological applications.

EUMETSAT also plans to compile 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 all timescales from nowcasting to climate and downstream models such as impact predictions. Initial work has begun with a feasibility study for additional feature types.

This presentation will introduce the collaborative working environment for feature identification, how to take part in the collaboration, and provide example use cases. It will also present early results from the feasibility study, demonstrating the potential for performing feature identification on long time series of Earth-observation data.