EGU23-7233, updated on 28 Apr 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

A flexible, scalable, cloud-native framework for geospatial modelling

Blair Edwards1, Paolo Fraccaro1, Nikola Stoyanov1, Anne Jones1, Junaid Butt1, Julian Kuehnert1, Andrew Taylor2, and Bhargav Garikipati2
Blair Edwards et al.
  • 1IBM Research
  • 2STFC Hartree Centre

Understanding and quantifying the risk of the physical impacts of climate change and their subsequent consequences have crucial importance in the changing climate for both businesses and society more widely. Historically, modelling workflows to assess such impacts have been bespoke and constrained by the data they can consume, the compute infrastructure, the expertise required to run them and the specific ways they are configured. Here we present, a cloud-native modelling framework for running geospatial models in a flexible, scalable, configurable, user-friendly manner. This enables models (physical or ML/AI) to be rapidly onboarded and composed into workflows. These workflows can be flexible, dynamic and extendable, running as for historical events, or as forecast ensembles, with varying data inputs, or extended to model impact in the real world (e.g. for example to infrastructure and populations). The framework supports the streamlined training and deployment of AI models, which can be seamlessly integrated with physical models to create hybrid workflows. We demonstrate the application and features of the framework for the examples of flooding and wildfire.

How to cite: Edwards, B., Fraccaro, P., Stoyanov, N., Jones, A., Butt, J., Kuehnert, J., Taylor, A., and Garikipati, B.: A flexible, scalable, cloud-native framework for geospatial modelling, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7233,, 2023.