Climate Impact Modelling Framework

TL;DR

The paper introduces CIMF, a flexible cloud-based framework for deploying geospatial climate impact models, enabling scalable, customizable risk assessments with integrated physical and machine learning models.

Contribution

It presents a novel modular, cloud-based framework that simplifies deployment and customization of climate impact models, enhancing scalability and user accessibility.

Findings

Successful deployment of flood risk models using CIMF

Flexible configuration of workflows for diverse climate impact assessments

Integration of physical and machine learning models demonstrated

Abstract

The application of models to assess the risk of the physical impacts of weather and climate and their subsequent consequences for society and business is of the utmost importance in our changing climate. The operation of such models is historically bespoke and constrained to specific compute infrastructure, driving datasets and predefined configurations. These constraints introduce challenges with scaling model runs and putting the models in the hands of interested users. Here we present a cloud-based modular framework for the deployment and operation of geospatial models, initially applied to climate impacts. The Climate Impact Modelling Frameworks (CIMF) enables the deployment of modular workflows in a dynamic and flexible manner. Users can specify workflow components in a streamlined manner, these components can then be easily organised into different configurations to assess risk in different ways and at different scales. This also enables different models (physical simulation or machine learning models) and workflows to be connected to produce combined risk assessment. Flood modelling is used as an end-to-end example to demonstrate the operation of CIMF.