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.

翻译：在不断变化的气候中,应用模型评估天气和气候的物理影响的风险及其对社会和商业的随后后果至关重要。这些模型的运作历来是言语性的,并受特定计算基础设施、驱动数据集和预设配置的限制。这些制约因素带来了规模化模型运行和将模型置于感兴趣的用户手中的挑战。我们在这里为最初用于气候影响的地理空间模型的部署和运行提出了一个基于云的模块框架。气候影响建模框架(CIMF)使模块工作流程能够以动态和灵活的方式部署。用户可以简化地指定工作流程组成部分,然后这些组成部分可以很容易地组织成不同的配置,以不同的方式和不同的规模评估风险。这也使得不同的模型(物理模拟或机器学习模型)和工作流程能够连接起来,以产生综合风险评估。洪水建模被用作展示CIMF运作的端对端范例。