Integration and execution of Community Land Model Urban (CLMU) in a containerized environment

However, CLMU presents significant challenges due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, a toolkit was developed, including (1) an operating system-independent containerized application developed to streamline the execution of CLMU and (2) a Python-based tool used to interface with the containerized CLMU and create urban surface and atmospheric forcing data.

This toolkit enables users to simulate urban climate and explore climate-related variables such as urban building energy consumption and human thermal stress. It also supports the simulation under future climate conditions and the exploration of urban climate responses to various surface properties, providing a foundation for evaluating urban climate adaptation strategies.