Physics-based urban climate models are essential tools that enable us to understand and predict urban hydrometeorological and climatic conditions, which have become increasingly important with global urbanization. Many mesoscale, regional, and global climate models have incorporated UCMs to better characterize urban climates and the effects of various climate change mitigation and adaptation strategies. Nevertheless, when modeling an entire metropolitan area, the heterogeneous characteristics of the built environment are often oversimplified, posing great challenges to the accuracy and reliability of simulations with UCMs. The street canyon representation in UCMs requires a set of descriptive parameters assigned to each urban land cover class, however, these representative urban parameters may not fully reflect the disparities within the same urban land cover class, as used in the current model. Besides, although the urban land cover and land use (LCLU) evolves both spatially and temporally, most current urban climate simulations use static LCLU data as the boundary conditions.  This is partially due to data availability, especially the Global South cities. This lack of dynamic information will likely lead to substantial uncertainties for long-term simulations in urban areas. Furthermore, even with the same set of input data, the derived representative urban parameters can vary at different resolutions. However, the sensitivity of urban climate models to these urban parameters, in particular their performance in long-term simulations across various scales, remains unclear. 

The overarching goal of this project is to leverage extensive EO data as dynamic boundary conditions to improve the performance of urban climate modeling, and accordingly, to better understand model sensitivity to different LCLU parameters across scales. Three specific objectives include: (1) Enhance dynamic LCLU representation for numerical models using multidimensional remotely sensed data; (2) Improve urban climate model performance with dynamic LCLU data across different spatial scales. Specifically, we will use the WRF model coupled with a single-layer UCM for long-term urban climate modeling. (3) Objective 3: Build an interactive web-based digital twin (DT) platform to allow interaction with users, and to deliver data based on users' preferences (e.g., output resolution and time span).

The most prominent innovation of our project is that a higher level of dynamic urban land parameters is extracted and updated than what is currently used in most urban climate models. By leveraging the rich information from multi-source and multi-source remote sensing data, we can capture the fine-scale details of building geometry (building height, building roof width, and road width), vegetation cover (LAI), and surface properties (albedo), which can significantly impact the accuracy of urban climate simulations. By enhancing urban canopy representation, parameterization of urban processes, and multimodal EO data processing, the proposed work would enhance the accuracy and reliability of urban climate model simulations. This improvement would lead to more precise predictions of urban temperature, air quality, and other environmental variables, aiding decision-makers in addressing climate-related risks and vulnerabilities in cities. Further, this proposed work will enable researchers to explore climate change impacts and adaptation strategies in urban areas, and accordingly, support efforts to enhance urban resilience and adaptation to climate change. Researchers can use this DT platform to test hypotheses, validate models, and share their findings with the community. Practitioners can apply the insights and findings from researchers to practical applications, bridging the gap between science and practice. This collaborative environment supported by this DT platform can accelerate innovation and the development of new solutions to urban climate challenges.