Enhancing Climate Resilience Through Urban Microscale Weather Data Analysis

As part of the Climate Ready Barcelona project, a crucial aspect is the development of a Climate Vulnerability Index (CVI). This abstract highlights the creation of the climatological and meteorological foundation, at the microscale urban level, for the CVI models, which is a critical task within this project.

The CVI is visualized via an interactive interface, illustrating its geographical distribution in Barcelona under existing and potential climate change scenarios. The meteorological data sets used in this research offer a high-resolution grid, which facilitates a detailed examination of each building block. Furthermore, the system is capable of generating forecasts and alerts for imminent climate events, such as heatwaves or extreme nighttime temperatures.

The CVI and alerts are derived from an integration of diverse data types, including energy consumption, climate and weather data, socioeconomic factors, and building characteristics. The climate and meteorological base for the CVI models also involves the integration of diverse data types, which, in this case, only focuses on regional and European models. Both of these data are represented and analyzed using a knowledge graph, which encapsulates diverse urban environment concepts and aggregates data at various administrative levels.

A significant aspect of this work involves the construction of an ontology for structuring these highly heterogeneous datasets. This ontology forms the backbone of the knowledge graph, comprising a linked network of nodes encapsulating various concepts within the urban environment. These include data aggregated at different administrative levels and points of interest strategically distributed throughout the urban environment.

The connections established among the distinct node typologies facilitate the development of advanced geospatial data analytics modules. These modules enable the accurate estimation of a climate vulnerability index, providing valuable insights into urban heat risk dynamics and potential mitigation strategies.

Coupled with machine learning techniques, the knowledge graph predicts urban heat patterns, drawing insights from historical data and identifying underlying trends. This integration offers a platform for advanced analytical reasoning, simulation, and accurate forecasting, capturing the spatial and temporal components inherent in the data.

The resulting map highlights areas most susceptible to climate change impacts, providing invaluable information for policymakers and planners. This aids in the development of informed decisions to enhance the city's climate resilience and adaptation strategies. Furthermore, it provides Barcelona's municipal housing department with a detailed diagnosis of climate-vulnerable areas, crucial for energy renovation plans.

The Climate Ready Barcelona project has been funded by Local Governments for Sustainability (ICLEI) and the Google Foundation and demonstrates a commitment to proactive climate change readiness. The work presented here underscores the importance of creating a robust climatological and meteorological foundation to enhance the liability of urban resilience models at the building scale, a task that forms the core of this research.