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Urbanization have been significantly reshaping the form of urban areas and natural landscapes, leading to complex urban morphologies. In 2012, the Local Climate Zone (LCZ) classification was proposed to address this issue and has since been widely adopted in urban climate studies globally. Despite its prevalence, literature on dynamic mapping of urban morphology remains sparse, making it difficult to delve into the study of urban renewal year by year. In this study, we compared different training scales, producing dynamic mappings of urban morphology with a spatial resolution of 100 meters spanning from 2000 to 2022 in major Chinese cities, based on the LCZ scheme. The results demonstrate strong inter-year consistency, and the accuracy of urban morphology change mapping is overall higher than 70%. Additionally, our results exhibit good alignment with other LCZ mapping datasets, more suitable for the current development situation in China, and effectively discriminate between building heights and densities across different LCZ types. This dataset holds significant potential for enhancing urban morphology monitoring and advancing urban climate research. [ABSTRACT FROM AUTHOR]

The complexities of urban climate and environmental challenges have garnered significant attention in the 21st century. Numerical simulations, offering high spatiotemporal resolution meteorological data, are essential tools in meteorological research and atmospheric science. Accurate representation of urban morphology parameters is crucial for enhancing the precision of these simulations in urban areas. Despite the availability of urban canopy parameter (UCP) data for 44 major cities in the United States and 60 in China for the weather research and forecasting (WRF) model, a comprehensive global dataset representing urban morphology remains absent. This study addresses this gap by leveraging existing global three-dimensional vector data of buildings, including footprints and heights, to compile a global 1 km spatially continuous UCP (GloUCP) dataset for the WRF model. Our findings indicate that GloUCP not only surpasses existing datasets in accuracy but also provides superior spatial coverage. In key urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Guangdong-Hong Kong-Macao Greater Bay Area in China, GloUCP offers detailed and reliable urban morphological information that closely aligns with reference datasets, outperforming other available sources. Similarly, in U.S. cities like Seattle, San Francisco, and Philadelphia, GloUCP consistently achieves lower RMSE values and higher correlation coefficients, demonstrating its robustness in modeling diverse urban environments. Furthermore, GloUCP's capability to effectively capture the vertical distribution of buildings, particularly in high-rise areas, highlights its utility in urban climate modeling and related applications. As UCPs are pivotal in regulating atmospheric responses to urbanization, the availability of this globally consistent urban description is a crucial prerequisite for advancing model development and informing climate-sensitive urban planning policies. The GloUCP dataset, converted to WRF binary file format, is available for download at https://doi.org/10.6084/m9.figshare.27011491 (Liao et al., 2024). [ABSTRACT FROM AUTHOR]

Mitigating tornado damage remains a critical challenge for communities throughout much of the United States. Enhanced construction techniques typically used in hurricane-prone regions are often recommended as at least part of the solution, but the effectiveness of these techniques lacks empirical evaluation. This study investigates the performance of modern wood-frame residential structures in Arabi, LA, constructed to hurricane-resistant standards, that were impacted by a strong tornado on March 22, 2022. Field teams deployed beginning March 25, 2022, just three days after the tornado touched down, capturing perishable data on the building performance using vehicle-mounted surface-level panoramic cameras, forensic engineering investigations, and unmanned aerial systems. This paper describes the field deployments and identifies common failure mechanisms observed by the field teams. Analytical fragility functions are developed based on the load path observations and compared to empirical fragility functions generated from linking the building performance observations with wind speed estimates at each building derived from a parametric wind field model conditioned to tree-fall patterns. The study finds that despite the frequent use of hurricane-resistant hardware, such as hurricane straps and anchor bolts, key weak links elsewhere in the load path compromised the resistance and led to premature failures. The agreement between the empirically- and analytically-derived fragility functions was lacking, demonstrating the challenges that remain in understanding near-surface tornado wind loading and structural response. Nonetheless, the study provides a consensus framework for future tornado assessments that can help improve our understanding of tornado loading through the utilization of reconnaissance data. Further, the study findings on deficiencies in the load paths of homes in hurricane-prone regions have practical value to risk assessments and future construction practices. [ABSTRACT FROM AUTHOR]