Utilizing Local Climate Zone Data for Land Use Representation in Weather Research and Forecasting (WRF) Models

Utilisation des données des zones climatiques locales pour la représentation de l'utilisation des terres dans les modèles de recherche et de prévision météorologiques (WRF) Utilizing Local Climate Zone Data for Land Use Representation in Weather Research and Forecasting (WRF) Models Hamza NISAR1, 2, Christophe MENEZO1, Mattheos SANTAMOURIS2 1 Laboratoire LOCIE UMR 5271, Université Savoie Mont Blanc – CNRS (France) 2 School Built Environment, University New South Wales (Sydney) Abstract: Local Climate Zones (LCZs) classify urban areas based on their morphology and thermal properties. Using the WUDAPT Level 0 methodology, this study develops an LCZ map for Lyon by leveraging satellite imagery and open-source tools. The process involves collecting training data, performing classification, and validating results. The LCZ map reveals Lyon’s diverse urban structures, offering valuable insights for climate studies and sustainable urban planning. This study highlights the utility of LCZ datasets in addressing challenges posed by urbanization and climate change. Introduction: Land-use data play a crucial role in atmospheric numerical modeling by quantifying the physical properties of various land types. These properties, such as albedo (α), emissivity (ε), surface roughness length (z₀m), soil heat capacity (C), thermal inertia (λ), and soil moisture availability (M), significantly influence land-atmosphere exchanges. These exchanges affect the energy balance, heat fluxes, and moisture dynamics, which are essential for modeling nearsurface meteorological variables such as temperature and humidity [1], [2]. Accurate land-use representation is vital for ensuring the reliability of numerical models like the Weather Research and Forecasting (WRF) model. Land-use variability directly impacts predictions of temperature, precipitation, and humidity [3], [4]. Existing datasets, such as the USGS global land-use map (30″ spatial resolution, ~1 km) [6] and the CORINE dataset for Europe (100 m resolution) [5], provide a baseline for land-use representation. However, these datasets lack the granularity needed to capture urban heterogeneity, particularly in densely populated areas. The LCZ framework addresses this limitation by offering detailed classifications of urban and natural areas into distinct categories based on morphology and land cover [10]. This finegrained approach improves the representation of urban effects on climate variables, such as near-surface air temperature (SAT) [9]. https://doi.org/10.25855/SFT2025-158 The WUDAPT (World Urban Database and Access Portal Tools) project provides a standardized methodology for generating LCZ maps globally using open-source tools and satellite imagery [14], [15]. The workflow involves collecting training data, applying supervised classification algorithms, and validating results with local knowledge. LCZ maps have significantly improved urban climate modeling, particularly when integrated with the WRF Urban Canopy Model (WRF-UCM) [10]. For example, WRF-UCM modifies urban surface parameters, enabling the simulation of diverse urban morphologies and their impacts on SAT [8]. Despite its advantages, the WUDAPT approach faces challenges, including variability in mapping quality due to inconsistent training data and classification errors [18], [19], [20]. Differentiating between similar LCZ types (e.g., compact high-rise vs. compact mid-rise) remains difficult due to limited building height data. Ground-truth data scarcity also complicates validation. Despite these challenges, WUDAPT has been applied to over 100 cities, showcasing its scalability and utility for urban climate research [14], [15]. This study applies the WUDAPT LCZ methodology to develop a detailed map for Lyon, France. By integrating the LCZ map with the WRF model, we aim to assess its quality, spatial resolution, and applicability in capturing urban thermal environments, particularly near-surface air temperature variations. Study Area and Model Configuration: The study focuses on Lyon, France, characterized by diverse urban development, including low-rise residential zones and high-density areas such as compact low-rise and mid-rise LCZ types. Lyon’s temperate oceanic climate, with August being the warmest month (26–30°C average temperatures), experiences pronounced urban heat island (UHI) effects due to dense infrastructure and limited green spaces. To simulate atmospheric conditions during the peak UHI period (17–22 August), the Weather Research and Forecasting (WRF) model is employed with three nested domains. The outermost domain (D1, 3.5 km resolution, 150x150 grid) captures regional meteorology, while the second domain (D2, 1.17 km, 130x130 grid) focuses on urban and peri-urban areas. The innermost domain (D3, 0.39 km, 100x100 grid) resolves localized UHI effects and urban-atmosphere interactions, ensuring detailed, multi-scale analysis. This hierarchical domain configuration ensures comprehensive and accurate coverage of meteorological processes across multiple spatial scales, as depicted in Figure 1. The initial and boundary conditions for the WRF model are derived from the ERA5 reanalysis dataset, which provides high-quality atmospheric data with a temporal resolution of 1 hour and a spatial resolution of approximately 31 km. ERA5 data accurately represents the large-scale meteorological conditions influencing the study area. The WRF model is coupled with the Building Effect Parameterization (BEP) and Building Energy Model (BEM) to represent urban canopy processes accurately. The BEP accounts for the effects of urban structures on wind flow, turbulence, and energy exchange, while the BEM simulates the thermal properties of buildings. Figure 1: Study area of Lyon and Domains Land Use Data: The WUDAPT Local Climate Zone (LCZ) dataset is utilized to evaluate its impact on UHI simulations. Google Earth has been employed to provide initial mapping by collecting training data for various urban and peri-urban zones as shown in Figure 2 . This data is then integrated into the WUDAPT platform, which facilitates the creation of the LCZ map through its standardized community-driven mapping workflow. The high granularity of the resulting LCZ dataset enables precise representation of urban morphology and land-use patterns. By incorporating this dataset into the WRF-UCM model, the study investigates its effectiveness in capturing urban-atmosphere interactions and its influence on UHI effects. Figure 2: Training data and LCZ Classification Results: Land Use Classification Analysis of LCZ: The classification of urban land use and land cover is a crucial factor in understanding the spatial heterogeneity of urban morphology and its implications for climatic processes. The WUDAPT LCZ dataset utilizes values from 30 to 40 to define urban zones, distinguishing 10 categories such as Compact Highrise, Open Lowrise, and Compact Midrise. This high granularity enables precise differentiation of localized urban features, as shown in Figure 3. . Figure 3: LCZ Classification Map The LCZ dataset demonstrates its ability to capture fine-scale variability in urban morphology. For example, it records 1928 pixels for Open Lowrise and 308 pixels for Compact Midrise, reflecting its detailed representation of urban characteristics. Categories such as Compact Highrise and Open Highrise are well-represented, further highlighting LCZ’s superior capacity for resolving diverse urban morphologies. Evaluation of Temperature Simulations: The statistical evaluation of temperature simulations using the LCZ dataset against observational data at two distinct locations (Bron and LFLL) reveals key insights into its performance and suitability for urban climate modeling. As depicted in Figure 4 and Figure 5, the LCZ dataset demonstrates strong alignment with observational data, with correlation coefficients of R=0.96 for Bron and R=0.94 for LFLL. The statistical metrics, summarized in Table 3, underscore LCZ’s ability to resolve finer-scale temporal dynamics, albeit with slightly higher RMSE values compared to broader datasets. Location Dataset RMSE (°C) Bias (°C) R R² Bron LCZ 1.80 0.65 0.96 0.90 LFLL LCZ 1.36 0.24 0.97 0.93 Table 1: Statistical Evaluation Data LCZ’s strength lies in its capacity to capture detailed diurnal temperature fluctuations and localized thermal variability. This is achieved through its precise classifications, such as Compact Highrise and Lightweight Lowrise zones, which are critical for microclimatic assessments. Figure 5 illustrates the temperature patterns for Bron, where LCZ exhibits significant variability in diurnal cycles, reflecting the nuanced impact of urban morphology on thermal dynamics. Similarly, Figure 4 highlights the LFLL location, where LCZ demonstrates its ability to represent complex thermal interactions in diverse urban and peri-urban environments. Figure 4: Comparison of simulated temperature against observational data (LFLL station) Figure 5: Comparison of simulated temperature against observational data (Bron station) Building on the statistical evaluation, the spatial temperature distribution maps further highlight the strengths of the LCZ dataset. As shown in Figure 6, LCZ captures highly localized temperature variations, emphasizing the impact of urban morphology on thermal profiles. Its detailed classifications allow for the identification of specific features, such as "HighTemperature Zones" in densely built areas and "Cooler Urban Fringes" influenced by vegetation. This granularity is essential for microclimatic studies and urban heat mitigation efforts, where understanding fine-scale dynamics is crucial Figure 6: Lyon Temperature Distribution The LCZ dataset’s high spatial resolution makes it ideal for detailed urban climate modeling and localized interventions. Its ability to capture fine-scale urban morphology and land-use impacts ensures that it is indispensable for studies focused on urban heat islands, microclimate assessments, and targeted mitigation strategies. The inclusion of detailed land-use types provides the necessary granularity for advancing urban climate research and supporting sustainable urban development initiatives. Conclusion This study demonstrates the effectiveness of the WUDAPT LCZ dataset in urban climate modeling. The dataset’s high spatial resolution and detailed classifications enable precise representation of urban heat islands and microclimatic dynamics. Statistical evaluations reveal strong alignment with observational data, underscoring its suitability for urban climate research. By capturing fine-scale urban morphology, the LCZ dataset supports sustainable urban planning and climate adaptation strategies. Additionally, it provides a robust framework for testing large-scale urban surface cladding, evaluating the influence of mass integration of solar PV components, and assessing urban energy efficiency measures. These applications are crucial for optimizing building materials, enhancing urban resilience, and developing strategies to mitigate climate change impacts in densely populated areas. Acknowledgment This project is supported by the AUFRANDE program and co-funded by the European Union and the EUR Solar Academy. References: [1] F. Chen and Y. Zhang, “On the coupling strength between the land surface and the atmosphere: From viewpoint of surface exchange coefficients,” Geophys Res Lett, vol. 36, no. 10, May 2009, doi: 10.1029/2009GL037980. [2] R. Mahmood, R. A. Pielke, and C. A. McAlpine, “Climate-relevant land use and land cover change policies,” Bull Am Meteorol Soc, vol. 97, no. 2, pp. 195–202, Feb. 2016, doi: 10.1175/BAMSD-14-00221.1. [3] B. Jiménez-Esteve, M. Udina, M. R. Soler, N. Pepin, and J. R. Miró, “Land use and topography influence in a complex terrain area: A high resolution mesoscale modelling study over the Eastern Pyrenees using the WRF model,” Atmos Res, vol. 202, pp. 49–62, Apr. 2018, doi: 10.1016/J.ATMOSRES.2017.11.012. [4] S. Hong, V. Lakshmi, E. E. Small, F. Chen, M. Tewari, and K. W. Manning, “Effects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model,” Journal of Geophysical Research: Atmospheres, vol. 114, no. D18, p. 18118, Sep. 2009, doi: 10.1029/2008JD011249. [5] “(PDF) A Description of the Advanced Research WRF Version 3.” Accessed: Jan. 16, 2025. [Online]. Available: https://www.researchgate.net/publication/306154004_A_Description_of_the_Advanced_Research_W RF_Version_3 [6] J. R. Anderson, E. E. Hardy, J. T. Roach, and R. E. Witmer, “A land use and land cover classification system for use with remote sensor data,” Professional Paper, 1976, doi: 10.3133/PP964. [7] Q. Huang and Y. Lu, “Urban heat island research from 1991 to 2015: a bibliometric analysis,” Theor Appl Climatol, vol. 131, no. 3–4, pp. 1055–1067, Feb. 2018, doi: 10.1007/S00704-016-2025-1. [8] Y. Shi, L. Katzschner, and E. Ng, “Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity,” Science of the Total Environment, vol. 618, pp. 891–904, Mar. 2018, doi: 10.1016/j.scitotenv.2017.08.252. [9] T. Liang et al., “Simulation of the influence of a fine-scale urban underlying surface on the urban heat island effect in Beijing,” Atmos Res, vol. 262, Nov. 2021, doi: 10.1016/J.ATMOSRES.2021.105786. [10] I. D. Stewart and T. R. Oke, “Local climate zones for urban temperature studies,” Bull Am Meteorol Soc, vol. 93, no. 12, pp. 1879–1900, Dec. 2012, doi: 10.1175/BAMS-D-11-00019.1. [11] L. C. Hay Chung, J. Xie, and C. Ren, “Improved machine-learning mapping of local climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine,” Build Environ, vol. 199, Jul. 2021, doi: 10.1016/J.BUILDENV.2021.107879. [12] J. Hu, P. Ghamisi, and X. X. Zhu, “Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification,” ISPRS Int J Geoinf, vol. 7, no. 9, Sep. 2018, doi: 10.3390/IJGI7090379. [13] A. Middel, J. Lukasczyk, R. Maciejewski, M. Demuzere, and M. Roth, “Sky View Factor footprints for urban climate modeling,” Urban Clim, vol. 25, pp. 120–134, Sep. 2018, doi: 10.1016/j.uclim.2018.05.004. [14] B. Bechtel et al., “Mapping local climate zones for a worldwide database of the form and function of cities,” ISPRS Int J Geoinf, vol. 4, no. 1, pp. 199–219, Mar. 2015, doi: 10.3390/IJGI4010199. [15] M. Demuzere, J. Kittner, and B. Bechtel, “LCZ Generator: A Web Application to Create Local Climate Zone Maps,” Front Environ Sci, vol. 9, Apr. 2021, doi: 10.3389/FENVS.2021.637455. [16] X. Zhou et al., “Exploring the impacts of heat release of vehicles on urban heat mitigation in Sendai, Japan using WRF model integrated with urban LCZ,” Sustain Cities Soc, vol. 82, Jul. 2022, doi: 10.1016/J.SCS.2022.103922. [17] M. Demuzere et al., “A global map of local climate zones to support earth system modelling and urban-scale environmental science,” Earth Syst Sci Data, vol. 14, no. 8, pp. 3835–3873, Aug. 2022, doi: 10.5194/ESSD-14-3835-2022. [18] C. Ren et al., “Assessment of Local Climate Zone Classification Maps of Cities in China and Feasible Refinements,” Sci Rep, vol. 9, no. 1, Dec. 2019, doi: 10.1038/S41598-019-55444-9. [19] C. Zhao, J. Jensen, Q. Weng, N. Currit, and R. Weaver, “Application of airborne remote sensing data on mapping local climate zones: Cases of three metropolitan areas of Texas, U.S.,” Comput Environ Urban Syst, vol. 74, pp. 175–193, Mar. 2019, doi: 10.1016/j.compenvurbsys.2018.11.002. [20] X. X. Zhu et al., “So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets],” IEEE Geosci Remote Sens Mag, vol. 8, no. 3, pp. 76–89, Sep. 2020, doi: 10.1109/MGRS.2020.2964708.