| 09 Sep 2026
High-Resolution Wide-Coverage Urban Canopy Parameters for Urban Simulations in Weather Research and Forecasting Models
Abstract. Cross-disciplinary researchers focusing on connected urban processes, especially those running numerical weather simulations at microscales, require reliable data on building footprints, heights and locations. For example, quantifying the impact of the thermal radiative properties of buildings on urban heating during a heat wave benefits from representation of the 3-dimensional physical characteristics of buildings within the urban location studied. Additionally, understanding how and where urban pollution travels within a city depends on how the buildings from ground level to building tops distort the air flow throughout the city. Even knowledge about which buildings may be susceptible to flooding given the magnitude of a recent rainstorm can be clarified by knowing where buildings are located with respect to the elevation of the earth beneath and surrounding them. As urban micrometeorological modeling helps answer more local scientific questions, high resolution data with wide coverage are needed. Previous research into these issues has yielded useful products for simulating these effects at resolutions of 1 kilometer and coarser; however, no regional-weather-model-readable data products are available at block level resolution for the full extent of any city, county, or wider region. To address this gap, four data sets are presented here: 1) Chicago, 2) Washington, DC, 3) Los Angeles County, and 4) the Arizona “urban corridor.” Parameters include, at 100-meter resolution, frontal area density, plan area density, rooftop area density, plan area fraction, mean building height, standard deviation of building heights, area weighted mean of building heights, building surface area to plan area ratio, height to width ratio, sky view factor and roughness length calculations. These data sets were generated using a new Python tool and validated using statistical and visual methods.
This manuscript presents urban canopy parameter (UCP) datasets at 100 m resolution for four urban regions in the United States to support urban simulations in the Weather Research and Forecasting model. These datasets could be useful for representing urban morphology in numerical models. However, several aspects require clarification, particularly the scope of validation. My specific comments are as follows.
1. The title should explicitly indicate that the datasets cover four urban regions in the United States. The current wording, particularly “Wide-Coverage,” could lead readers to expect a dataset with much broader or even global coverage. Specifying the geographical scope would better align the title with the contents of the manuscript.
2. Line 101: Imputation of missing building heights. The manuscript states that missing building heights were imputed using the median height of the twenty nearest buildings. Please justify the suitability of this approach and the choice of twenty neighbors. Building heights can vary considerably over short distances, and proximity alone may not adequately represent local morphological differences. Given the target resolution of 100 m, errors in imputed heights could affect the derived UCPs. Please report the proportion and spatial distribution of buildings with missing heights in each study region and evaluate the accuracy of the imputation method, for example by withholding known heights and comparing the estimates with the original values. The implications of imputation uncertainty for the resulting UCPs should also be assessed.
3. Please specify the reference year or acquisition period of the source data used to derive the UCPs for each study region. If building footprints and heights come from different years or sources, please explain how these temporal inconsistencies were handled and clarify the period that the resulting datasets represent.
4. The current validation focuses on mean building height, which is insufficient to establish the reliability of the full range of parameters provided. I recommend extending the evaluation through comparisons with existing UCP datasets for additional shared variables, such as plan area fraction, frontal area density, and building height variability, where available. Such comparisons should account for differences in spatial resolution, parameter definitions, and reference years. A broader quantitative evaluation would provide stronger support for the reliability of the datasets and help identify parameter-specific uncertainties.
5. Line 31: There appears to be an extra occurrence of “approaches.” Please remove the redundant word.
6. Line 87: The expression “at 15 5m intervals” is unclear and appears to contain a typographical or formatting error. Please correct it and clearly specify the intended interval.