Assessing the Performance of a Convection-Permitting Regional Hydroclimate Simulation in Urban Areas across the Continental United States

Accurate representation of urban areas in weather and climate modeling is crucial for understanding and predicting the complex interactions between urban environments and atmospheric processes. CONUS404, a long-term, high-resolution reanalysis dataset based on a convection-permitting regional simulation, is among the most comprehensive datasets capable of resolving various hydrometeorological processes across the continental United States (CONUS). However, its performance in urban environments, particularly the effectiveness of the bulk urban parameterization scheme, has not yet been evaluated. This study aimed to assess CONUS404’s decadal-scale hourly hydrometeorological simulations in urban environments across the CONUS using station observations from the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H) v1.0. Our analysis encompassed 208 urban grids and 342 non-urban grids from 2011 to 2020. The results suggest the mean absolute error in hourly temperature is smaller in urban grids than in non-urban grids. Specifically, hourly temperature shows a mean cold bias of –0.51°C in non-urban grids but a slight mean warm bias of 0.09°C in urban grids, with daily maximum temperatures underestimated and daily minimum temperatures overestimated. For hourly dewpoint simulations, urban grids exhibit a stronger dry bias compared to non-urban grids, indicating a potential underrepresentation of evapotranspiration from urban vegetation when using the bulk urban approach. Hourly wind speed is underestimated in both urban and non-urban grids, on average, with average biases of –0.73 m/s and –0.35 m/s, respectively. Further evaluation on a seasonal time scale revealed significant challenges in accurately resolving wintertime temperature and dewpoint. We also found a strong dependency of model errors on grid-level urban fraction. This study highlights the importance of appropriate urban surface representation in weather and climate modeling and has significant implications for improving the accuracy of urban hydrometeorological and climate simulations at the continental scale.