Improved Urban Finescale Forecasting During a Heat Wave by Using High-Resolution Urban Canopy Parameters

Abstract

For urban weather finescale forecasting, obtaining accurate and up-to-date urban canopy parameters (UCPs) is necessary and still a challenge. In this study, a high-resolution dataset of UCPs was developed by using vector-format building information and then applied in the WRF/urban system with the single-layer urban canopy model (SLUCM)/building effect parameterization (BEP) model to improve the urban finescale forecasting of a typical heat wave event during summer 2016 in Hangzhou. A series of sensitivity experiments were conducted, and the results showed that the high-resolution UCP data improved the model skill in simulating the spatial distributions and diurnal variations of 2-m temperature, 2-m relative humidity, and 10-m wind speed in the urban areas of Hangzhou, especially for the BEP model. Better results were produced when refining the computation domain due to more realistic urban morphological characteristics were adopted. The sensitive experiments suggest that the high-resolution UCPs played a significant role in representing the UHI effect though changing the surface thermodynamic parameters (e.g., roughness length), hereafter increasing the sensible heat and surface heat flux, and finally resulting a notable urban heat island (UHI) effect.