Impact of New Land Cover Data on WRF Simulation of Heatwaves in Shanghai

Accurate land cover data are essential for improving representation of near-surface meteorological conditions in numerical weather prediction models. In this study, a newly developed 30-m resolution regional land cover dataset (ModelLand30) is applied to the Weather Research and Forecasting (WRF) model. Compared with the default land cover (MODIS) dataset in WRF, the ModelLand30 dataset provides a more nuanced depiction of land cover characteristics, particularly in urban areas. To evaluate the impact of ModelLand30 on simulation of near-surface meteorological variables, experiments are conducted for two high-impact heatwave events that hit Shanghai in August 2020, using the MODIS dataset (EXP1) and the ModelLand30 dataset (EXP2). Based on the ModelLand30 dataset, an additional experiment using the mosaic approach (EXP3) is conducted to further examine the influence of sub-grid surface heterogeneity. The results show that compared with EXP1, EXP2 reduces the overestimation of surface sensible heat flux and successfully reproduces its diurnal cycle, because of more accurate land cover types in ModelLand30. EXP2 also reduces the underestimation of 2-m temperature during nighttime but overestimates it during daytime in Shanghai urban areas. Due to consideration of the effect of non-urban land types within sub-grid cells, the mosaic approach (EXP3) further improves the simulation of surface latent heat flux, and also lessens the daytime temperature overestimation in the Shanghai urban area. The results highlight the advantage of the ModelLand30 dataset in WRF and the importance of better representation of sub-grid surface heterogeneity for improved heatwave prediction.