Impact of New Regional Land Cover Data on WRF Simulations of Heat Waves in Shanghai

The accuracy of land cover data is essential for improving simulations 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 using the Weather Research and Forecasting (WRF) model. Compared with the default land cover dataset (MODIS) in the WRF model, the ModelLand30 dataset provides a more nuanced depiction of land cover characteristics, particularly in urban areas. To evaluate the impact of ModelLand30 on the simulation of near-surface meteorological variables, simulations are conducted for high-impact heat waves in Shanghai using the MODIS dataset and the ModelLand30 dataset. Based on the ModelLand30 dataset, an additional simulation using the mosaic approach is conducted to further examine the influence of sub-grid surface heterogeneity. Results show that compared with the MODIS dataset, the ModelLand30 dataset reduces the overestimation of the surface sensible heat flux and successfully reproduces the diurnal cycle of the surface latent heat flux at the Fengxian station, as it provides more accurate description on land cover types. The ModelLand30 dataset also reduces the underestimation of the 2-m temperature during nighttime but overestimates the 2-m temperature during daytime in Shanghai urban areas. Due to the consideration of the effect of non-urban land types within sub-grid cells, the mosaic approach further improves the simulation performance of the surface latent heat flux. It also lessens the daytime temperature overestimation in the Shanghai urban area. This study highlights the application of the ModelLand30 dataset in the WRF model and the importance of the representation of sub-grid surface heterogeneity for heatwave prediction. 数值模式中的土地覆盖数据集的准确性对于提升近地面气象要素的模拟性能起到重要的作用。本文将自主研发的30米分辨率区域土地覆盖数据集ModelLand30应用到WRF模式中。与WRF自带的土地覆盖数据集MODIS相比，ModelLand30数据集提供了对土地覆盖特征更为精细的刻画，特别是在城市用地地区。为了评估ModelLand30数据集对近地面气象要素模拟的影响，针对上海高温热浪事件，分别使用MODIS和ModelLand30数据集进行了两组模拟试验。此外，基于ModelLand30数据集，使用WRF-Noah 陆面模式中的Mosaic方案额外增设一组模拟试验来进一步检验下垫面次网格异质性的影响。结果表明，由于ModelLand30数据集提供了合理的土地覆盖分布，可减少MODIS数据集对奉贤站感热通量的高估，并合理再现该站潜热通量的日变化。此外，ModelLand30数据集可减少夜间2米温度的低估，但对上海城区白天的2米温度高估。由于Mosaic方案考虑了次网格中非城市下垫面类型的影响，因此采用该方案可进一步提高城市地区潜热通量模拟性能，并减少白天温度的高估。本研究表明了ModelLand30数据集在区域模式中的应用价值以及下垫面次网格异质性在模式中的表达对高温热浪过程预测的重要性。