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.

In the context of increasing global climate change and frequent extreme weather events, the stable operation and development of the electricity market face significant risks. As an important component of weather derivatives, weather futures can transform meteorological data into tradable financial products, achieving market-based allocation of weather risks. They provide efficient risk-hedging tools for power generation companies, electricity retailers, and large consumers, helping to optimize resource allocation. This paper takes Shanxi province as an example and electricity and meteorological data from 2022 to 2024 are used in the study. By integrating electricity market indicators and meteorological factors, a model is constructed using multiple linear regression and nonlinear transformations. A stepwise regression approach is applied to select a concise and significant set of features, which are then used to form the Daily High Temperature Index (DHTI). Corresponding weather derivative contracts are designed accordingly. The constructed index incorporates meteorological elements such as the daily temperature index as well as electricity factors including day-ahead price, real-time price, and renewable energy output. After nonlinear transformation and dimensionless processing, the index demonstrates a strong correlation with electricity revenue and exhibits good model stability. The case study demonstrates that this index can be effectively used for hedging by both power generators and consumers, smoothing revenue fluctuations caused by temperature changes. It provides low-cost and high-efficiency risk management tools for electricity market participants, promoting the improvement and development of electricity market in China.