The current neighborhood probability (NP) method mainly considers the spatial displacement error in high-resolution precipitation forecasts, but the problem of the forecast time exceeding or lagging behind the observed field has not been properly solved. Therefore, a temporal factor was introduced into the NP method in this paper, and precipitation forecasts were evaluated in different spatial-temporal neighborhoods based on the improved NP method and fractions skill score (FSS), combined with the relative operating characteristic (ROC) curve. The results indicated that the forecasting accuracy of the ensemble forecast was higher than the control forecast. The neighborhood ensemble probability (NEP) and probability matched mean (PMM) methods were superior to the traditional ensemble mean (EM) method in forecasting heavy rainfall, which compensated for the limitations of the heavy rainfall forecasting ability of EM. For such squall line processes, a spatial scale of 15–45 km neighborhood radius could effectively rectify the displacement error of precipitation. There was a corresponding relationship between temporal scale and rainfall intensity in convective-scale precipitation forecast, so the temporal uncertainty of different levels of precipitation could be captured by different temporal scales. The spatial and temporal scales had interdependent influences on precipitation forecast effects, which could be affected by the intrinsic spatial-temporal scale of convective-scale weather systems. The improved NP method could simultaneously reflect the spatial and temporal uncertainties of convective-scale precipitation forecasts in high-resolution models, achieving a comprehensive assessment of spatial-temporal scale and providing probabilistic forecast results that match different levels of precipitation.

Constructing underground reservoirs has emerged as a crucial strategy to address the shortage of fresh water in Rizhao, Shandong Province, China. However, the water quality, microbial community composition, and biogeochemical cycling of nutrients in underground reservoirs compared to raw water remain unknown. To unveil the characteristics of microbial community structures and their nitrogen cycling metabolic potential in coastal underground reservoirs, we utilized a functional gene array (GeoChip 5.0) in conjunction with high-throughput sequencing of 16S rRNA and 18S rRNA genes. Our findings indicate that the water quality in the underground reservoir exhibits a certain degree of eutrophication compared to raw water, with higher concentrations of TN, TP, NO₃⁻N, NO₂⁻-N, and Chl a, but lower concentrations of DO and NH₄⁺-N. The alpha diversity of bacterial and microeukaryotic communities was significantly lower in the underground reservoir. The bacterial community presented a stronger correlation with environmental factors than the microeukaryotic community. Regarding the relative abundance of bacterial communities, Gammaproteobacteria dominated the bacterial community in raw water, while Gammaproteobacteria and Alphaproteobacteria dominated the bacterial community in underground reservoir water. Additionally, the relative abundance of Nitrospirae was noticeably higher in the underground reservoir water. Moreover, we found significantly higher sequence abundance of the archaea Thaumarchaeota in the underground reservoir. Furthermore, our analysis revealed that, except for the amoA functional gene, which significantly increased the metabolic potential of nitrification, the metabolic potential of other microbial nitrogen functional genes was significantly reduced. This reduction may contribute to the lower concentration of NH₄⁺-N in the underground reservoir. This study provides a comprehensive understanding of the microbial community characteristics and their nitrogen cycling metabolic potential in underground reservoirs. It serves as a valuable reference for water source selection, the formulation of water quality assurance measures, and the construction and management of underground reservoirs for subsequent impounding.