A new method is proposed for modelling the yearly maxima of short-term precipitation, with the aim of producing spatial maps of return level estimates. Yearly precipitation maxima are modelled using a Bayesian hierarchical model with a latent Gaussian field, with the blended generalised extreme value (bGEV) distribution used as a substitute for the more standard generalised extreme value (GEV) distribution. Inference is made less wasteful with a novel two-step procedure that performs separate modelling of the scale parameter of the bGEV distribution using peaks over threshold data. Fast inference is performed using integrated nested Laplace approximations (INLA) together with the stochastic partial differential equation (SPDE) approach, both implemented in \texttt{R-INLA}. The model is fitted to yearly short-term precipitation maxima from the south of Norway, and is able to quickly produce high-resolution return level maps with uncertainty. Using cross-validation, we find that the proposed model provides a better fit to short-term precipitation data than the standard block maxima model.

翻译：为模拟短期降水量的年最高值,提出了一种新的方法,目的是制作回报水平估计的空间地图。年度降水量最高值采用贝叶西亚等级模型和潜高山场模型进行模拟,混合的极端值分布(bGEV)用于替代更标准的通用极端值分布。推论用一个新的两步程序来减少浪费,该程序使用峰值超过阈值的数据对BGEV分布的尺度参数进行单独的模型。使用综合的巢巢状拉普近地(INLA)和Stochacistic局部差分方程式(SPDE)方法进行快速推推,这两种方法均在\ textt{R-INLA}中实施。该模型适合挪威南部每年的短期降水量峰值,并且能够迅速生成具有不确定性的高分辨率返回水平地图。我们通过交叉校准,发现拟议的模型比标准区块模型更适合短期降水量数据。