学术文献库

Numerical weather forecasts can exhibit systematic errors due to simplifying model assumptions and computational approximations. Statistical postprocessing is a statistical approach to correcting such biases. A statistical postprocessing model takes input data from a numerical forecast model, and outputs a parametric predictive distribution of a real-world observation, with model parameters learned from past forecast-observation pairs. In this paper we develop and discuss methods for postprocessing of gridded data. We show that estimates of postprocessing parameters on a spatial grid can be improved by Bayesian hierarchical modelling with spatial priors. We use the "Max-and-Smooth" approach [Hrafnkelsson et al., 2021] to approximate a fully Bayesian inference in two steps. First we calculate maximum-likelihood estimates (MLEs) of postprocessing parameters at individual grid points. Second we smooth the MLEs using a measurement error model with a spatial prior. Our approach provides the theoretical basis for the parameter smoothing approach by Kharin et al. [2017], and simplifies and generalises the Bayesian hierarchical modelling approach by Moeller et al. [2015]. A new derivation of Max-and-Smooth is presented. The method is applicable to arbitrary postprocessing models, as illustrated on Model Output Statistics, Logistic Regression, and Nonhomogeneous Gaussian Regression. We report consistent improvements in forecast accuracy, calibration, and probabilistic skill in postprocessing of temperature and precipitation forecasts.