Pointwise and Complex Quality Metrics in Atmospheric Modeling: Methods and Approaches

Abstract

In atmospheric sciences, various quantitative indicators, or metrics, are used to describe the quality of modeling results of various flavors including numerical weather prediction, statistical correction, various downscaling products, etc. Metrics provide the accuracy of reproduction of the processes underlying the models and allow comparison of models by assessing the uncertainty of their results. The key importance of metrics lies in a more thorough study of the advantages and disadvantages of classical approaches and in the development of new, more complex assessment methods. This article presents a classification of the most frequently encountered quality metrics in the scientific literature. In addition to assessing traditional pointwise metrics, complex methods considering various aspects of modeling results and special metrics used in climate studies are described. Among the complex metrics, methods with an emphasis on the spatial structure and heterogeneity of the predicted variable fields and probabilistic methods for verifying ensemble forecasts are distinguished. Special attention in this paper is devoted to the growing popularity of object-oriented metrics and metrics based on rare and extreme events. Climate models are assessed by comparing the results of retrospective modeling with historical data, which complicates the choice of metrics. A variety of climate metrics focusing on specific climate processes or integrating several parameters is described. The need for developing more diverse metrics for effective evaluation of climate models is explored. All metrics considered in this article are supplemented by examples in the scientific literature and assessments of their application to atmospheric research.