Cell tracking -based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall.

The rapid temporal evolution of convective rainfall poses a challenge for rainfall nowcasting models. With the growing potential of machine learning models for precipitation nowcasting to produce realistic-looking nowcasts for long lead times, it is important to investigate whether the nowcasts also produce realistic development for convective rainfall. Common verification metrics traditionally used to validate nowcasting models are often dominated by large-scale stratiform rainfall, and averaging the metrics across entire precipitation fields obscures how accurately the models replicate individual convective cells, which makes it difficult to distinguish the model skill for the growth and decay of convective rainfall. In this study, we present a convective cell tracking-based framework to investigate how accurately nowcasting models reproduce the development of convective rainfall. The framework consists of first identifying and tracking the convective cells in the input observation rainfall fields, and then identifying and tracking the cells separately in the target observations and the nowcast rainfall fields by continuing the cell tracks identified in the observations. Features describing the cells and cell tracks, such as the cell volume rain rate and area, are then extracted. In addition to the errors in these feature values, the models' skill in reproducing the existence of convective cells is estimated by calculating several contingency-table metrics, such as the Critical Success Index. The results allow the analysis of how accurately the models reproduce the growth and decay of convective rainfall and quantify the differences between the models, for example, due to differences in how the models smooth the nowcasts, i.e., blurring. The framework also allows differentiation of the results based on the initial conditions of the cell tracks, demonstrated here by separating the tracks into decaying or growing cell tracks based on the cell status when the nowcast is created. The framework is demonstrated using four open-source advection-based models: the advection nowcast, S-PROG, and LINDA implemented in the pysteps library, and L-CNN, with data from the Swiss radar network. The results indicate that the L-CNN model reproduced the existence of convective cells best among the models and had smaller errors in the cell volume rain rate than LINDA and S-PROG. LINDA had the smallest underestimation in the cell mean rain rate, whereas S-PROG significantly overestimated the cell volume rain rate and area because of blurring. [ABSTRACT FROM AUTHOR]