Spread/Error relationship and spatial error structure of precipitation ensemble nowcasting: Comparison of STEPS and generative AI

Abstract. The predictability of the generative AI-based nowcasting model LDCast is evaluated over Belgium, together with the pysteps implementation of the nowcasting algorithm STEPS. Neither STEPS nor LDCast were fine-tuned for the Belgian region, so both models are evaluated under conditions in which they will most likely be used in practice at national weather offices. STEPS and LDCast are slightly underdispersive, but the ensemble spread provides an estimation of the error at almost all scales. Both models adapt the properties of their ensembles to the type of event, either convective or stratiform. The spatial scores of the STEPS and LDCast ensembles are compared with those of surrogate ensembles, revealing that both STEPS and LDCast have very little ability to spatially localise the error of the ensemble mean. This suggests that the content of STEPS and LDCast ensembles is informative in terms of statistics, but not in terms of dynamics.