Assessment of two approaches for very short range precipitation prediction for a convection-dominant period at different scales

Abstract

Extreme precipitation plays a crucial role in managing weather-related risks, particularly flooding, and has a significant impact on various sectors such as hydrology, agriculture, and economy. It is important to evaluate precipitation forecast outputs to help refining prediction models, especially for heavy events. This study examines the performance of two weather models, both designed to provide frequently updated forecasts for a short time range, but using different approaches: extrapolation-based and fully dynamical 3D numerical weather prediction (NWP) models. These models are assessed over a two-month period with several heavy thunderstorms, using data from Austria and a smaller catchment in southeast Austria. We addressed the errors and uncertainties in these models using different verification metrics and two different observational datasets. In general, both models performed well in the first hour of the forecast at both large and small scales. However, performance decreased with lead time after the first hour, with a faster decline in the extrapolation-based model. The performance of the models changed when considering different precipitation thresholds. For higher thresholds (heavier precipitation), the NWP model performed better than the extrapolation-based model, whereas it performed worse for lower thresholds. When using the object-oriented Structure Amplitude Location (SAL) metric, the dynamical NWP model performed better regarding the structure of precipitation, while the extrapolated-based model had a better performance when considering the amplitude parameter, i.e., the total amount of precipitation. With a lower error in the first hours of each run, the dynamical NWP model showed that an hourly update cycle helps to reduce the forecast errors noticeably. The results highlight the need of considering forecast model performance at different intensity thresholds and spatial scales.