Project IMA: Building the Belgian Seamless Prediction System

Thanks to recent advances in multisensory observation systems and high-resolution numerical weather prediction (NWP) models, a wealth of information is available to feed and improve operational weather forecasting systems. At the same time, end users such as the renewable energy sector and hydrological services require increasingly detailed and timely weather forecasts that take into account the latest observations.

However, data assimilation in NWP models cannot yet leverage the full spatial or temporal resolution of today's observation systems. Moreover, the combined assimilation and model run takes significantly more time than an extrapolation-based nowcast, and cannot match its accuracy at short lead times. Therefore, many National Meteorological Services (NMSs) are moving towards seamless prediction systems. Seamless prediction aims to make optimal use of today’s rapidly available, high-resolution multisensory observations, nowcasting algorithms and state-of-the-art convection-permitting NWP models. This approach integrates multiple data and model sources to provide a single, frequently updating deterministic or probabilistic forecast for lead times from minutes to days.

We present the seamless ensemble prediction system of the Royal Meteorological Institute of Belgium, called Project IMA (Japanese for "now" or "soon"). It provides rapidly updating seamless forecasts for the next 5 minutes to 24 hours. The nowcasting component is based on two systems: (1) the open-source probabilistic precipitation nowcasting scheme pySTEPS, which now features a scale-dependent blending with NWP ensemble forecasts (also presented in this session) and (2) an ensemble of INCA-BE nowcasts using two different NWP models, for other meteorological variables. The short-range NWP component consists of a multimodel lagged Mini-EPS of two convection-permitting configurations of the ACCORD system: AROME and ALARO, running at 1.3km resolution. It features a 3-hourly DA cycle and provides high-frequency precipitation output to facilitate the blending of precipitation nowcasts and forecasts. The system runs robustly using our NodeRunner tool based on EcFlow, ECMWF's operational work-flow package. We will give an overview of the development (past and future), some lessons learned, and use cases for Project IMA.