Advances in Project IMA, the Seamless Prediction Programme of the Royal Meteorological Institute of Belgium

Seamless prediction systems provide frequently updated forecasts across different timescales by combining observations, such as weather radar data, with numerical weather prediction (NWP) models. These systems are increasingly needed by users like hydrological services, local authorities, renewable energy operators, and smartphone apps to make better and earlier decisions. This is especially true for precipitation, which is highly variable in space and time and strongly influences downstream models like (urban) hydrology. To achieve this, forecasts must not only be fast and accurate but also come with calibrated ensembles to estimate uncertainty and propagate errors properly.
In Belgium, Project IMA (inspired by the Japanese word for "now" or "soon") is the seamless prediction system developed by the Royal Meteorological Institute (RMI). It uses RMI’s observation network, including RADQPE for gauge-corrected precipitation estimates, the pysteps-be probabilistic rainfall nowcasting system, the INCA-BE nowcasting system, and the ACCORD NWP models ALARO and AROME. Unlike many other systems, Project IMA offers seamless ensemble precipitation nowcasts for lead times up to 6 hours, updated every 5 minutes, designed to improve flash flood predictions and quantify their uncertainty.
This presentation will showcase recent developments in Project IMA, including updates to the open-source pysteps framework, such as an improved runtime efficiency, code structure and better representation of extremes. We will discuss new deep learning-based methods for blending forecasts to extend their lead time and improve accuracy, calibration, and usefulness for end users such as hydrologists, crisis managers and water authorities.
Project IMA aims to ensure a rapid transfer from research to operations and encourages open-source contributions to ensure transparency and reproducibility. It supports the United Nations’ “Early Warnings for All” initiative, which strives to make forecasts more accessible and actionable by 2027.