HourGlass: Filling in the Gaps Between Forecasts

Summary

A forecast valid at noon and another at 18:00 can tell us where the weather is going, but not everything that happens in between. Storms develop, move and weaken continuously, yet many state-of-the-art artificial intelligence (AI) weather forecasting systems provide forecasts only every six hours. While this resolution is sufficient for many applications, finer temporal detail is essential for applications such as flood forecasting and renewable energy, as well as for rapidly evolving weather events. Producing hourly forecasts directly is possible but can lead to error accumulation and temporal inconsistencies. HourGlass is our new data-driven probabilistic temporal downscaling method developed through a collaboration between MET Norway and ECMWF to address this challenge. Rather than replacing the underlying forecast, HourGlass reconstructs the likely evolution of the atmosphere between forecast times, generating coherent hourly forecasts that remain consistent with the original prediction. The collaboration combines MET Norway's expertise in regional AI weather forecasting with ECMWF’s expertise in global AI weather forecasting, demonstrating that the same approach can be successfully applied across forecasting scales. Following successful evaluation in both global and regional forecasting systems, HourGlass will become part of ECMWF's operational forecasting system later this year. As part of the Artificial Intelligence Forecasting System (AIFS) v3, it will provide hourly forecasts of key surface variables for both AIFS Single and AIFS Ensemble (AIFS ENS), bringing high-temporal-resolution AI forecasts to operational users. At MET Norway, HourGlass is already running in near-real time and is available to forecasters for evaluation, with plans to implement it operationally to produce hourly forecasts for the Bris ensemble forecasting system. How HourGlass generates hourly forecasts from six-hourly inputs. M₁ and M₂ represent different ensemble members. Preserving variability and consistency This operational capability is made possible by two key features of HourGlass. Existing temporal downscaling methods have typically been trained deterministically, which can lead to overly smooth forecasts, particularly near the centre of the temporal window. HourGlass instead uses variants of the continuous ranked probability score (CRPS) to help preserve small-scale…