Rapid Update NWP Postprocessing with AI and Real-Time Measurements

Effective short-term weather forecasting is vital for informed decision-making during severe weather events to mitigate their impact. Traditional numerical weather prediction (NWP) models often face challenges in accurately predicting rapidly evolving weather phenomena. This study introduces an innovative approach that uses artificial intelligence (AI) to post-process NWP forecasts for the near future with respect to the latest available weather measurements. In the scope of this work, our solution leverages real-time synoptic scale meteorological station measurements, radar reflectivity data, and satellite imagery to post-process Global Forecast System (GFS) predictions for the Central Europe area. By fusing these diverse data sources, both the accuracy and resolution of the input GFS predictions are enhanced, offering an increase in prediction step resolution from 3 hours to 1 hour and an update of the forecasts with the most recent measurements every 30 minutes. Our solution internally uses a deep neural network trained to post-process GFS predictions to mimic ERA5 reanalysis as closely as possible. The predicted variables are total accumulated precipitation, temperature 2 meters above the ground, and wind gusts. However, in theory, the presented approach is not limited to the abovementioned set of input data or target variables. The model achieves up to 2.5 times lower mean absolute error compared to baseline forecasts, showcasing its effectiveness in capturing real-time weather dynamics. Moreover, the model exhibits the capability for rapid updates as new weather measurements become available, continuously refining predictions. This dynamic adaptability ensures that forecasts remain relevant and accurate, even in rapidly changing weather conditions. Alongside the quantitative evaluation against the ERA5 data, we will present a case study showcasing the usefulness of the post-processed forecasts in specific weather situations.