EMS Annual Meeting Abstracts
Vol. 20, EMS2023-355, 2023, updated on 06 Jul 2023
EMS Annual Meeting 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Novel High-Resolution AI-based Global Precipitation Forecasting System

Marvin Vincent Gabler, Jair Wuillaud, Hamid Taheri Shahraiyni, Daniela Neupert, Alexey Grigoryev, Rodrigo Almeida, Azamat Galimzhanov, Gabriel Martin Hernandez, Jordan Dane Daubinet, Nikoo Ekhtiari, Roan John Song, Peter Dudbridge, and Emrecan Tarakci
Marvin Vincent Gabler et al.
  • Jua.ai AG, Pfäffikon, Switzerland

In this study, we present Jua’s Vilhelm, an innovative high-resolution AI-based global precipitation forecasting system. Vihelm exhibits significant improvements over existing state-of-the-art numerical models for prediction of binary precipitation (precipitation events) of up to 48 hours in advance, demonstrating superior performance when compared to the well-established Integrated Forecast System (IFS) model. Leveraging a combination of cutting-edge deep learning techniques and an in-depth understanding of physics, Vilhelm generates high-resolution hourly global predictions for surface parameters on a 1x1km grid. Beyond precipitation, the model is particularly adept at forecasts for a number of additional critical surface parameters including wind speed, direction and air temperature. This study focuses on its performance on precipitation prediction, notably the speed in which forecasts can be produced. The model runs in a matter of seconds, enabling the execution of hundreds of ensemble runs within a small number of minutes, an accomplishment previously unattainable. To assess the performance of Jua Vilhelm against Numerical Weather Prediction (NWP) models, we selected the IFS model of the European Centre for Medium-Range Weather Forecasts (ECMWF) as a suitable comparison. The ECMWF’s ERA5 reanalysis dataset was employed as a benchmark for evaluating the Vilhelm model on a global scale, using its full 0.25-degree resolution. We benchmarked both models at 6-hour intervals for multiple initial conditions for one year. Various metrics (Accuracy, Precision, Recall, Heidke Skill Score, F1 Score, True Negative Rate and False Alarm Rate) were used for comparative analysis between IFS precipitation forecasts and Vilhelm’s model. Vilhelm’s precipitation forecasts showed better performance than IFS over all metrics for prediction of precipitation events. Furthermore, precision, recall and P-R Curve of Vilhelm model for prediction of precipitation events were calculated using SYNOP (Surface Synoptic Observations) data as ground truth data. The results demonstrated the high performance of Vilhelm model. The results showed that development of Jua Vilhelm marks a significant advancement in the field of weather forecasting, offering unprecedented accuracy and speed.

How to cite: Gabler, M. V., Wuillaud, J., Taheri Shahraiyni, H., Neupert, D., Grigoryev, A., Almeida, R., Galimzhanov, A., Hernandez, G. M., Daubinet, J. D., Ekhtiari, N., Song, R. J., Dudbridge, P., and Tarakci, E.: A Novel High-Resolution AI-based Global Precipitation Forecasting System, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-355, https://doi.org/10.5194/ems2023-355, 2023.