Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts
Researchers have developed a new method to improve the sharpness of neural network-based ensemble weather forecasts. By adding a penalty term to the network's loss function, they can reduce the width of prediction intervals without sacrificing forecast accuracy. This technique was demonstrated using 2m temperature forecasts from the European Centre for Medium-Range Weather Forecasts, showing a significant decrease in prediction interval width. AI
IMPACT Enhances accuracy and reliability of weather prediction models, potentially improving disaster preparedness and resource management.