Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts
arXiv:2606.08587v1 Announce Type: cross Abstract: Statistical post-processing has proven to be an effective tool in improving ensemble forecast of different weather variables. Case studies show that post-processing can remedy the typically underdispersive and potentially biased behaviour of the ensemble while optimizing a proper scoring rule expressing the forecast skill. The price of these positive effects is generally a deterioration in sharpness; the width of the central prediction intervals and the uncertainty of the predictions are increasing, especially for shorter lead times. This work
The paper leverages recent advancements in neural network capabilities to address a perennial challenge in ensemble forecasting, suggesting a refinement of existing statistical post-processing methods.
Improved sharpness in weather predictions, especially for shorter lead times, can significantly enhance decision-making across various weather-sensitive sectors, from agriculture to logistics and disaster preparedness.
The application of neural networks could lead to more precise and reliable weather forecasts, mitigating the trade-off between forecast skill and sharpness that traditionally plagues ensemble predictions.
- · Meteorological services
- · Insurance companies
- · Logistics and supply chain
- · Agriculture
- · Traditional statistical post-processing methods
- · Forecasters relying solely on uncalibrated ensemble outputs
More accurate short-range weather predictions become widely available, leading to better operational planning.
Economic benefits accrue from reduced weather-related disruptions and optimized resource allocation.
Increased public trust in weather forecasting, potentially influencing policy decisions related to climate adaptation and infrastructure.
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