Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do not quantify the uncertainty associated with their forecasts. This limits the trust in the model and the usefulness of the forecasts. In this work we construct and formalise a conformal prediction framework as a post-processing method for estimating this uncertainty. The method is model-agnostic and gives calibrated error bounds for all variables, lead times and spatial locations. No modifications are required to the model and the computational cost is negligible compared to model training. We demonstrate the usefulness of the conformal prediction framework on a limited area neural weather model for the Nordic region. We further explore the advantages of the framework for deterministic and probabilistic models.

翻译：神经天气模型已展现出作为基于物理模型的廉价且精确替代方案的巨大潜力。然而，大多数训练用于执行天气预报的模型并未量化其预测相关的不确定性。这限制了模型的可靠性和预测的实用性。在本工作中，我们构建并形式化了一个共形预测框架，作为估计这种不确定性的后处理方法。该方法与模型无关，可为所有变量、预报时效和空间位置提供校准的误差界。无需对模型进行任何修改，且计算成本与模型训练相比可忽略不计。我们在针对北欧地区的有限区域神经天气模型上展示了共形预测框架的实用性。我们进一步探讨了该框架在确定性和概率性模型中的优势。