标题： WeatherBench 概率：一种概率中程天气预报的基准数据集以及深度学习基线模型 显示英文标题

标题： WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models

摘要： WeatherBench是一个用于中程天气预报的基准数据集，包括大地水准面、温度和降水的预处理数据、预定义的评估指标以及若干基线模型。 WeatherBench 概率扩展了这一领域，通过添加一组已建立的概率验证指标（连续排名概率得分、扩散-技能比和等级直方图）以及使用ECWMF IFS集合预报的最先进操作基线来实现概率预测。 此外，我们测试了三种不同的概率机器学习方法——蒙特卡洛丢弃、参数预测和分类预测，在这些方法中，概率分布被离散化。 我们发现，简单的蒙特卡洛丢弃严重低估了不确定性。 参数模型和分类模型都能产生质量相似的可靠预测。 参数模型具有更少的自由度，而当涉及预测非高斯分布时，分类模型更具灵活性。 没有任何模型能够达到操作性IFS模型的技能水平。 我们希望这个基准能够使其他研究人员评估他们的概率方法。 显示英文摘要

摘要： WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined evaluation metrics and a number of baseline models. WeatherBench Probability extends this to probabilistic forecasting by adding a set of established probabilistic verification metrics (continuous ranked probability score, spread-skill ratio and rank histograms) and a state-of-the-art operational baseline using the ECWMF IFS ensemble forecast. In addition, we test three different probabilistic machine learning methods -- Monte Carlo dropout, parametric prediction and categorical prediction, in which the probability distribution is discretized. We find that plain Monte Carlo dropout severely underestimates uncertainty. The parametric and categorical models both produce fairly reliable forecasts of similar quality. The parametric models have fewer degrees of freedom while the categorical model is more flexible when it comes to predicting non-Gaussian distributions. None of the models are able to match the skill of the operational IFS model. We hope that this benchmark will enable other researchers to evaluate their probabilistic approaches.