This paper presents our winning entry for the EVA 2019 data competition, the aim of which is to predict Red Sea surface temperature extremes over space and time. To achieve this, we used a stochastic partial differential equation (Poisson equation) based method, improved through a regularization to penalize large magnitudes of solutions. This approach is shown to be successful according to the competition’s evaluation criterion, i.e. a threshold-weighted continuous ranked probability score. Our stochastic Poisson equation and its boundary conditions resolve the data’s non-stationarity naturally and effectively. Meanwhile, our numerical method is computationally efficient at dealing with the data’s high dimensionality, without any parameter estimation. It demonstrates the usefulness of stochastic differential equations on spatio-temporal predictions, including the extremes of the process.

本文介绍了EVA 2019数据竞赛的获奖作品，其目的是预测随时间和空间变化的红海表面极端温度。为此，我们使用了基于随机偏微分方程（Poisson方程）的方法，并通过正则化对其进行了改进，以惩罚较大量的解。根据比赛的评估标准，该方法被证明是成功的，即阈值加权连续排名概率分数。我们的随机泊松方程及其边界条件可以自然有效地解决数据的非平稳性。同时，我们的数值方法可以有效地处理数据的高维，而无需任何参数估计。它证明了随机微分方程对时空预测的有用性，包括过程的极端情况。