KEYWORDS: Climatology, Data modeling, Design and modelling, Environmental sensing, Process modeling, Modeling, Mathematical optimization, Deep learning, Covariance matrices, Covariance
Climate data is of great essence in various fields of researches. As direct observations are limited by sensing resolution and accuracy, a considerable amount of climate data is generated by theoretical computation. However, current mainstream climate models suffer from either limited accuracy or discrete outputs. In this paper, we propose a method of generating climate data by utilizing the properties of the Gaussian Process and carefully designing its kernel structure. Different from previous work, our model considers both temporal and spatial dimensions as inputs, hence being able to generate continuous predictions on certain areas or time intervals and achieve high accuracy. We conduct experiments on the famous ERA5 dataset and compare our method with classic model. Our model outperforms the classic one in terms of error scores in both spatial and temporal tasks, and comparison of uncertainty growth shows our advantage.
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