用深度学习生成连续降水代理变量，提升气象模拟精度。
Continuous latent representations for modeling precipitation with deep learning
- 构建正态分布的伪降水场替代原始数据
- 实现1°到0.25°分辨率的降水降尺度
- 适合气候建模与水文应用研究者
降水数据具有稀疏性和时空不连续性，给模拟及偏差校正、降尺度处理带来挑战，包括间歇性与极端值表征不准（对水文应用至关重要）、重网格化时出现吉布斯现象、缺乏细尺度细节。为应对这些问题，常用方法是将降水变量非线性变换为更易处理的形式。本文探索利用深度学习生成一种平滑、时空连续的代理变量以模拟降水数据。我们提出一个正态分布场，称为伪降水（PP），作为降水模拟的替代变量。通过将其应用于从1°（约100公里）到0.25°（约25公里）的降水降尺度，验证了该变量的实际适用性。
原文摘要 · Abstract (English)
The sparse and spatio-temporally discontinuous nature of precipitation data presents significant challenges for simulation and statistical processing for bias correction and downscaling. These include incorrect representation of intermittency and extreme values (critical for hydrology applications), Gibbs phenomenon upon regridding, and lack of fine scales details. To address these challenges, a common approach is to transform the precipitation variable nonlinearly into one that is more malleable. In this work, we explore how deep learning can be used to generate a smooth, spatio-temporally continuous variable as a proxy for simulation of precipitation data. We develop a normally distributed field called pseudo-precipitation (PP) as an alternative for simulating precipitation. The practical applicability of this variable is investigated by applying it for downscaling precipitation from \(1\degree\) (\(\sim\) 100 km) to \(0.25\degree\) (\(\sim\) 25 km).
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