Snow is an essential input for various land surface models. Seasonal snow estimates are available as snow water equivalent (SWE) from process-based reanalysis products or locally from in situ measurements. While the reanalysis products are computationally expensive and available at only fixed spatial and temporal resolutions, the in situ measurements are highly localized and sparse. To address these issues and enable the analysis of the effect of a large suite of physical, morphological, and geological conditions on the presence and amount of snow, we build a Long Short-Term Memory (LSTM) network, which is able to estimate the SWE based on time series input of the various physical/meteorological factors as well static spatial/morphological factors. Specifically, this model breaks down the SWE estimation into two separate tasks: (i) a classification task that indicates the presence/absence of snow on a specific day and (ii) a regression task that indicates the height of the SWE on a specific day in the case of snow presence. The model is trained using physical/in situ SWE measurements from the SNOw TELemetry (SNOTEL) snow pillows in the western United States. We will show that trained LSTM models have a classification accuracy of $\geq 93\%$ for the presence of snow and a coefficient of correlation of $\sim 0.9$ concerning their SWE estimates. We will also demonstrate that the models can generalize both spatially and temporally to previously unseen data.

翻译：雪是各类地表模型的关键输入参数。季节性积雪估算可通过基于过程的再分析产品以雪水当量（SWE）形式获取，或通过现场测量在局部地区获得。然而，再分析产品计算成本高昂且仅提供固定的时空分辨率，而现场测量则高度局部化且分布稀疏。为解决这些问题，并实现对大量物理、形态和地质条件对积雪存在及雪量影响的分析，我们构建了一个长短期记忆（LSTM）网络。该网络能够基于多种物理/气象因素的时间序列输入以及静态空间/形态因素来估算SWE。具体而言，该模型将SWE估算分解为两个独立任务：（i）分类任务：判断特定日期是否存在积雪；（ii）回归任务：在存在积雪的情况下估算特定日期的SWE高度。模型使用来自美国西部雪层遥测（SNOTEL）雪枕的物理/现场SWE测量数据进行训练。实验表明，训练后的LSTM模型在积雪存在性分类任务中准确率≥93%，SWE估算的相关系数达到~0.9。我们还将证明该模型在空间和时间维度上均能泛化至未见数据。