Across various research domains, remotely-sensed weather products are valuable for answering many scientific questions; however, their temporal and spatial resolutions are often too coarse to answer many questions. For instance, in wildlife research, it's crucial to have fine-scaled, highly localized weather observations when studying animal movement and behavior. This paper harnesses acoustic data to identify variations in rain, wind and air temperature at different thresholds, with rain being the most successfully predicted. Training a model solely on acoustic data yields optimal results, but it demands labor-intensive sample labeling. Meanwhile, hourly satellite data from the MERRA-2 system, though sufficient for certain tasks, produced predictions that were notably less accurate in predict these acoustic labels. We find that acoustic classifiers can be trained from the MERRA-2 data that are more accurate than the raw MERRA-2 data itself. By using MERRA-2 to roughly identify rain in the acoustic data, we were able to produce a functional model without using human-validated labels. Since MERRA-2 has global coverage, our method offers a practical way to train rain models using acoustic datasets around the world.

翻译：在众多研究领域中，遥感天气产品为解答各类科学问题提供了重要价值；然而，其时间和空间分辨率往往过于粗糙，难以满足许多研究需求。例如在野生动物研究中，分析动物运动和行为时，精细化、高本地化的气象观测至关重要。本文利用声学数据识别雨、风、气温在不同阈值下的变化，其中降雨预测的成功率最高。仅基于声学数据训练的模型可获得最优结果，但需要耗费大量人力进行样本标注。而MERRA-2系统提供的逐时卫星数据虽能满足某些任务需求，其预测声学标签的准确性却明显不足。我们发现，基于MERRA-2数据训练的声学分类器，其预测精度甚至高于原始MERRA-2数据本身。通过利用MERRA-2对声学数据中的降雨进行粗略识别，我们得以在不使用人工标注标签的情况下构建出功能模型。鉴于MERRA-2具备全球覆盖能力，本方法为利用全球声学数据集训练降雨模型提供了可行路径。