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.