  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.
