  Hydrometers that can measure size and velocity distributions of precipitation
are needed for research and corrections of rainfall estimates from weather
radars and microwave links. Existing video disdrometers measure drop size
distributions, but underestimate small raindrops and are impractical for
widespread always-on IoT deployment. We propose an innovative method of
measuring droplet size and velocity using a neuromorphic event camera. These
dynamic vision sensors asynchronously output a sparse stream of pixel
brightness changes. Droplets falling through the plane of focus create events
generated by the motion of the droplet. Droplet size and speed are inferred
from the stream of events. Using an improved hard disk arm actuator to reliably
generate artificial raindrops, our experiments show small errors of 7% (maximum
mean absolute percentage error) for droplet sizes from 0.3 to 2.5 mm and speeds
from 1.3 m/s to 8.0 m/s. Each droplet requires the processing of only a few
hundred to thousands of events, potentially enabling low-power always-on
disdrometers that consume power proportional to the rainfall rate.
