SnowML™ 

SnowML™, an open-data, machine learning–driven system for estimation of snow water equivalent (SWE) and seasonal water supply forecasting. The SnowML framework integrates satellite remote sensing, ground-based observations, and atmospheric model parameters to produce a spatially continuous, time-evolving SWE dataset. These data drive a physics-based snowmelt–streamflow model that produces forecasts at cadences ranging from daily to 10‑day to monthly intervals, providing high‑frequency predictions of discharge and accumulated runoff volume for both short‑ and longer‑term planning horizons.

SnowQ®, patent pending, is our weather radar-based remote sensing solution for snow water equivalent (SWE). It enables delivery of  continuous data to snowmelt forecasters and water resource managers.

At the core of SnowQ is technology that integrates data streams from weather radar networks, ground sensor arrays, remote observations and other sources. Using proprietary algorithms to calibrate SWE-aloft to SWE on the ground, the system applies machine-learning techniques to generate accurate, basin-wide SWE maps.