We start with numerical weather models that produce 10s of billions of forecasted values every day. A machine looks at these values and compares them to the output of a single sensor at a specfic weather station. Over years of looking at the models output and the sensor, the machine learns to forecast for that sensor. This learning is then scaled to many sensors at many weather stations.
On each forecasted page you will see the real-time output from a group of weather stations and the forecasted weather using the above technique. If there is not enough history for certain sensors there may be no forecast as the machine needs more time to learn. At times weather stations go down or have issues, and therefore pages may not return data.
On the Wx Maps page you will see the raw output from our WRF model that is running up to 4x a day.
You will also see a section called "available snowpack models". These are snowpack simulations for available weather stations. They combine the measured weather with variables from model output to create an expected snowpack within the vicinity of each weather station. It includes both a historical and future snowpack forecast. It is updated hourly, and the UI is not mobile friendly. As with all avalanche observations, it should be used with caution. Creating accurate snowpacks from these types of data is extremly difficult, and if you rely too much on it's accuracy when traveling through avalanche terrain you will die.
The Snowfall page contains information using all of the above technologies. Weather models, weather stations, machine learning, and snowpack models work together to give a historical and forecasted snowfall.
A special thanks to the Northwest Avalanche Center. Currently all of the weather station data on this site is driven by there excellent network. Also thanks to the WSL Institute for Snow and Avalanche Research at SLF for their comprehensive snowpack model.
It takes a lot of hardware to crunch these numbers. Thanks to Crystal Mountain Resort for stepping up and providing the funds to scale out to even bigger clusters in the cloud.Contact here: email@example.com