How much water arrived?
SNOTEL and other trusted stations help constrain water equivalent, precipitation and the conditions of the storm.
Precipitation deliverySteamboat Springs, Colorado
Every mountain
has a pattern.
We’re building the forecasting system that learns it.
Atmospheric guidance. The character of a mountain. Evidence from every storm. Connected to make mountain weather more useful.
Build your mountain list01 / ATMOSPHERE MEETS TERRAIN
Incoming clouds & snow ObservationsMethod illustration
WEATHER / MOUNTAINS / DATA / POSSIBILITYForecast → Observe → Verify → Learn
THE PREMISE 01—04
The atmosphere sets the stage.
The mountain changes the outcome.
Elevation, exposure and the direction of a storm shape where snow falls. A single grid point can miss the relationships that make one ridge, valley or pass different from the next.
Low Pressure Labs builds adaptive forecasting systems for complex mountain terrain. Our method combines physical context with the history of what forecasts predicted and what the mountain actually received.
The forecasting method
Four parts.
One learning loop.
Multiple weather models are the starting point of our planned system. Compare their guidance, learn the mountain, and check the outcome.
MULTIPLE MODELS → A SHARED FORECAST WINDOWMethod illustration
Source guidance Location forecast Observed outcome
Start with multiple models.
Different weather models offer different views of a storm. Our planned Ensemble Engine compares their precipitation, temperature, wind and timing. Agreement and disagreement help describe the range of possibilities; learned weights will depend on the mountain, forecast horizon, storm type and verified performance.
Multiple models are the foundation. How we weight, correct and verify their guidance is the work.
Our architecture, illustrated. The adaptive weighting, mountain correction and calibration layers are under development.
Mountain Truth
A forecast is only as
useful as its reality check.
Snow reports and weather stations describe different parts of a storm. We keep those differences visible.
What depth did it become?
Vetted snow-stake and snow-board reports help constrain new-snow depth and the mountain’s snow-to-liquid behavior.
Snow conversionThe same place.
The same window.
A report is useful only when we know what it measures. The method preserves:
- Location and elevation
- Observation and reset times
- Accumulation window
- Source, revisions and quality
Missing or uncertain evidence stays visible.
Explore the distinction
The same water can make different snow.
Move the snow-to-liquid ratio to see why water amount and new-snow depth need separate attention.
Denser snowLighter snow
Example only: 1 inch of liquid water × ratio. This simplified relationship is not a snow-density forecast.
The standard for improvement
Better is a result.
It has to be measured.
Our verification design preserves the forecast as issued, compares it with evidence from the same period, and tests new methods on storms they have never seen.
Keep the original.
Record the source guidance, LPL version, issue time and forecast window. A later update must never erase the original forecast.
Score like with like.
Compare error and uncertainty against matched observations, individual models, simple blends and earlier LPL versions.
Earn the next version.
Hold out complete storms and seasons. Promote a candidate only when testing supports improvement without unacceptable regressions.
Built around real places
From the first chair
to the mountain pass.
The same scientific question matters to skiers, guides, mountain operations and people who work in exposed terrain: what will happen here?
WHERE WE ARE
Public guidance first. Measured learning next.
The initial app uses direct public forecast guidance for supported U.S. locations. The observation archive, learned corrections and calibrated probabilities are the next research stages. This website explains that methodology; it does not provide a live forecast.
Why start with multiple weather models?
One model may favor an earlier storm arrival; another may predict more precipitation or a higher snow level. Comparing them helps expose those differences instead of hiding them in a single number. Our planned system will weight guidance using evidence of what works for the location and conditions—not simply average every model equally. Model agreement alone does not guarantee accuracy.
This adaptive multi-model blend is under development. The current free version uses direct public forecast guidance, not an operational LPL ensemble.
What makes a Mountain Forecast Profile different?
It brings elevation, terrain, exposure and storm direction together with the history of how models performed at that location. As reliable observations accumulate, the profile can become more specific to that mountain.
What happens at a mountain with little observed history?
Public guidance can still provide a starting point. The learning design allows a data-sparse mountain to borrow statistically supported relationships from similar terrain and regions while building its own record.
How will LPL communicate confidence?
The intended outputs include snowfall timing, expected accumulation, a likely range and useful threshold probabilities. Those probabilities must be tested for calibration; a precise-looking number alone is not evidence of confidence.
Low Pressure Labs / Steamboat Springs, CO
Better data.
Higher days.
Interested in mountain observations, field validation or the forecasting method? Let’s talk.