Synthetic Lee Model
A small, deliberately narrow mountain-wave and lee-hazard sandbox. The aim is to explore how a synthetic ridge responds to different wind, stability, and geometry setups — before attempting anything closer to a real paragliding forecast.
It is not a CFD solver. It is not a calibrated turbulence forecast. The viewer and scores are diagnostic and schematic by design. The goal is pilot-interpretable hazard heuristics anchored in classic mountain-meteorology literature and validated against two well-studied lee-wave cases.
What the model computes
- An asymmetric synthetic ridge, parameterised by height, windward and lee widths.
- Ridge-relative forcing from wind direction and ridge azimuth.
- A layered stability profile: mixed layer, inversion, free atmosphere.
- Lightweight mountain-wave diagnostics from Froude number, Scorer parameter, and terrain shape.
- A Trap Proxy, Rotor Likelihood, and Hazard Score — plus a schematic lee cross-section for visualisation.
The raw benchmark-oriented model layer is kept separate from the paragliding display layer. Raw scores are diagnostic; displayed scores are a pilot-facing interpretation, clamped to the 0–10 m/s wind range that matters for free flight and gated to zero when the schematic lee field stays below a meaningful-signal threshold.
Validation
The model has been checked against two classic lee-wave cases:
- Boulder 11 January 1972 — the canonical strong downslope windstorm. The model reproduces the active baseline response, the weaker oblique response, the collapse under ridge-parallel flow, and the stronger response when the inversion is present.
- MAP IOP 10 (24 October 1999) — a moderate aligned lee-wave case over the Alps. The model reproduces the active-but-moderate aligned response, weaker oblique response, collapse under ridge-parallel flow, and weaker lee and rotor signal when the low-level inversion is removed.
Both harnesses also include a quiet_baseline and a marginal_baseline so the model is checked for not overtriggering on benign days and for keeping a usable middle of its dynamic range.
This doesn't make the model forecast-ready. It means the synthetic logic behaves plausibly against two classic benchmarks.
Explore
Why this matters for paragliding
Lee turbulence — rotors behind ridges when the wind blows across them — is one of the most consistent causes of paragliding incidents in mountain terrain. A pilot’s best defence is staying out of the lee in the first place, which means knowing where and when the lee is active for a given wind and stability profile.
Current public paragliding weather tools handle this crudely: they report wind speed and maybe direction, leaving the lee-hazard call to pilot judgement and local knowledge. This lab is a step toward a model that could, one day, power a lee-hazard layer inside Skyvarg — giving pilots a clear read on which sides of which ridges are likely to be rotor-active on a given day. For now: a sandbox, validated, published as research, and open for the community to kick the tyres on.