Surf Height: Hawaiian vs. Face

Answer: Hawaiian-scale measures a wave from the side or back and reports roughly half the face height — a local convention that deliberately underreports so surfers sound modest. Face height is the trough-to-crest measurement of the wave the surfer is actually riding. A day described as "4ft Hawaiian" is usually the same day described as "8ft face" or "head high" outside Hawaii. When a forecast omits the convention, face height is the default in the continental US.

Why two conventions exist

Hawaiian measurement started among local North Shore surfers as a way to describe waves modestly. Calling a 20-foot face "10-foot Hawaiian" was partly humility, partly code among locals that outside visitors wouldn’t read as fairly. Over time it calcified as the reporting convention for Hawaiian forecasts and some big-wave outlets.

Face height (also called "face scale" or "face-of-the-wave") is the direct trough-to-crest reading. It’s what Surfline, Magicseaweed, and most US mainland outlets default to.

What NDBC publishes

NOAA NDBC buoys report neither Hawaiian nor face height. They report significant wave height (WVHT) — the average of the top third of wave heights in a 20-minute sample, measured trough-to-crest on the open ocean, in meters or feet. This is the deep-water measurement, before the wave stands up as it reaches shallow water at the break.

As a wave moves into shallow water and stands up to break, it grows taller — often to 1.3×–2× the deep-water WVHT, depending on bottom contour. So a buoy reading of "2m (6.5ft) @ 14s" often breaks as a 4–8ft face on a beach break and taller on a point or reef.

Rules of thumb

  • Hawaiian → face: multiply by about 2. "6ft Hawaiian" ≈ "12ft face."
  • Face → Hawaiian: divide by about 2. "8ft face" ≈ "4ft Hawaiian."
  • Buoy WVHT → face at the break: multiply by 1.3–2× depending on the spot. This is where local knowledge counts.

How LazySurfer handles it

LazySurfer stores the raw NDBC WVHT reading and doesn’t try to convert between conventions. The ML Similarity Score compares apples-to-apples buoy readings against your own logged sessions, so as long as you log consistently (e.g., always mark sessions by face height or always by buoy reading), the model learns your personal scale.

Related terms