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OceanData Consulting
Public measurement · daily

Metocean AI Scoreboard

Every day, a post-processing AI is compared with the official physical forecast on a panel of French wave buoys, tide gauges and wind stations. Results are published as they are, including where the AI loses.

Gap to the physical forecast

Forecast issued —

Scoreboard — Figures not displayed

Measurements are read when the page opens, from the pipeline’s public repository; they are never frozen into this page. They could not be obtained here — network unavailable, file not matching the contract, or page read without JavaScript. Nothing is shown rather than a doubtful figure.

Check the public repository status

Réseau indisponible (scores.json)

Part of the displayed gap is only a correction of the baseline’s constant bias, and the two tide gauges are still trained on reanalysis wind although they serve forecasts: their figures are therefore rather optimistic. The limits are detailed on the method page. The 48 h exceedance probabilities of all stations are gathered on the alerts page.

Reading guide

What this table measures

Three clarifications without which the figures above mean nothing.

Comparison with the official forecast

The reference is not a convenient baseline: it is the physical forecast served operationally. For waves as for wind, each station is compared with the model that forecasts it best — Météo-France MFWAM, DWD EWAM or NCEP GFS-Wave for waves, Météo-France ARPEGE, ECMWF IFS or DWD ICON-EU for wind, depending on the site and named in the table. For water level, the harmonic tidal analysis.

Stations below the threshold stay displayed

A station where the AI does not beat the physics stays tracked and listed, without an AI figure. Removing it from the table would make the others unverifiable. The table above names the ones currently in this case.

Measured every day, never embellished retroactively

Each forecast is compared with the observations that arrive afterwards. Days without usable observations stay visible as such, and enter no average.

Method, sources and limits · Archived review of 9 September 2026 · Raw data and pipeline code

Custom deployment & evaluation

Want to evaluate a model on your buoys or your coastline?

We set up a blind evaluation protocol on your in situ data and train AI post-processing calibrated to correct your local forecasts.

Blind protocolConfidential in situ dataQuantified report within 5 days

Published station pages

Directory generated on 2026-10-10 from stations.json. Measurements and verdicts are loaded live on each page and are not frozen in this document.