Walchensee wind: forecast and measurements

30 days of forecast vs. what actually happened at the lake. Click a single day to see its details.

Last 30 days

Rule-based forecast (actually used)
ML-based forecast — logistic (experimental)
ML-based forecast — HistGradientBoosting (black-box model, experimental)
Actual measurements (session ≥ 1 h)
Mon Oct 12 Tue Nov 10
session (≥ 1 h ≥ 11 kt) marginal (≥ 1 h ≥ 8 kt) no wind no data thunderstorm (forecast rows: predicted · actual row: observed)

Nov 6, 2026

81106:0021:0008:14 · avg 3.2 kt · gust 4.3 kt08:48 · avg 6.3 kt · gust 9.2 kt09:39 · avg 3.3 kt · gust 6.5 kt09:49 · avg 1.1 kt · gust 1.6 kt09:59 · avg 0.9 kt · gust 3.2 kt10:09 · avg 0.6 kt · gust 2.2 kt10:19 · avg 1.3 kt · gust 3.2 kt10:29 · avg 0.7 kt · gust 3.2 kt10:39 · avg 1.4 kt · gust 2.2 kt10:49 · avg 2.0 kt · gust 9.2 kt10:59 · avg 7.8 kt · gust 10.8 kt11:09 · avg 10.0 kt · gust 13.5 kt11:19 · avg 11.8 kt · gust 15.1 kt11:29 · avg 12.8 kt · gust 15.1 kt11:39 · avg 13.8 kt · gust 17.3 kt11:49 · avg 14.8 kt · gust 17.8 kt11:59 · avg 14.0 kt · gust 17.3 kt12:09 · avg 14.7 kt · gust 17.8 kt12:19 · avg 14.8 kt · gust 17.8 kt12:29 · avg 14.8 kt · gust 18.9 kt12:39 · avg 14.8 kt · gust 17.8 kt12:49 · avg 14.7 kt · gust 17.8 kt12:59 · avg 14.4 kt · gust 17.8 kt13:09 · avg 14.0 kt · gust 18.9 kt13:19 · avg 12.6 kt · gust 17.8 kt13:29 · avg 12.4 kt · gust 17.3 kt13:39 · avg 13.1 kt · gust 20.0 kt13:49 · avg 13.4 kt · gust 17.8 kt13:59 · avg 13.3 kt · gust 16.2 kt14:09 · avg 13.6 kt · gust 17.3 kt14:19 · avg 12.9 kt · gust 16.2 kt14:29 · avg 13.8 kt · gust 17.3 kt14:39 · avg 13.6 kt · gust 17.8 kt14:49 · avg 13.3 kt · gust 17.8 kt14:59 · avg 13.2 kt · gust 17.3 kt15:09 · avg 13.8 kt · gust 17.8 kt15:19 · avg 14.0 kt · gust 17.8 kt15:29 · avg 13.3 kt · gust 16.2 kt15:39 · avg 12.4 kt · gust 16.2 kt15:49 · avg 11.8 kt · gust 15.1 kt15:59 · avg 10.5 kt · gust 13.5 kt16:09 · avg 10.1 kt · gust 13.5 kt16:11 · avg 10.3 kt · gust 11.9 kt
avg wind gust ignition (8 kt) session (11 kt)
For Nov 6, 2026 · as written at the time

NO GO

96% daytime cloud + only 350 W/m² solar — no insolation to drive a thermal

🤖 ML — logistic experimental GO GO 49% · MAYBE 18% · NO GO 33%

Strongest inputs — (+) dew-point spread; (−) pressure Δ (thermik), morning solar.

🤖 ML — HGB · black box experimental GO GO 56% · MAYBE 4% · NO GO 40%

Most distinctive inputs: morning solar, Innsbruck pressure, daytime cloud.

Experimental shadow models — they never drive the verdict. How good are they? →

Previous forecast method

How the day was forecast at the time, before the rules were recalibrated — shown for comparison only.