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 β€” logistic (experimental)
ML-based forecast β€” HistGradientBoosting (black-box model, experimental)
Actual measurements (session β‰₯ 1 h)
Mon Oct 12 Tue Nov 10
session (β‰₯ 1 h β‰₯ 11 kt) marginal (β‰₯ 1 h β‰₯ 8 kt) no wind no data thunderstorm (forecast rows: predicted Β· actual row: observed)

Oct 23, 2026

81106:0021:0009:39 Β· avg 4.4 kt Β· gust 6.5 kt09:49 Β· avg 4.1 kt Β· gust 11.9 kt09:59 Β· avg 7.2 kt Β· gust 13.0 kt10:09 Β· avg 5.3 kt Β· gust 9.2 kt10:19 Β· avg 3.5 kt Β· gust 7.6 kt10:29 Β· avg 3.5 kt Β· gust 6.5 kt10:39 Β· avg 3.4 kt Β· gust 6.5 kt10:49 Β· avg 4.3 kt Β· gust 9.2 kt10:59 Β· avg 4.7 kt Β· gust 9.2 kt11:09 Β· avg 4.6 kt Β· gust 9.2 kt11:19 Β· avg 4.8 kt Β· gust 9.2 kt11:29 Β· avg 2.9 kt Β· gust 8.6 kt11:39 Β· avg 8.0 kt Β· gust 15.1 kt11:49 Β· avg 9.7 kt Β· gust 20.0 kt11:59 Β· avg 10.1 kt Β· gust 20.0 kt12:09 Β· avg 10.8 kt Β· gust 16.2 kt12:19 Β· avg 10.9 kt Β· gust 17.8 kt12:29 Β· avg 11.5 kt Β· gust 17.3 kt12:39 Β· avg 10.4 kt Β· gust 16.2 kt12:49 Β· avg 9.1 kt Β· gust 13.0 kt12:59 Β· avg 8.2 kt Β· gust 13.0 kt13:09 Β· avg 8.3 kt Β· gust 11.9 kt13:18 Β· avg 7.2 kt Β· gust 13.0 kt13:21 Β· avg 5.9 kt Β· gust 6.5 kt14:29 Β· avg 14.9 kt Β· gust 20.0 kt14:39 Β· avg 14.1 kt Β· gust 20.0 kt14:49 Β· avg 15.2 kt Β· gust 21.6 kt14:59 Β· avg 14.7 kt Β· gust 18.9 kt15:09 Β· avg 11.2 kt Β· gust 17.3 kt15:19 Β· avg 12.3 kt Β· gust 20.0 kt15:29 Β· avg 11.3 kt Β· gust 16.2 kt15:39 Β· avg 8.6 kt Β· gust 14.6 kt15:49 Β· avg 8.6 kt Β· gust 11.9 kt16:49 Β· avg 8.6 kt Β· gust 8.6 kt16:51 Β· avg 11.1 kt Β· gust 13.5 kt
avg wind gust ignition (8 kt) session (11 kt)
For Oct 23, 2026 Β· as written at the time

NO GO

850 hPa 20 kt from 200Β° (SSE) β€” counters the N-thermal

πŸ€– ML β€” logistic experimental MAYBE GO 21% Β· MAYBE 46% Β· NO GO 34%

Strongest inputs β€” (+) FΓΆhn Ξ”; (βˆ’) morning solar, dew-point spread.

πŸ€– ML β€” HGB Β· black box experimental GO GO 71% Β· MAYBE 0% Β· NO GO 29%

Most distinctive inputs: morning solar, Innsbruck pressure, Bolzano pressure.

Experimental shadow models β€” they never drive the verdict. How good are they? β†’

Previous forecast method

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