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

Nov 5, 2026

81106:0021:0008:33 ยท avg 8.1 kt ยท gust 9.2 kt09:19 ยท avg 6.3 kt ยท gust 8.6 kt10:03 ยท avg 6.2 kt ยท gust 7.6 kt10:47 ยท avg 4.3 kt ยท gust 4.9 kt11:39 ยท avg 10.6 kt ยท gust 13.5 kt11:49 ยท avg 11.5 kt ยท gust 15.1 kt11:59 ยท avg 12.9 kt ยท gust 16.2 kt12:09 ยท avg 14.4 kt ยท gust 20.0 kt12:19 ยท avg 16.0 kt ยท gust 21.6 kt12:29 ยท avg 17.2 kt ยท gust 21.6 kt12:39 ยท avg 16.6 kt ยท gust 20.5 kt12:49 ยท avg 16.4 kt ยท gust 20.0 kt12:59 ยท avg 16.2 kt ยท gust 20.5 kt13:09 ยท avg 16.1 kt ยท gust 20.5 kt13:19 ยท avg 15.9 kt ยท gust 21.6 kt13:29 ยท avg 15.8 kt ยท gust 20.5 kt13:39 ยท avg 15.9 kt ยท gust 20.5 kt13:49 ยท avg 16.9 kt ยท gust 20.5 kt13:59 ยท avg 16.6 kt ยท gust 21.6 kt14:09 ยท avg 16.9 kt ยท gust 22.1 kt14:19 ยท avg 16.4 kt ยท gust 21.6 kt14:29 ยท avg 16.5 kt ยท gust 21.6 kt14:39 ยท avg 16.6 kt ยท gust 20.5 kt14:49 ยท avg 15.8 kt ยท gust 20.0 kt14:59 ยท avg 15.6 kt ยท gust 18.9 kt15:09 ยท avg 15.1 kt ยท gust 18.9 kt15:19 ยท avg 14.7 kt ยท gust 20.0 kt15:29 ยท avg 13.6 kt ยท gust 17.8 kt15:39 ยท avg 11.8 kt ยท gust 17.3 kt15:49 ยท avg 11.4 kt ยท gust 15.1 kt15:59 ยท avg 11.3 kt ยท gust 16.2 kt16:09 ยท avg 10.4 kt ยท gust 14.6 kt16:19 ยท avg 10.9 kt ยท gust 14.6 kt16:29 ยท avg 11.7 kt ยท gust 14.6 kt16:39 ยท avg 12.0 kt ยท gust 17.3 kt16:43 ยท avg 11.9 kt ยท gust 14.6 kt
avg wind gust ignition (8 kt) session (11 kt)
For Nov 5, 2026 ยท as written at the time

MAYBE

dew-point spread 2.3ยฐC โ€” air too moist, solar energy lost to evaporation

๐Ÿค– ML โ€” logistic experimental GO GO 50% ยท MAYBE 29% ยท NO GO 21%

Strongest inputs โ€” (+) Bolzano pressure; (โˆ’) morning solar, pressure ฮ” (thermik).

๐Ÿค– ML โ€” HGB ยท black box experimental NO GO GO 29% ยท MAYBE 13% ยท NO GO 58%

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

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

Previous forecast method

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