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• When a variable level is nominally underground - e.g. for mean sea level pressure in very mountainous areas, or 1000hPa or even 850hPa temperature in very mountainous areas, extreme unrealistic values can develop in the course of a forecast, in some or all ensemble members.  In turn this can manifest as extreme ensemble spread in that particular variable. One problem area is the Andes, but there are others too. These 'glitches' do not always happen, and their onset is not always at the same lead time (e.g. Fig2).
• Some extreme weather events appear less intense than IFS ensemble (e.g. windstorms).
• Temperature extremes are more consistently handled, generally beating classical NWP forecasts.
• The AIFS-ENS contrail has a slight advantage over other AIFS-ENS members because it starts from unperturbed initial conditions.  However, because of the ML training it contains model uncertainty.  It is not the same as IFS ensemble control (Ex-HRES).
• Extremes are better identified than with AIFS-Single.

Fig1Fig2.1.6.2.2-1: An example of an AIFS-ENS meteogram for Warsaw DT00UTC 26 Jun 2025.  The plots are in standard box and whisker form and includes the AIFS-ENS control.  The AIFS single is shown as a continuous blue line. Note AIFS single departs outside the AIFS-ENS box and whisker in places.  This may be due to the difference in the ML derivation of the algorithms for AIFS single and AIFS-ENS.

FIG2.1.6.2.2-2: An example of AIFS-ENS chart output from AIFS-ENS mean and spread VT12UTC 30 Jun 2025, DT12UTC 24 Jun 2025.  Large uncertainty over the southern Andes as high as 8-14C locally. This can be due to 1000hPa contour lying below the model surface in mountainous areas.

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