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Topic / title | Description | Related activities / comments |
|---|---|---|
General issues | ||
| G1. Overly smooth forecasts |
| . | A function of the error minimisation training of AIFS is to deliver increasingly smooth fields at longer lead times. Equally there is shortfall in energy at small scales that increases with lead time. One example is strong wind zones around deep cyclones, which get smoothed out, reducing peak values. Another example area would be objective fronts - identification of such requires the thermal gradient nearby to exceed a threshold; then in practice total length of such fronts reduces with forecast lead time as gradient peaks get smoothed out. | Whilst this behaviour is also a well-known characteristic of an ensemble mean, the issue is less pronounced in AIFS. Plus, successive AIFS implementations have managed to further reduce the smoothing effect. |
| G2. Underestimation of small-scale extremes | ||
| G3. Parameter consistency | ||
Cloud cover | ||
| Issue over arabian pin | ? | |
| Under-dispersive distribution |
2m temperature | ||
T1. Consistency issues | AIFS is primarily trained using ERA5 data. For the 2m temperature component the offline land-data assimilation 2m temperature field is used. This uses 2m temperature observations, which can sometimes be inconsistent with the overlying atmosphere simulation in ERA5 which does not use those observations. One example would be challenging winter-time low cloud/fog. In such a case the cloud cover could be inconsistent with 2m temperature within AIFS output. |