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Considerations: Fog and Freezing fog

Prediction of fog depends strongly on the simultaneous and accurate prediction of several parameters in the lowest layers of the atmosphere on a spatial scale smaller than the IFS (scale of m rather than km).  Forecast values of temperature and moisture are of prime importance of course, but these in turn depend upon correct assessment and prediction of surface parameters (e.g. soil moisture and the efficiency of nocturnal radiative cooling) and the precise low-level wind and associated turbulent mixing.  In particular the detail of orography is very important in governing local winds (e.g. katabatic winds) and stagnating air in valleys. 

Pinpointing regions of very low visibility is very challenging for IFS, possibly even more so in cases of freezing fog where humidity mixing ratios are smaller and moisture (water and ice) interactions are more complex.  Fog (or freezing fog) may be very patchy in areas where IFS forecasts fog to occur, even those where a high probability of fog is predicted.  It can be more extensive than IFS indicates, but equally patchy fog may not be indicated at all.  

For these reasons jumpiness in forecasts of fog and freezing fog should be expected.  And results from a series of forecasts may well not converge towards a correct distribution or "intensity" of fog, particularly at the lowest visibilities.  At present, there is no clear evidence of any systematic differences in the IFS handling of fog versus freezing fog, although it remains possible that there are some (e.g. biases).

Fog forecasts are quite difficult to verify in the absence of satellite imagery since conventional observations are reports of fog at an individual location, not an area. Also, patchy fog may not be identified.

Sub-grid variability in visibility (due to patchy fog) can potentially be "greater" than it is in for any other meteorological parameter.  Users should always remember that raw IFS forecasts are delivered on the model grid resolution (e.g. currently ~9km boxes for the ensemble).  Compare this with the dimensions of a fog patch, which may be ~10m.  These dimensions differ by 3-4 orders of magnitude.


Fig9.4-6: Forecast visibility at 30hr lead time (left) and 6hr lead time (right) with verifying observations, in a freezing fog situation.  Although there is a good deal of consistency between the forecast fog distribution, there is slightly more fog forecast on the T+6 forecast chart than on the T+30 chart.  However, consistency should not necessarily be taken as a good guide to the true extent of fog.  The large area of fog over north France is captured quite well,  but over Britain observations of fog do not in general match either of the forecast locations at T+30 or T+6 (e.g. East Anglia).   However, fog is often quite patchy and may not be captured by the observations (observations are reports of fog at an individual location, not an area).    Additionally it should be noted that forecast visibility charts show poor visibility where cloud covers hills.

 

   

Fig9.4-7: Forecast probability <200m visibility at 30hr lead time (left) and 6hr lead time (right) with verifying observations for the same freezing fog case as in Fig9.4-6.  In this example the areas of low/moderate probability capture the observed 200m visibility fairly well over England and Wales, and the probability distribution sharpened up as lead time reduced (e.g. over East Anglia), though not clearly in a manner that verified better.  A chart of probability of fog with visibility <200m (or other low thresholds) should only be taken to be very roughly indicative of those areas most at risk.

Note: HRES and Ensemble Control Forecast (ex-HRES) are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast (ex-HRES) output is equivalent to HRES output where shown in the diagrams.   At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.

Users should recognise that:

  • Fog can be very patchy and 10km-scale models (such as IFS) cannot be expected to handle this well. 
  • Areal extent of fog is broadly captured by HRES and Ensemble Control Forecast (ex-HRES) and ensemble member forecasts but at best these are only indicative of areas at risk.  Such predictions do not preclude occurrence outside of the apparent "risk areas".
  • Local skill in forecasting the occurrence of fog (e.g. for a specific site) can be very low.  Local skill in predicting very low visibilities (e.g. meeting motoring warnings threshold criteria) can also be very low.  Beware!
  • Jumpiness in fog forecasts from HRES and Ensemble Control Forecast (ex-HRES) must be expected.  Results may well not show convergence towards the correct solution.  Forecasts with shorter lead times will not necessarily be more skilful that those from longer lead times.

Errors in forecast near-surface data associated with cases of thick fog.

Fig9.4-8: An example of incorrect temperature and dew point forecasts in a case of predicted fog.  Imperfectly modelled mixing processes near the surface induce errors in 2m temperature and humidity.

See also potential cause of poor stratus forecast.


An example of fog prediction by HRES

HRES and Ensemble Control Forecast (ex-HRES) can give good guidance on the development of fog and signal to users the possibility of otherwise unexpected hazardous conditions.  Sea or coastal fog is relatively rare in June in the Mediterranean but HRES was able to predict well ahead of time a significant event in June 2022.  

Fog banks formed as a weak westerly flow brought relatively moist surface layer air across anomalously cold sea surface temperatures near the south coast of Sicily.  HRES captured the development and eastward advance of the fog banks particularly well.   Previous HRESforecasts showed a strong signal for fog banks in the general southern Sicily area, even at four days lead-time.  These gave a useful warning to coastal craft and recreational sailing, especially given the unexpected nature of the event.  Little if any fog was predicted for any other parts of the Mediterranean.

Fig9.4-3 Hour-by-hour visibility HRES predictions around Sicily 16 June 2022 00 to 24 UTC, DT 16 June 2022 T+0).  The colour scale shows the visibility in m.  Note the eastward movement of the fog bank with some detail shown of the changes in area.  Some areas with visibility <100 m (purple) are evident.


Fig9.4-4: Mediterranean sea surface temperatures around Sicily HRES at 00 UTC 16 June 2022.  The colour scale shows the temperatures in ºC

 

Fig9.4-5: Example of visibility meteogram showing forecast visibility in cumulative frequency form.  Colours represent forecast visibility ranges (see scale). 

Note: HRES and Ensemble Control Forecast (ex-HRES) are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast (ex-HRES) output is equivalent to HRES output where shown in the diagrams.   At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.


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