Section 9.4 Visibility

• New Section9.4.1 Reduction of visibility in fog
• New Section9.4.2 Reduction of visibility in dust and sand

Note: HRES and Ensemble Control Forecast are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast 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.

Visibility within IFS

As a weather hazard, fog is extremely important, but a difficult variable to predict.  The visibility diagnostic includes information on the reduced visibility in fog.

Correctly forecasting very low visibility depends upon predicting the correct dynamic and thermodynamic conditions in the boundary layer.   These can be highly variable in space and time.  Also they are often tied to orographic features that are not resolved by the atmospheric model.   For these reasons a probabilistic approach using the ensemble members is more useful.

IFS forecasts of low St and fog depend upon correct and detailed analysis and modelling of the lowest layers of the model atmosphere.  This depends upon valid data being available from:

• a relevant radiosonde ascent.
• data from surface observations.
• sea temperatures (for sea and coastal areas).
• winds near the surface.

Forecast visibility:

• can fall below 500m where fog is forecast due to cloud water drops in the lowest model layer.
• may often be too high in convective precipitation as this is not taken into account.  Snow showers and heavy rain showers can reduce visibility considerably in reality.  Large-scale precipitation is unaffected as this taken into account during visibility calculations.
• in clean air in general seems rather too great.  Visibility can not be greater than 100km as this reflects the extinction coefficient of clean air used by the calculation.

Before accepting forecast areas of low St or fog, users should assess how well the boundary has been modelled by IFS.   Compare the analysed and observed structure of the boundary layer and assess critically the validity of the data used.  Low level winds and turbulent effects can also play a part.

The visibility diagnostic is an experimental product introduced in 2016.  The quality of this product is undergoing continued evaluation with the aim of improving the diagnostic and its usefulness.  It should be used with caution; expectations regarding the quality of this product should remain low.  Visibility products are currently made available so that they be evaluated and also to allow users to gain experience.  Users are advised to keep themselves updated about visibility products through the ECMWF Newsletter and web site. 

Visibility in the atmospheric model is defined as the near surface horizontal visibility within the lowest 20m layer above the surface and is calculated using an exponential scattering law.

The visual range is taken as the distance at which, despite extinction of light by the atmospheric model fog, it would be possible to distinguish between a theoretical object and the model's foggy background.  In the model, the contrast between object and foggy background is taken as 2% difference in luminosity (a liminal contrast of 0.02).  The extinction coefficients are calculated for the contributions from precipitation and cloud water droplets in the lowest layer of the model and also the presence of climatologically and seasonally varying aerosol species. 

The current technique for visibility forecasting has several limitations:

• It uses a fixed particle size for cloud and precipitation particles.
• The effects caused by local deviations of the aerosol fields from climatological values and the interactions of fog and aerosol particles are not modelled yet.
• Horizontal resolution is relatively low (9km).
• The lowest level in the vertical is 10m so capturing the detailed extent and composition is difficult, especially in rugged areas.

 Visibility calculation using a ‘tuned’ CAMS aerosol climatology was introduced in mid 2017 in Cycle 43R3.