Stochastic Tendency Perturbations

Uncertainties within the IFS ensemble system are currently represented by a stochastic perturbation technique (SPPT).  This represents uncertainties due to the model integration, and is used during:

• the execution of the forecast.
• creation of the Ensemble of Data Assimilations (EDA) as part of the representation of uncertainties in the forecast initial conditions.

Stochastically Perturbed Parameterisation Tendencies (SPPT)

SPPT randomly perturbs the tendencies from the physical parameterisation schemes.  This is done to represent uncertainties in the effects of under-resolved processes.  These uncertainties arise from either or both:

• parameterisation scheme assumptions. These incorporate bulk descriptions of sub-grid scale processes active within an individual grid box or column.
• approximations that are necessary to describe poorly constrained processes.

The physics schemes operate through an entire grid box column.  The shape of the unperturbed column of tendencies is preserved by multiplying by a single random number.

SPPT perturbs tendencies but not fluxes and this may cause certain inconsistencies and different behaviour between perturbed and unperturbed forecasts.

Some Limitations of SPPT

The current SPPT scheme:

• tends to focus its perturbations above the boundary layer.  Uncertainties in weather parameters near the surface (e.g. temperature, visibility) may not be well represented.   The ensemble spread for such parameters may be too small.
  
• perturbations do not explicitly depend on the current synoptic pattern.  Occasionally the ensemble may show a very small risk of extreme weather beyond what is synoptically reasonable (e.g. winter maritime convective heating may be dampened and some members may be unrealistically cold).

It should be stressed that overall stochastic perturbations undoubtedly do deliver clear improvements in the ensemble performance.

Additional Sources of Information

(Note: In older material there may be references to issues that have subsequently been addressed)

• Read more on stochastic representation of model uncertainties.
• Read more on stochastic parameterisation and model uncertainty.
• Read more on stochastic methods for representing model uncertainties in the IFS.