Contributed by Laura Baker
Ensemble weather forecasts are used to represent the uncertainty in the forecast, rather than just giving a single deterministic forecast. In a very predictable system, all the ensemble members typically follow a similar path, while in an unpredictable system, the ensemble may have a large divergence or spread between members.
A simple way to create an ensemble is to perturb the initial conditions of the forecast. Since the atmosphere is a chaotic system, a small perturbation can potentially lead to a large difference in the forecast. However, just perturbing the initial conditions of the forecast is sometimes not enough, and these ensembles can often be underspread, which means that they do not cover the full range of possible states that could occur. This means that the ensemble forecast could miss what actually occurs in observations. One way to further increase the spread of the ensemble is to add some representation of model error, or model uncertainty, into the forecast. Model uncertainty becomes relatively more important as you go down to smaller scales, so in a high-resolution ensemble it is more important to include these effects.
A recent study as part of the DIAMET project aimed to investigate the effects of randomly perturbing individual parameters in the forecast model as a way of representing model error. We used a configuration of the Met Office Unified Model with a resolution of 1.5 km and a domain covering the southern part of the UK. We generated an ensemble with one control member and 23 perturbed members. The initial conditions for each ensemble member came from a global ensemble forecast with a lower resolution (60 km). Since our domain is a sub-domain of the global model, the lateral boundary conditions are also derived from the global model forecast, and each ensemble member has perturbed boundary conditions corresponding to their initial condition perturbations.
We focussed on a single case study which occurred during one of the DIAMET field campaign periods. This case was particularly interesting from an ensembles perspective because it involved the passage of a frontal rain band with an interesting banded structure which was not well represented in the operational forecast. None of the individual ensemble members captured the two separate rain bands, but some of them had rain in the location of the second band.
We perturbed parameters in the boundary layer and microphysics parameterisation schemes. 16 parameters were chosen to be perturbed, which were known by experts to have some uncertainty in their values. We perturbed each parameter randomly within a certain range, and each ensemble member had different random perturbations applied to its parameters. We focussed our analysis on near-surface variables (wind speed, temperature and relative humidity) which could be compared with observations from surface stations, and rainfall rate and accumulation (which could be compared with radar observations). We found that for the near-surface variables, representing model error using this method improved the forecast skill and increased the spread of the ensemble. In contrast, for the rainfall the forecast skill and ensemble spread were degraded by this method after the first couple of hours of the forecast.
This study is a useful first step to developing a high-resolution ensemble system with a representation of model error. This work was recently published in Nonlinear Processes in Geophysics and can be accessed here.