Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

Abstract

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48-h forecasts at 7 km horizontal resolution were generated for a 2000×2000 km domain covering central Europe. Forecasts were made for seven case studies characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76 % of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25-50 % of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced mesoto synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

“…semble members using the Plant-Craig scheme differ from each other more than for the strongly forced cases, where initial and boundary condition variability is relatively more important (Groenemeijer and Craig, 2012). Our result is similar to what was found by Kober et al (2015), where the Plant-Craig scheme was found to perform better than a non-stochastic scheme for a weakly forced case, and at low Figure 7.…”

“…The present study investigates the behaviour of the scheme in a trial of 34 forecasts with the MOGREPS-R ensemble, using a grid length of 24 km. The mass-flux variance produced by the PC scheme is inversely proportional to the grid box area being used, and so it is not obvious from the results of Groenemeijer and Craig (2012) whether the stochastic variations of PC will contribute significantly to variability within an ensemble system operating at the scales of MOGREPS-R. Nonetheless, MOGREPS-R has been shown, in common with most ensemble forecasting systems, to produce insufficient spread relative to its forecast error in precipitation (Tennant and Beare, 2013), suggesting that there is scope for the introduction of a stochastic convection parameterization to be able to improve its performance.…”

“…Two further parameters were adjusted from the study by Keane and Plant (2012), in order to increase this fraction: the mean mass flux per cloud m and the root mean square cloud radius r 2 . Similar changes were made for the same reason by Groenemeijer and Craig (2012) in their mid-latitude tests over land and reflect the fact that the original settings in Plant and Craig (2008) and Keane and Plant (2012) were chosen to match well with cloud-resolving model simulations of tropical oceanic convection. Specifically, the mean mass flux per cloud was reduced here from 2 × 10 7 kg s −1 to 0.8 × 10 7 kg s −1 in order to increase the number of plumes produced by the scheme.…”

“…For example, the current ECMWF model has a global average of about 60 % (Bechtold, 2015). Doubtless the convective precipitation fraction produced by the Plant-Craig scheme in MOGREPS-R could be increased further with stronger changes to parameters, and we remark that Groenemeijer and Craig (2012) set r 2 to 1250 m for their tests, which would likely have such an effect.The convective rainfall fraction will also depend on the details of the host model, its large-scale cloud parameterization and the grid spacing, and the settings of the convective parameterization itself. For example, the Plant-Craig scheme in COSMO has been found to yield a convective fraction of 36 % at 28 km grid spacing in the extra-tropics (Selz and Craig, 2015a), and in ICON it was found to yield a convective fraction of 59 % at 25 km grid spacing, also in the extra-tropics (Tobias Selz, personal communication, 2016).…”

“…Groenemeijer and Craig (2012) examined seven cases using the Consortium for Small-scale Modeling (COSMO) ensemble system with 7 km grid spacing and compared the spread in an ensemble using only different realizations of the PC scheme (i.e. where the random seed in the PC scheme was varied but the members were otherwise identical) with that in an ensemble where additionally the initial and boundary conditions were varied.…”