Abstract/Summary

The prediction of convection (in terms of position, timing, and strength) is important to achieve in high-resolution weather forecasting. This problem not only requires good convective-scale models, but also data assimilation systems to give initial conditions which neither improperly hinders nor hastens convection in the ensuing forecasts. Solving this problem is difficult and expensive using operational-scale numerical weather prediction systems, and so a simplified model of convective scale flow is under development. This paper extends the “ABC model” of dry convective-scale flow to include mixing ratios of vapour and condensate phases of water. This revised model is called “Hydro-ABC”. Hydro ABC includes transport of the vapour and condensate mixing ratios within a dynamical core, and transitions between these two phases via a micro-physics scheme. A saturated mixing ratio is derived from model quantities, which helps determine whether evaporation or condensation happens. Latent heat is exchanged with the buoyancy variable (ABC’s potential temperature-like variable) in such a way to conserve total energy, where total energy is the sum of dry energy and latent heat. The model equations are designed to conserve the domain-total mass, water, and energy. An example numerical model integration is performed and analysed, which shows the development of a realistic looking anvil cloud, and excitation of inertio-gravity and acoustic modes over a wide range of frequencies. This behaviour means that Hydro-ABC is a challenging model to allow experimentation with innovative data assimilation strategies in the next stage of work. Further, an ensemble of Hydro-ABC integrations is also performed in order to study the possible forecast error covariance statistics (necessary for data assimilation). These show patterns that are dependent on the presence of convective activity (at any model’s vertical column), thus giving a taste of flow-dependent error statistics. Candidate indicators/harbingers of convection are also evaluated (namely relative humidity, hydrostatic imbalance, horizontal divergence, convective available potential energy, and convective inhibition), which appear to be reliable diagnostics concerning the presence of convection. These diagnostics will be useful in the selection of the relevant forecast error covariance statistics when data assimilation for Hydro-ABC is developed.