Experiments with the modified Rotating Shallow Water model (modRSW, v.1.0): assessing the relevance for convective-scale data assimilation research

Abstract. Following the development of the modified rotating shallow water model (modRSW), which includes simplified dynamics of convection and precipitation, we present the results of a series of idealised forecast-assimilation experiments to demonstrate its relevance for data assimilation research at convective scales, focusing on its ability to imitate an operational Numerical Weather Prediction system. We address in a rigorous manner how to ascertain whether an idealised model is relevant for data assimilation research by comparing our modRSW model configuration – based on a twin-setting approach in which synthetic observations are assimilated hourly into a Deterministic Ensemble Kalman filter – with the most common properties of an operational system. We demonstrate forecast-assimilation experiments that produce values of error growth rates (6–9 hours) and observational influence on the analyses (around 30 %) comparable to those found in operational systems. In addition, we provide a description of the approach we took to reach a well-tuned configuration by comparing the ensemble spread with the Root Mean Square Error of the ensemble mean, and examining the Continuous Ranked Probability Score. We also provide subjective assessments of forecasts at different lead times.