Revisiting the latent heat nudging scheme for the rainfall assimilation in convective systems

Next-generation, high-resolution numerical weather prediction models require accurate and economical assimilation schemes for radar data. In this study, we reeval- uate the Latent Heat Nudging (LHN) scheme, a simple as- similation scheme for rainfall data, within a meso- NWP model by means of experiments with simulated and radar- derived rainfall observations. The idealised tests indicate that the LHN scheme is well able to capture the dynami- cal structure and the right rainfall amount of a simulated su- percell storm in a perfect environment. Even in degraded environments, the scheme is able to assimilate the storm, but the rainfall amount and distribution can vary consider- ably. Errors in the humidity field primarily impact the pre- cipitation amount in the assimilation cycle, whereas changes in the wind field can cause interferences between the LHN scheme and the storm dynamics leading to a distorted dy- namical structure and rainfall distribution of the assimilated storm. The scheme is quite sensitive to errors in the obser- vations, where position and structure errors are found to be more serious than amplitude errors. A case study of severe convection confirms the potential of rainfall assimilation in reducing the spin-up in the forecast. The information pro- vided by the radar has a beneficial impact in the prediction of precipitation over the entire lifetime of the convective sys- tem.