High-resolution radar rainfall assimilation: Exploratory studies with latent heat nudging

Precipitation is certainly one of the most relevant parameters in forecasting the weather, particularly in numerical weather prediction (NWP). Yet, while general NWP has witnessed considerable progress in recent years, quantitative precipitation forecasting (QPF) has shown little to no improvement, especially for the convection-dominated warm season. Reasons for the low performance include model deficiencies, insufficient resolution, and the lack of a properly defined initial model state, particularly related to moisture. Next-generation operational NWP systems will run with grid spacings of around 1 km to circumvent the parametrisation of convection. Such model resolution demands new types of high-resolution observations for initialisation. Doppler radars are able to provide information on precipitation-related phenomena with sufficient spatio-temporal resolution for this purpose. In order to meet the tight time constraints in short-range NWP, very efficient radar assimilation methods will be essential. Latent Heat Nudging (LHN), a simple scheme for rainfall assimilation, fulfills this requirement. However, despite the LHN’s success at larger scales it has not been thoroughly tested in high-resolution applications so far. LHN seeks to adjust the model’s buoyancy in order to match the observed precipitation intensity and, therefore, seems to be a natural candidate for the assimilation of convective weather systems. The present thesis attempts to re-evaluate and characterise the LHN scheme within a meso-γ scale NWP model. Emphasis is put on the extent to which LHN can introduce precipitation and small scale flow features into the initial state of a forecast and improve short-range QPF. To this end assimilation simulations of idealised and real severe convective systems are carried out. In a first study the LHN scheme is applied within the framework of an idealised supercell type storm, providing ’pseudo rainfall observations’ for the assimilation simulations. Consideration is given to the model’s response to the forcing as well as to the LHN’s sensitivity to the uncertainty in the observations and the environment. The scheme is found to successfully capture the dynamical storm structure and reproduce the appropriate rainfall amount in a perfect environment, while a bias in the environmental humidity or wind field can render a successful assimilation difficult. Further, a pronounced sensitivity to the observation insertion frequency points to the need of high update rates for the radar observations, particularly for cases with high propagation velocities. A second study explores the performance of the LHN scheme in high-resolution simulations of two severe convection cases in Switzerland using rainfall data from the Swiss Radar Network. LHN successfully provides the trigger for convection at the right position and matches the model precipitation closely with that observed during assimilation. A generally realistic response of the model dynamics to the precipitation forcing, such as cold pools and gust fronts, is found during the assimilation period. The performance of free precipitation forecasts strongly varies between the two cases. The stability of the storm environment plays an important role in the evolution of the convection once the LHN forcing is stopped. The LHN scheme appears to be sensitive to the amplitude of the radar-derived precipitation in that it may introduce a too cold and overestimated convective outflow, as well as a phase shift of some 40-50 km of the gustfront during the assimilation period when using rainfall observations with a positive bias of factor 3.