Abstract : H.1
Radar rainfall assimilation and short-range QPF in a high-resolution Ensemble Prediction System

Christian Keil, Daniel Leuenberger, George Craig
christian.keil@dlr.de
Institut für Physik der Atmosphäre

Summertime convection is generally hard to predict by deterministic regional Numerical Weather Prediction (NWP) systems. Assimilation of radar data in high-resolution models is an efficient way to trigger convection at the right time and location and can improve particularly the initial conditions for quantitative precipitation forecasts (QPF). However, experience suggests that the introduced radar information is only successfully retained in the free forecast, if the mesoscale environment supports convection.

In this study we examine the role of the environment in the success of high-resolution radar rainfall assimilation with the Latent Heat Nudging technique and short-range QPF in a summer convection case using a two-step ensemble approach: Mesoscale ensemble forecasts are produced using the COnsortium for Small-scale MOdelling Limited-area Ensemble Prediction System (COSMO-LEPS), in which the global ECMWF EPS provides initial and boundary conditions for the high-resolution non-hydrostatic Lokal-Modell (LM; dx=7km). This ensemble drives a high-resolution (dx=2.8km) LMK ensemble wherein conventional data and radar-derived surface rainfall are assimilated.

Every 7km member provides a different meso-scale representation of the convective environment and allows exploring the relation between the quality of the driving member and the skill of the model rainfall during assimilation and short-range forecast of its nested high-resolution counterpart.

The benefit of the ensemble approach over a deterministic forecast is examined and implications for a possible next-generation ensemble forecasting system with a best member selection based on remote sensing data are discussed.