Impact of assimilating lidar water vapour and temperature profiles with a hybrid ensemble transform Kalman filter: Three?dimensional variational analysis on the convection?permitting scale

We discuss the analysis impact of ensemble-based assimilation differential absorption lidar observed water vapour and Raman temperature profiles into Weather Research Forecasting model at convection-permitting scale. The flow-dependent background error covariance in data (DA) system that uses hybrid three-dimensional variational (3DVAR) ensemble transform Kalman filter (ETKF) was compared to 3DVAR DA. 3DVAR-ETKF experiment resulted a 50% lower RMSE than when taking assimilated as reference 26% (38%) (temperature) comparing against independent radiosonde observations collocated with site. planetary boundary-layer height analyses ceilometer provided additional evidence improvement. showed 140 m, whereas 60 m. Although limited single case study, we attribute these improvements matrix approach. vertical profile measured from stationary established spatial 100 km radius. This seems indicate future an operational network. persisted 7 hr forecast time 4 GPS observations.