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

Abstract

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