A comparison between the Local Ensemble Transform Kalman Filter and the Ensemble Square Root Filter for the assimilation of radar data in convective‐scale models

Abstract

Two ensemble data assimilation methods are used to assimilate Doppler radar observations into a convection‐allowing model. The analyses and subsequent forecasts from the two systems are compared. The Local Ensemble Transform Kalman Filter (LETKF) simultaneously assimilates all observations that can impact the model state at a given location. It is compared to the Ensemble Square Root Filter (EnSRF), which assimilates observations sequentially and has commonly been used for convective‐scale Doppler radar data assimilation. While the filters should behave the same for ideal systems, a comparison between the serial and simultaneous filters has not previously been explored at the convective scale where significant nonlinear effects are present. Observing System Simulation Experiments (OSSEs) are first used to compare the assimilation systems for the analysis and forecast of a supercell thunderstorm. Both the EnSRF and LETKF produce reasonable analyses from the Doppler velocity and reflectivity observations of the true supercell. Small improvements in analysis errors and system noise from the LETKF simultaneous update do not significantly impact the subsequent forecasts. This result is consistent across a range of localization length‐scales and is independent of the manner in which localization is applied. Tests comparing the EnSRF and LETKF for a real‐data case also have small differences. The magnitudes of these differences are similar to those that arise from the sampling variability associated with a finite ensemble. Overall, the results suggest the EnSRF and LETKF approaches are equally capable methods for radar data assimilation at convective scales.