Recent developments in the data assimilation of AROME/HU numerical weather prediction model

Abstract

A local three-dimensional variational data assimilation (DA) system was implemented operationally in AROME/HU (Application of Research to Operations at Mesoscale) non-hydrostatic mesoscale model at the Hungarian Meteorological Service (OMSZ) in 2013. In the first version, rapid update cycling (RUC) approach was employed with 3-hour frequency in local upper-air DA using conventional observations only. Optimal interpolation method was adopted for the surface data assimilation later in 2016. This paper describes the current developments showing the impact of more conventional and remote-sensing observations assimilated in this system, which reveals the benefit of additional local high-resolution observations. Furthermore, it is shown that an hourly assimilation-forecast cycle outperforms the 3-hourly updated system in our DA. Besides the upper-air assimilation developments, a simplified extended Kalman filter (SEKF) was also tested for surface data assimilation, showing promising performance on both the analyses and the forecasts of AROME/HU system.