A Hybrid Four-Dimensional Ensemble Variational (Hybrid-4DEnVar) Data Assimilation System for the Model for Prediction across Scales (MPAS)

A global Four-Dimensional Ensemble Variational (4DEnVar) data assimilation system for MPAS is constructed with the Joint Effort for Data assimilation Integration (JEDI). Global cycling data assimilation experiments with analyses ranging from 120–30 km show that 4DEnVar updates have lower mean errors in both observation and model space than comparable 3DEnVar experiments. Cycling experiments with the ensemble run at a coarser resolution are also conducted and shown to perform well. Additional improvements are then demonstrated when assimilating all-sky AMUS-A observations and when using Hybrid-4DEnVar. Lastly, extended forecasts initialized from the four-dimensional analyses are compared with forecasts initialized from three-dimensional analyses. A particular focus is on the prediction of clouds and precipitation in forecasts initialized from Hybrid-4DEnVar analyses versus Hybrid-3DEnVar analyses. Results from extended forecasts show that both forecasts of traditional meteorological fields and precipitation are improved in Hybrid-4DEnVar forecasts.