A Hybrid Four-Dimensional Variational Data Assimilation System for the Model for Prediction Across Scales (MPAS-Atmosphere): Leveraging the Joint Effort for Data Assimilation Integration (JEDI)

A global Four-Dimensional Ensemble Variational (4DEnVar) data assimilation system for the Atmospheric component of the Model for Prediction Across Scales (MPAS-A) is presented that uses the Joint Effort for Data assimilation Integration (JEDI). Dual-resolution cycling experiments with a 30 km analysis but an ensemble run at a coarser (60 km) resolution are shown to perform well, thereby reducing the computational cost. Month long global cycling data assimilation experiments show that 4DEnVar updates have lower mean errors in both observation and model space than comparable 3DEnVar experiments. Additional improvements over 4DEnVar are then demonstrated when using Hybrid-4DEnVar, which leverages the benefits of both flow-dependent ensemble covariance and a static climatological covariance, and when assimilating all-sky Advanced Microwave Sounding Unit-A (AMSU-A) radiance observations. 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 versus Hybrid-3DEnVar analyses. Results from extended forecasts show that both forecasts of traditional meteorological fields and precipitation are improved through use of Hybrid-4DEnVar. However, improvements in precipitation forecasts from 4D methods are shown to be most significant in the southern hemisphere, consistent with where the largest improvements in other meteorological fields are found. Significant improvements in precipitation forecasts in the tropics are found in both 3D and 4D experiments assimilating all-sky AMSU-A radiance observations. In summary, 4DEnVar and Hybrid-4DEnVar capabilities are available through MPAS-JEDI—an open-source community developed tool—and perform well during continuous global cycling experiments across traditional verification metrics.