Data Assimilation Development for the Proposed 2020 Operational FV3-Based Global Forecast System (GFS)

As part of the Next Generation Global Prediction System (NGGPS), NCEP is replacing the spectral dynamical core of the Global Forecast System (GFS) with the Finite-Volume Cubed-Sphere Dynamical Core (FV3). The initial version of the FV3-based GFS is expected to go into operations in early 2019. This implementation is targeted to contain similar components as operations and run at a comparable resolution. The follow on implementation, currently planned for 2020, is slated to include advances to both the model, such as advanced physics and increased vertical resolution, and the data assimilation system. One of the biggest data assimilation challenges for the 2020 implementation concerns raising the model top from approximately 55 km to 80 km, with the vertical resolution increasing from 64 layers to 127 layers. New static background error statistics need to be derived as there is currently no climatological background error information above the operational model top. The traditional method of using lagged forecast pairs, known as the NMC method (Parrish and Derber 1992), proves challenging due to the extensive model spin up in the highest levels. We will compare statistics derived from the NMC method using initial conditions from another global model with an 80 km top, reducing but not eliminating the spin up, with that derived from using an Ensemble Kalman Filter (EnKF) only system. The impact of the change in the model top also needs to be evaluated for several existing components, such as the normal mode constraint, stochastic physics, and channel selection for satellite radiances. Other data assimilation features being targeted for the 2020 implementation are the 4D incremental analysis update (4DIAU), changing the EnKF solver to the local ensemble transform Kalman filter (LETKF), and moving the EnKF update to the early cycle. This presentation will include details of the static background error generation and the other data assimilation components of the proposed 2020 implementation and as well as preliminary results from a low resolution framework.