1.3 Adopting NCEP’s Hybrid 4DEnVar Data Assimilation System to the FV3GFS

Monday, 8 January 2018: 9:30 AM
Ballroom G (ACC) (Austin, Texas)
Rahul Mahajan, EMC, College Park, MD; and D. T. Kleist, C. Thomas, J. S. Whitaker, and R. Treadon

Through an National Weather Service (NWS) led community effort, several dynamical cores were evaluated as part of the Next Generation Global Prediction System (NGGPS) and the Finite-Volume Cubed-Sphere Dynamical Core (FV3) was chosen as the replacement of the Global Spectral Model (GSM) for the upcoming fully coupled weather-scale system. It is computationally efficient, conservative, and non-hydrostatic, making it suitable across the many spatio-temporal scales of the weather and climate predictability. The first step towards this unified system is to replace the spectral dynamical core of the Global Forecast System (GFS) with the new dynamical core. The Gridpoint Statistical Interpolation (GSI), which forms the basis of data assimilation for the GFS needs to be adopted to the new dynamical core as well.

Development of a skeletal prototype FV3GFS system is nearly completed and several components of the legacy GFS system are being incorporated or transitioned into the FV3GFS system including the updates required to the data assimilation system which will be focus of this presentation. Several updates are necessary; e.g. addition of use of stochastic physics to represent model uncertainty, initialization of forecasts with the use of an Incremental Analysis Update (IAU), use of a FV3 climatological static covariance matrix are the primary components. In order to apply the GSI’s Hybrid 4DEnVar scheme to the new model, a static background error for the new model will be computed, one derived from forecasts generated using the FV3 model. Many facets of the new background error, such as the variance, length scales, and regression coefficients for the balance operator, will be compared with the previous background error based on the spectral GFS. Results from experiments quantifying the impact of the new background error will be presented. Performance of the stochastic physics in representing model uncertainty will be demonstrated. Use of initialization through an IAU will be compared without any initializations. A review of a realtime pre-operational beta system will be presented.

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