Scale-dependent weighting and localization for global numerical weather prediction
Various forms of hybrid data assimilation have become standard practice for operational global numerical weather prediction. NCEP has been utilizing hybrid 4DEnVar since 2016 and will continue to do so with the initial implementation of the FV3-based Next Generation Global Prediction System (NGGPS) model in early 2019. Taiwan’s Central Weather Bureau (CWB) has been utilizing a GSI-based hybrid 3DEnVar for several years.
Many hybrid schemes utilize single global weighting parameters to prescribe the contributions from the ensemble and climatological error covariances. Furthermore, the implemented versions of the GSI-based hybrid utilize localization for the ensemble contribution that is Gaussian. Several studies have shown that spectral and scale-dependent localization can be effective within the context of EnVar (Buehner 2012, Buehner and Shlyaeva 2015, Lorenc 2017). Early work with toy models has shown that a similar idea of scale-dependence can be extended to hybrid weights.
Here, we will describe an effort toward applying waveband-dependent localization as well as scale-dependent weighting within a hybrid assimilation paradigm. Results will be shown for low resolution versions of the FV3-based system at NCEP as well as the spectral model used at CWB. Future work regarding quantitatively estimating the scale-dependent weighting and localization parameters will be discussed.
Dr. Catherine Thomas IMSG @ NOAA/NWS/NCEP