Daryl Kleist, University of Maryland-College Park

Scale-dependent weighting and localization for the NCEP GFS hybrid 4D EnVar scheme

Kayo Ide, Rahul Mahajan, Deng-Shun Chen


Following the successful implementation of hybrid 3D EnVar into NCEP operations for the Global Forecast System (GFS) and Global Data Assimilation System (GDAS), significant progress has been made toward the 4D extension of the algorithm. NCEP is planning to implement the hybrid 4D EnVar into operations for the GDAS/GFS sometime in May 2016. A description of the implementation package along with results from extensive testing will be presented.

The current hybrid scheme in use for the NCEP GDAS utilizes single global weighting parameters to prescribe the contributions from the ensemble and static error covariances. Furthermore, the localization applied to the control variable to prescribe the ensemble-based analysis increment is assumed to be Gaussian, with the horizontal localization varying only by vertical level. Several studies have already shown that scale-dependent localization can be more effective within the context of EnVar (Buehner 2012, Buehner and Shlyaeva 2015). Here, we will describe an effort toward applying waveband dependent localization as well as weighting between the static and ensemble contributions within a hybrid assimilation paradigm. Results from a toy model as well as a low-resolution prototype of the NCEP GFS will be presented.

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