The Hybrid Tangent Linear Model in JEDI, Generic Linearization Error Reduction and 4d-Var with Multiple Models

4d-Var has been shown to provide some of the most reliable weather forecasts to date, but is not without its pitfalls. In particular, 4d-Var depends heavily on a tangent linear model (TLM) and an adjoint to the tangent linear model. While conceptually simple, coding these two elements is extremely time intensive and difficult. A small change in the larger weather model can induce months of work on its TLM and adjoint delaying the benefits of improvements on the model side. In this talk I will introduce the Hybrid Tangent Linear Model (HTLM), developed in [Payne 2021], which is aimed at avoiding these pitfalls as well as present a generic implementation of it in the JEDI system. Because of that generic implementation, I will also highlight the reduction in linearization errors we observe using several different models, GEOS, GFS, and SOCHA. The HTLM works by leveraging any available incomplete TLM (perhaps dynamics only) along with a nonlinear ensemble, by first forwarding an ensemble of perturbations with it and then sampling on vertical columns to find coefficients for a corrective update of the TLM forwarded perturbation. The generic implementation of the HTLM in JEDI provides the opportunity for any model with a JEDI interface to do 4d-Var assimilation with it.