A Hybrid Tangent Linear Model in The Joint Effort for Data Integration (JEDI) system.

Four Dimensional Data Assimilation (4d-Var) has been shown to provide some of the most reliable weather forecasts to date, but is not with out 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 improving TLMs as well as allowing the use of incomplete TLMs when model physics changes. The HTLM uses information for an ensemble run of the nonlinear model to correct TLM trajectories accounting for missing physics. I will also describe its general implementation in JEDI, plans for further development, and challenges of the method.