# Leibniz MMS Days 2018 - Abstract

**Janjic-Pfander, Tijana**

*Key Note Lecture: Challenges of atmospheric data assimilation*

The initial state for atmospheric numerical models is produced by combining available observational data with a short range model simulation using a data assimilation algorithm. This gives us an initial state from which we can run a deterministic model to produce predictions of the future. The main goal of data assimilation is to produce the best analysis (best initial condition) for the numerical model; that is, the estimate that gives the best prediction for the time scales that we are focusing on. This is a challenging problem since high-resolution numerical models of the atmosphere in use today resolve highly nonlinear dynamics and physics, making them in short runs very sensitive to proper initial and boundary conditions.

In this talk, we present the mechanisms of the data assimilation algorithms.. We focus on the ensemble Kalman filter algorithm to estimate the atmospheric state as well as its necessary modifications for our application. Most of the current algorithms used in practice for combining data and previous model forecasts (prior estimates) use Gaussian error assumptions. These assumptions are not appropriate for nonlinear dynamics, since only in the case of linear dynamics will Gaussian errors remain Gaussian in time, not in case of estimating variables that need to be positive or in certain ranges as, for example, rain. Consequently, data assimilation for numerical weather prediction models that resolve many scales of motion and for observations of higher temporal/spatial density/resolution requires re-evaluating and improving the methodology that is currently inherited from less nonlinear applications. We argue that relaxing underlying assumptions of the data assimilation algorithms might be possible by improving the link between the data assimilation and the model. For example, the stronger connection can be established by constraining the analysis with imposing conservation laws and other physical constraints. Applications are illustrated on the convective scale data assimilation example.