# A local ensemble transform Kalman particle filter for convective scale data assimilation

## TL;DR

This paper introduces a new hybrid ensemble data assimilation algorithm combining Kalman and particle filters, designed for high-resolution weather models to better handle non-Gaussian phenomena like convection.

## Contribution

It proposes a fully ensemble-space hybrid algorithm with a deterministic scheme and a novel criterion for balancing Kalman and particle updates, enhancing non-Gaussian data assimilation.

## Key findings

- Feasible in quasi-operational convective-scale setups
- Effective for non-Gaussian variables like wind and precipitation
- Shows potential for improved high-resolution weather forecasting

## Abstract

Ensemble data assimilation methods such as the Ensemble Kalman Filter (EnKF) are a key component of probabilistic weather forecasting. They represent the uncertainty in the initial conditions by an ensemble which incorporates information coming from the physical model with the latest observations. High-resolution numerical weather prediction models ran at operational centers are able to resolve non-linear and non-Gaussian physical phenomena such as convection. There is therefore a growing need to develop ensemble assimilation algorithms able to deal with non-Gaussianity while staying computationally feasible. In the present paper we address some of these needs by proposing a new hybrid algorithm based on the Ensemble Kalman Particle Filter. It is fully formulated in ensemble space and uses a deterministic scheme such that it has the ensemble transform Kalman filter (ETKF) instead of the stochastic EnKF as a limiting case. A new criterion for choosing the proportion of particle filter and ETKF update is also proposed. The new algorithm is implemented in the COSMO framework and numerical experiments in a quasi-operational convective-scale setup are conducted. The results show the feasibility of the new algorithm in practice and indicate a strong potential for such local hybrid methods, in particular for forecasting non-Gaussian variables such as wind and hourly precipitation.