[7-1] Invited

On Ensemble and Particle Filters for Numerical Weather Prediction

Roland Potthast (DWD), Andreas Rhodin (DWD), Christoph Schraff(DWD) and Hendrik Reich (DWD)

In almost all operational centres for numerical weather prediction around the world ensemble data assimilation techniques are of rapidly growing importance. Ensemble techniques allow to describe and forecast uncertainty of the analysis, but they also improve the assimilation result itself, by allowing estimates of the covariance or, more general, the prior and posterior probability distribution of atmospheric states.

In our talk, we will first give a survey about recent activities of the German Meteorological Service DWD, who is working towards the use of an Ensemble Kalman Filter both for its new global ICON model as well as for the convective scale high-resolution model COSMO-DE. To be more precise, for the global model a hybrid variational ensemble Kalman filter (VarEnKF) is under development. We survey the setup of its Ensemble Kalman Filter component, which is based on the LETKF of Hunt, with a range of further features such as relaxation to prior perturbations or random perturbations. Then, for the kilometer scale ensemble data assimilation (KENDA) for the 2.8km/2.2km resolution COSMO-DE model we des-cribe the ensemble Kalman filter which is being tuned for operational use and show recent results which demonstrate that it is clearly superior to the current nudging scheme. We will also point to very encouraging results on the performance of the COSMO-DE-EPS when initial conditions for its 40-member ensemble are taken from KENDA.

In collaborations with several universities and the DWD-funded Hans Ertel Centre on Weather Research (HErZ), research groups employ the ensemble Kalman filter of KENDA as a basis for further research on the assimilation of particular observation systems, such as radar reflectivities, MODE-S data or SEVIRI radiances. We give a brief survey about the state of these projects.

In the third part of the talk, we present recent work on the further development of the ensemble data assimilation towards a particle filter for large-scale atmospheric systems, which keeps the advantages of the LETKF, but overcomes some of its limitations. We describe a Localized Markov Chain Particle Filter (LMCPF), present its mathematical foundation and show some tests for simple systems. The implementation of the LMCPF in the KENDA framework of DWD is ongoing work.