第91回北大MMCセミナー

We discuss the setup of the ensemble data assimilation (EDA) and forecasting systems (EPS)　for high-dimensional dynamical systems with particular focus on numerical weather prediction as carried out operationally at the German Weather Service DWD and its COSMO partners. In particular, we describe the ICON global+mesoscale atmospheric model (two-way nested), 13km/6.5km resolution, with its hybrid ensemble variational data assimilation (LETKF+EnVAR) run on a 3h cycle, and the ensemble prediction system ICON EPS. Second, this system drives the high-resolution ensemble data assimilation system COSMO-KENDA (Kilometer Scale Ensemble Data Assimilation)  with 2.2km operational resolution at DWD and up to 1km resolution at further members of the COSMO consortium (Germany, Switzerland, Italy, Russia, Poland, Romania, Greece and Israel) to provide initial conditions for the high-resolution ensemble forecasting systems, e.g. the operational COSMO-D2-EPS or experimental ICON-LAM EPS. The system is also successfully run on GPU based supercomputers. Further, we show results on the tests of localized adaptive particle filter (LAPF) and a localized Markov chain particle filter (LMCPF), which are being tested for the global model setup, currently in the standard experimental global resolution of 40km. We discuss how to overcome filter collapse or divergence by adaptive rejuvenation, mapping into ensemble space based on spread estimators. We also discuss how to keep balances intact when drawing from probability distributions in combination with localization. Here, we alsoemploy incremental analysis update (IAU) for the ICON model system. Different further versions of particle-filters and hybrid ensemble-particle filters are under test both for ICON on the global scale as well as for COSMO or ICON-LAM on the convective scale in collaboration with colleagues from ETH, Reading and Potsdam.