The Asian summer monsoon anticyclone is a dominant circulation system in the upper troposphere and lower stratosphere (UTLS) in boreal summer (about June–September). An appropriate simulation of the monsoon anticyclone is an important challenge for chemistry climate and chemistry transport models. Here we compare simulations of the ECHAM5/MESSy Chemistry Climate model (EMAC) and the Chemical Lagrangian Model of the Stratosphere (CLaMS) based on the European Centre for Medium-Range Weather Forecasts Reanalysis-Interim (ERA-Interim); EMAC simulations are nudged towards ERA-Interim, whereas transport in CLaMS is driven by ERA-Interim. We employ surface origin tracers for continental South Asia. These surface origin tracers are lifted upward into the Asian summer monsoon anticyclone, both in EMAC and CLaMS. We investigate monsoon conditions for boreal summer 2015. In summer 2015, the entire monsoon, and in particular upward transport in the monsoon anticyclone, was strongly influenced by El Niño. In both models, in 2015, the simulated impact of surface origin tracers on the composition of air in the Asian summer monsoon anticyclone is very weak at 420 K. Further, in both models, a very strong decline with altitude (between ≈ 370–400 K) of surface origin tracers is obvious. The pattern of the Asian monsoon anticyclone in August and early September is represented very similarly in EMAC and CLaMS, with a lower fraction of the surface origin tracer for continental South Asia in CLaMS. The simulated pattern of surface origin tracers in the Asian summer monsoon anticyclone in CLaMS is much less smooth than in EMAC. Finally, we find a strong day-to-day variability in the Asian summer monsoon anticyclone and a confinement of monsoon air at UTLS altitudes (≈370 K to 400 K) similarly in both, EMAC and CLaMS.

The first European Nowcasting and Weather Forecasting Conference (ENWFC-2024) dedicated to nowcasting, seamless forecasting, statistical postprocessing and ensemble prediction took place in Oslo, Norway (Oslo Science Park) from 4 to 8 November 2024, organized by EUMETNET (European National Meteorological and Hydrological Services Network) within the Weather Forecasting Cooperation Programme (E-WFC).More than 70 participants attended the conference in person; and ca. 100 more were joining via livestream.67 conference's presentations (45 oral and 22 posters) were given.The sections in this conference report summarize key topics and discussions of the ENWFC-2024 within: Innovations in satellite and radar technology, early warnings, and crowdsourced data; Advancements in statistical and AI-based weather forecasting; Verification and societal impacts; and Applications.A particular focus was provided to the use of Machine Learning techniques and their applications in the context of the listed key topics.

In this paper, a validation of the torus mapping dealiasing method for observations of Doppler velocities by meteorological radars is presented for the purpose of using dealiased data in data assimilation for operational numerical weather prediction. Analysis was done on a large sample of Doppler velocity data from German and Slovenian radar networks preprocessed by the Operational Program for Exchange of Weather Radar Information (OPERA). The quality of dealiased data is assessed by a comparison with collocated radiosonde and aircraft observations and also through analysis of differences between observations and corresponding values from a regional numerical weather prediction model. Performance of model’s quality control on dealiased data is also evaluated. We show that the torus mapping method is a robust procedure which produces radar datasets of comparable quality to aircraft and radiosonde data and has potential for operational applications in data assimilation.