The weather and atmospheric composition (AC) are closely related. AC forecasts rely on the correct prediction of the meteorological situation, in particular for the simulation of transport, wet deposition and surface fluxes. Numerical Weather Prediction (NWP) from the short-range to the seasonal range has started using prognostic presentations of aerosols, greenhouse gases and reactive gases in radiation and cloud physics schemes to improve the forecast accuracy. Recognizing the scientific and operational benefits of combining NWP and AC forecasting and data assimilation, integrated AC-NWP systems for global, regional, and local applications have been developed. Most recently machine-learning-based approaches for forecasting the weather and AC have emerged.

We invite contributions on all aspects of forecasting and data assimilation of aerosols, reactive gases, greenhouse gases, and weather or stratospheric dynamics across different time scales. Our focus is on the scientific, computational, and societal advantages of such integrated approaches. Specifically, but not exclusively, we invite papers addressing the following topics:

a) Improved weather predictions due to simulated feedback between aerosols and chemistry in radiation and cloud physics,

b) Improved AC prediction by improved representation of the meteorological variability,

c) Advancements in operational NWP-AC prediction systems, in particular using data-driven machine learning methods,

f) Data assimilation developments for AC and NWP systems,

g) Forecasting of stratospheric composition and dynamics after large volcanic eruptions such as the Hunga-Tonga,

h) Combined impact of environmental hazards on society, such as air pollution and high-impact weather, wildfires, dust storms and the underlying meteorological factors,

i) Evaluation, validation, and applications of NWP-AC prediction systems.

This Session is organized in cooperation with the Copernicus Atmosphere Monitoring Service (CAMS) and the Global Air Quality Forecasting and Information Systems (GAFIS) initiative of the WMO Global Atmosphere Watch (GAW) Program.