This session welcomes papers on applying physical principles and artificial intelligence, or their fusion, to develop, evaluate, and improve numerical weather prediction models. Papers on using physics, machine learning techniques and observations to address research and applications questions in the following topics are highly encouraged:

1) Forecasting and simulating high-impact weather events - research on using advanced artificial intelligence and machine learning techniques to improve numerical weather model prediction of severe weather events (such as winter storms, tropical storms, and severe mesoscale convective storms);

2) Development and improvement of model numerics - basic research on advanced numerical techniques for weather and climate models (such as cloud-resolving global models and high-resolution regional models specialized for extreme weather events on sub-synoptic scales);

3) Development and improvement of model physics - progress in research on advanced model physics parameterization schemes (such as stochastic physics, air-wave-ocean coupling physics, turbulent diffusion and interaction with the surface, sub-grid condensation and convection, grid-resolved cloud and precipitation, land-surface parameterization, and radiation);

4) Verification of model physics and forecast products against theories and observations;

5) Data assimilation systems - progress in the development of data assimilation systems for operational applications (such as reanalysis and climate services), research on advanced methods for data assimilation on various scales (such as treatment of model and observation errors in data assimilation, and observational network design and experiments);

6) Ensemble forecasts and predictability - strategies in ensemble construction, model resolution and forecast range-related issues, and applications to data assimilation;

7) Advances and challenges in applying data from various conventional and avant-garde observation platforms to evaluate and improve high-resolution simulations and forecasting.