Determine the physical formulation of convective memory

Determine which physical formulation best represents convective memory, including whether memory should be represented through mechanisms such as recent precipitation, cold-pool evolution, or another prognostic formulation.

Background

The paper explains that conventional approaches to convective memory introduce prognostic variables tied to specific physical mechanisms. Examples include modulating deep-convection entrainment using recent surface precipitation and explicitly tracking cold-pool evolution. These mechanisms offer physically interpretable alternatives to data-driven temporal models, but the paper notes that their relative suitability for representing convective memory has not been established.

Resolving this issue would help determine how prognostic variables should be designed for convection parameterizations and whether a particular physical mechanism can reliably capture the timing and persistence of convective activity across different atmospheric conditions.

References

However, it remains unclear which physical formulation best represents convective memory.

— Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation  (2609.24882 - Schönfeld et al., 21 Sep 2026) in Section 1, Introduction