Aerosol Processes

Haero offers implementations for each of the important stages in the aerosol life cycle. Here we describe the physics of each stage, the approximations made by the associated parametrizations, and the implementations of the underlying prognostic and diagnostic processes.

Each process is independent of all other processes. In other words, state variables are taken directly from the prognostics and diagostics passed to the process, and no assumptions are made about how these state variables were computed.

Aerosol process

An aerosol process computes a tendency, which provides the right-hand-side for a differential equation solved either by Haero or its model. In this sense, an aerosol process is prognostic.

Processes may need intermediate quantities using the available state data and metadata to evaluate their tendencies. These intermediate quantities are diagnostic in the sense they they can be computed, or diagnosed, from the current state variables; they are not evolved in time by either Haero or its host model. They are computed by diagnostic functions; storage of these quantities depends on their use in various aerosol processes and/or the host model.

Diagnostic function

A diagnostic function updates any relevant diagnostic variables in place at simulation time \(t\).

Diagnostic functions are associated with the design of Kokkos kernel functors. In Haero, we apply a design priniciple: each diagnostic function updates exactly 1 diagnostic quantity. So each diagnostic function is launched as a separate kernel.

Independent aerosol processes

Every aerosol process is independent of other aerosol processes. Specifically: a process takes a set of state variables and diagnostics, and using only these variables, it computes a set of tendencies for the prognostic state variables at simulation time \(t\).

This process independence is a dramatic departure from prior implementations of aerosol physics in MAM. Any sequential coupling of processes must be performed by a host model.

With this assumption, it's possible to recover the legacy MAM algorithms by constructing a coupling procedure that follows the relevant assumptions. But it's also possible to construct more sophisticated coupling processes that take advantage of advances in numerical analysis.