Let $(\Omega,\mathscr{F},P)$ be a probability space. Assume for each $n$, $Y_n:\Omega\rightarrow\mathbb{R}$ is a function but $Y_n$ is not necessarily $\mathscr{F}$-measurable. In this case, is it still meaningful to talk about almost sure convergence of $Y_n$? Conceptually, we can define almost sure convergence as $$\exists\hat{\Omega}\in\mathscr{F}\quad\mathrm{such\,\,that}\quad P(\hat{\Omega})=1\quad\mathrm{and}\quad \{Y_n(\omega)\}\,\,\mathrm{converges}\,\,\forall \omega\in\hat{\Omega}.$$
In every probability textbook I have, they all define almost sure convergence for "random variables". But I think what I mentioned might arise naturally in some situations. For example, if for each $n$, $\{X^n_\lambda\}_{\lambda\in \Lambda}$ is a class of random variables where $\Lambda$ is uncountable, then $$Y_n\equiv\sup_{\lambda}X_\lambda^n$$ is not necessarily measurable, but still we sometimes want to talk about convergence property of $\{Y_n\}$.