  The emergence of organized multiscale patterns resulting from convection is
ubiquitous, observed throughout different cloud types. The reproduction of such
patterns by general circulation models remains a challenge due to the complex
nature of clouds, characterized by processes interacting over a wide range of
spatio-temporal scales. The new advances in data-driven modeling techniques
have raised a lot of promises to discover dynamical equations from partial
observations of complex systems.
  This study presents such a discovery from high-resolution satellite datasets
of continental cloud fields. The model is made of stochastic differential
equations able to simulate with high fidelity the spatio-temporal coherence and
variability of the cloud patterns such as the characteristic lifetime of
individual clouds or global organizational features governed by convective
inertia gravity waves. This feat is achieved through the model's lagged effects
associated with convection recirculation times, and hidden variables
parameterizing the unobserved processes and variables.
