  Prediction of chaotic systems relies on a floating fusion of sensor data
(observations) with a numerical model to decide on a good system trajectory and
to compensate nonlinear feedback effects. Ensemble-based data assimilation (DA)
is a major method for this concern depending on propagating an ensemble of
perturbed model realizations.In this paper we develop an elastic, online,
fault-tolerant and modular framework called Melissa-DA for large-scale
ensemble-based DA. Melissa-DA allows elastic addition or removal of compute
resources for state propagation at runtime. Dynamic load balancing based on
list scheduling ensuresefficient execution. Online processing of the data
produced by ensemble members enables to avoid the I/O bottleneck of file-based
approaches. Our implementation embeds the PDAF parallel DA engine, enabling the
use of various DA methods. Melissa-DA can support extra ensemble-based
DAmethods by implementing the transformation of member background states into
analysis states. Experiments confirm the excellent scalability of Melissa-DA,
running on up to 16,240 cores, to propagate 16,384 members for a regional
hydrological critical zone assimilation relying on theParFlow model on a domain
with about 4 M grid cells.
