Ensemble-based methods for data assimilation and emission inversions are a
popular way to encode flow-dependency within the model error covariance. While
most ensemble methods do not require the use of an adjoint model, the need to
repeatedly run a geophysical model to generate the ensemble can be a
significant computational burden. In this paper, we introduce EnsAI, a new
AI-based ensemble generation system for atmospheric chemical constituents. When
trained on an existing ensemble for ammonia generated by the GEM-MACH air
quality model, it was shown that the ensembles produced by EnsAI can accurately
reproduce the meteorology-dependent features of the original ensemble, while
generating the ensemble 3,300 times faster than the original GEM-MACH ensemble.
While EnsAI requires an upfront cost for generating an ensemble used for
training, as well as the training itself, the long term computational savings
can greatly exceed these initial computational costs. When used in an emissions
inversion system, EnsAI produced similar inversion results to those in which
the original GEM-MACH ensemble was used while using significantly less
computational resources.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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