English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Efficient downscaling of large ensembles of coarse-scale information iscrucial in several applications, such as oceanic and atmospheric modeling. Thedetermining form map is a theoretical lifting function from the low-resolutionsolution trajectories of a dissipative dynamical system to their correspondingfine-scale counterparts. Recently, a physics-informed deep neural network("CDAnet") was introduced, providing a surrogate of the determining form mapfor efficient downscaling. CDAnet was demonstrated to efficiently downscalenoise-free coarse-scale data in a deterministic setting. Herein, theperformance of well-trained CDAnet models is analyzed in a stochastic settinginvolving (i) observational noise, (ii) model noise, and (iii) a combination ofobservational and model noises. The analysis is performed employing theRayleigh-Benard convection paradigm, under three training conditions, namely,training with perfect, noisy, or downscaled data. Furthermore, the effects ofnoises, Rayleigh number, and spatial and temporal resolutions of the inputcoarse-scale information on the downscaled fields are examined. The resultssuggest that the expected l2-error of CDAnet behaves quadratically in terms ofthe standard deviations of the observational and model noises. The results alsosuggest that CDAnet responds to uncertainties similar to the theorized andnumerically-validated CDA behavior with an additional error overhead due toCDAnet being a surrogate model of the determining form map.

Downscaling Using CDAnet Under Observational and Model Noises: The Rayleigh-Benard Convection Paradigm