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Showing 4 of 4 academic articles

Spatial random downscaling of rainfall signals in Andean heterogeneous terrain

A. Posadas, L. A. Duffaut Espinosa, C. Yarlequé, M. Carbajal et al. • Nonlinear Processes in Geophysics (2015)

Remotely sensed data are often used as proxies for indirect precipitation measures over data-scarce and complex-terrain areas such as the Peruvian Andes. Although this information might be appropriate for some research requirements, the ext...

DOAJ Open Access (DOAJ) DOI: 10.5194/npg-22-383-2015

Submesoscale‐Permitting Physical/Biogeochemical Future Projections for the Main Hawaiian Islands

T. Friedrich, B. S. Powell, J. L. Gunnarson, G. Liu et al. • Journal of Advances in Modeling Earth Systems (2024)

Abstract Global climate models provide useful tools to forecast large‐scale anthropogenic trends and the impacts on ocean physics and marine biology and chemistry. Due to coarse spatial resolution, they typically lack the ability to represe...

DOAJ Open Access (DOAJ) DOI: 10.1029/2023MS003855

Downscaling atmospheric chemistry simulations with physically consistent deep learning

A. Geiss, S. J. Silva, S. J. Silva, J. C. Hardin et al. • Geoscientific Model Development (2022)

Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrat...

DOAJ Open Access (DOAJ) DOI: 10.5194/gmd-15-6677-2022

A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution

Joseph W. Lockwood, Avantika Gori, Pierre Gentine • Journal of Geophysical Research: Machine Learning and Computation (2024)

Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coa...

DOAJ Open Access (DOAJ) DOI: 10.1029/2024JH000375