Results 1 to 10 of about 25,529,051 (296)

Deep Data Assimilation: Integrating Deep Learning with Data Assimilation

Applied Sciences (Switzerland), 2021
In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed ...
Rossella Arcucci
exaly   +2 more sources

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

Reviews of Geophysics
Data assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the ...
Jinliang Hou, Qingyun Duan, Kun Yang
exaly   +2 more sources

Effect of microphysics scheme and data assimilation on hydrometeor and radiative flux simulations in the Arctic [PDF]

Royal Society Open Science
Although clouds are a major factor influencing atmospheric environments in the Arctic, numerical simulations of Arctic clouds are uncertain. In this study, the effects of microphysics scheme and data assimilation (DA) on the simulation of clouds ...
Dae-Hui Kim, Hyun Mee Kim
doaj   +2 more sources

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation [PDF]

Geoscientific Model Development, 2022
On 24 September 2021, JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the Model Prediction Across Scales – Atmosphere (MPAS-A) built on the software framework of the Joint Effort for Data assimilation Integration (JEDI) was publicly released for
Z. Liu, C. Snyder, J. J. Guerrette, B.-J. Jung, J. Ban, S. Vahl, S. Vahl, Y. Wu, Y. Wu, Y. Trémolet, T. Auligné, B. Ménétrier, A. Shlyaeva, S. Herbener, E. Liu, E. Liu, D. Holdaway, D. Holdaway, B. T. Johnson   +18 more
doaj   +1 more source

Bivariate sea-ice assimilation for global-ocean analysis–reanalysis [PDF]

Ocean Science, 2023
In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational
A. Cipollone, D. S. Banerjee, D. S. Banerjee, D. S. Banerjee, D. Iovino, A. Aydogdu, S. Masina   +6 more
doaj   +1 more source

Score-based Data Assimilation [PDF]

Neural Information Processing Systems, 2023
Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem of identifying plausible state trajectories that explain noisy or incomplete observations of stochastic dynamical systems.
François Rozet, Gilles Louppe
semanticscholar   +1 more source

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review [PDF]

IEEE/CAA Journal of Automatica Sinica, 2023
Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and
Sibo Cheng, César Quilodrán-Casas, Said Ouala, A. Farchi, Che Liu, P. Tandeo, Ronan Fablet, D. Lucor, B. Iooss, J. Brajard, Dunhui Xiao, T. Janjić, Weiping Ding, Yi-Ke Guo, A. Carrassi, M. Bocquet, R. Arcucci   +16 more
semanticscholar   +1 more source

3D‐Var data assimilation using a variational autoencoder [PDF]

Quarterly Journal of the Royal Meteorological Society, 2023
Data assimilation of atmospheric observations traditionally relies on variational and Kalman filter methods. Here, an alternative neural network data assimilation (NNDA) with variational autoencoder (VAE) is proposed.
Boštjan Melinc, Ž. Zaplotnik
semanticscholar   +1 more source

The bulk parameterizations of turbulent air–sea fluxes in NEMO4: the origin of sea surface temperature differences in a global model study [PDF]

Geoscientific Model Development, 2022
Wind stress and turbulent heat fluxes are the major driving forces that modify the ocean dynamics and thermodynamics. In the Nucleus for European Modelling of the Ocean (NEMO) ocean general circulation model, these turbulent air–sea fluxes (TASFs) can ...
G. Bonino, D. Iovino, L. Brodeau, S. Masina   +3 more
doaj   +1 more source

Summertime sea-ice prediction in the Weddell Sea improved by sea-ice thickness initialization

Scientific Reports, 2021
Skillful sea-ice prediction in the Antarctic Ocean remains a big challenge due to paucity of sea-ice observations and insufficient representation of sea-ice processes in climate models.
Yushi Morioka, Doroteaciro Iovino, Andrea Cipollone, Simona Masina, Swadhin K. Behera   +4 more
doaj   +1 more source