Journal ArticleDOI
The quiet revolution of numerical weather prediction
TLDR
As a computational problem, global weather prediction is comparable to the simulation of the human brain and of the evolution of the early Universe, and it is performed every day at major operational centres across the world.Abstract:
Advances in numerical weather prediction represent a quiet revolution because they have resulted from a steady accumulation of scientific knowledge and technological advances over many years that, with only a few exceptions, have not been associated with the aura of fundamental physics breakthroughs. Nonetheless, the impact of numerical weather prediction is among the greatest of any area of physical science. As a computational problem, global weather prediction is comparable to the simulation of the human brain and of the evolution of the early Universe, and it is performed every day at major operational centres across the world.read more
Citations
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Journal ArticleDOI
Deep learning and process understanding for data-driven Earth system science
Markus Reichstein,Gustau Camps-Valls,Bjorn Stevens,Martin Jung,Joachim Denzler,Nuno Carvalhais,Nuno Carvalhais,Prabhat +7 more
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
Journal ArticleDOI
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
TL;DR: This work reviews the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
Journal ArticleDOI
Deep learning to represent subgrid processes in climate models.
TL;DR: In this paper, a deep neural network is used to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly.
Journal ArticleDOI
Iterative near-term ecological forecasting: Needs, opportunities, and challenges
Michael Dietze,Andrew M. Fox,Lindsay M. Beck-Johnson,Julio L. Betancourt,Mevin B. Hooten,Catherine S. Jarnevich,Timothy H. Keitt,Melissa A. Kenney,Christine Laney,Laurel G. Larsen,Henry W. Loescher,Henry W. Loescher,Claire Lunch,Bryan C. Pijanowski,James T. Randerson,Emily K. Read,Andrew T. Tredennick,Rodrigo Vargas,Kathleen C. Weathers,Ethan P. White +19 more
TL;DR: The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.
BookDOI
Reproducibility and Replicability in Science
TL;DR: The National Academies of Sciences, Engineering, and Medicine conducted a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research as mentioned in this paper.
References
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Journal ArticleDOI
Deterministic nonperiodic flow
TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
Book
Atmospheric Modeling, Data Assimilation and Predictability
TL;DR: A comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability.
Book
Data Assimilation: The Ensemble Kalman Filter
TL;DR: In this paper, the authors define a statistical analysis scheme for estimating an oil reservoir simulator and an ocean prediction system based on the En-KF model, and propose a sampling strategy for the EnKF and square root analysis schemes.
Book
Atmospheric Data Analysis
TL;DR: In this article, the authors present a method of successive corrections for Normal Mode Initialization (NME) in univariate, multivariate and univariate Statistical Interpolation (SI) problems.
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