Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges
Sid-Ahmed Boukabara,Vladimir M. Krasnopolsky,Jebb Stewart,Eric Maddy,Narges Shahroudi,Ross N. Hoffman +5 more
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TLDR
It is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.Abstract:
Artificial intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental ...read more
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Journal ArticleDOI
Deep-learning-based information mining from ocean remote-sensing imagery
Xiaofeng Li,Bin Liu,Gang Zheng,Yibin Ren,Shuangshang Zhang,Liu Yingjie,Le Gao,Liu Yuhai,Bin Zhang,Fan Wang +9 more
TL;DR: This review paper first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of oceanRemote-Sensing imagery to show how effective these deep- learning frameworks are.
Journal ArticleDOI
Purely satellite data-driven deep learning forecast of complicated tropical instability waves.
TL;DR: This study demonstrates the strong potential of the satellite data–driven deep learning model as an alternative to traditional numerical models for forecasting oceanic phenomena.
Journal ArticleDOI
Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in Meteorological Applications
Imme Ebert-Uphoff,Kyle Hilburn +1 more
TL;DR: This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of receptive fields, underutilized meteorological performance measures, and methods for Neural network interpretation,such as synthetic experiments and layer-wise relevance propagation.
Journal ArticleDOI
Deep Data Assimilation: Integrating Deep Learning with Data Assimilation
TL;DR: The DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system, and it is proved that the DDA approach implies a reduction of the model error, which decreases at each iteration.
Journal ArticleDOI
Learning earth system models from observations: machine learning or data assimilation?
TL;DR: In this paper, the equivalences between data assimilation (DA) and machine learning (ML) have been discussed, and a unified framework of Bayesian networks has been proposed for the unification of DA and ML.
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