Bridging observations, theory and numerical simulation of the ocean using machine learning
Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …
sophistication of tools available for its study. The incorporation of machine learning (ML) …
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning tasks. When data
are 'missing at random'there exist a range of tools and techniques to deal with the issue …
are 'missing at random'there exist a range of tools and techniques to deal with the issue …
Earthformer: Exploring space-time transformers for earth system forecasting
Conventionally, Earth system (eg, weather and climate) forecasting relies on numerical
simulation with complex physical models and hence is both expensive in computation and …
simulation with complex physical models and hence is both expensive in computation and …
Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status, and outlook
Benchmark datasets and benchmark problems have been a key aspect for the success of
modern machine learning applications in many scientific domains. Consequently, an active …
modern machine learning applications in many scientific domains. Consequently, an active …
Machine learning methods in weather and climate applications: A survey
L Chen, B Han, X Wang, J Zhao, W Yang, Z Yang - Applied Sciences, 2023 - mdpi.com
With the rapid development of artificial intelligence, machine learning is gradually becoming
popular for predictions in all walks of life. In meteorology, it is gradually competing with …
popular for predictions in all walks of life. In meteorology, it is gradually competing with …
[HTML][HTML] Machine learning for numerical weather and climate modelling: a review
CO de Burgh-Day… - Geoscientific Model …, 2023 - gmd.copernicus.org
Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …
modelling. Applications range from improved solvers and preconditioners, to …
Small ship detection of SAR images based on optimized feature pyramid and sample augmentation
Synthetic aperture radar images have become the latest high-resolution imaging equipment,
which can monitor the Earth 24 ha day. More and more deep-learning technologies are …
which can monitor the Earth 24 ha day. More and more deep-learning technologies are …
Dynamic-LSTM hybrid models to improve seasonal drought predictions over China
Accurate drought prediction is essential for drought resilience and water resources
management. The skill of seasonal drought prediction from dynamical and statistical models …
management. The skill of seasonal drought prediction from dynamical and statistical models …
Subseasonal prediction of regional Antarctic sea ice by a deep learning model
Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but
a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related …
a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related …
A data-driven deep learning model for weekly sea ice concentration prediction of the pan-arctic during the melting season
Y Ren, X Li, W Zhang - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
This study proposes a purely data-driven model for the weekly prediction of daily sea ice
concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season …
concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season …