Royal Society Special Issue: Machine Learning for Weather and Climate Modelling – HPCwire

Royal Society Special Issue: Machine Learning for Weather and Climate Modelling – HPCwire

Feb. 15, 2020 — Royal Society Publishing has recently published a special issue of Philosophical Transactions A entitled Machine learning for weather and climate modelling compiled and edited by Matthew Chantry, Hannah Christensen, Peter Dueben and Tim Palmer. The articles can be accessed directly at www.bit.ly/TransA-2194

The recent rise of machine learning begs the question of how these tools can help improve weather and climate forecasting. In this theme issue, experts from weather and climate modelling and machine learning highlight and address the exciting challenges found across a wide range of problems within this field. This issue includes review articles capturing the current state of the science alongside original research. Articles tackle: data assimilation (deducing the current state of the atmosphere); forecasting the earth system; and improving existing models by post-processing their outputs to improve accuracy.

Access content online at bit.ly/TransA-2194

Print issues will soon be available at bit.ly/TA-print. To receive a discount, use promotional code TA 2194 when prompted. Inquiries on print issues can also be made to [email protected]


Source: Royal Society Publishing

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