One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Martin G. Schultz, Clara Betancourt, Bing Gong, Felix Kleinert, Michael Langguth, Lukas Hubert Leufen, Amirpasha Mozaffari, Scarlet Stadtler
The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue 'Machine learning for weather and climate modelling'.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., & Stadtler, S. (2021). Can deep learning beat numerical weather prediction? Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 379(2194), 20200097. https://doi.org/10.1098/rsta.2020.0097
Schultz, Martin G., et al. "Can deep learning beat numerical weather prediction?" Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, vol. 379, no. 2194, 2021, pp. 20200097, https://doi.org/10.1098/rsta.2020.0097.
Schultz, Martin G., Clara Betancourt, Bing Gong, Felix Kleinert, Michael Langguth, Lukas Hubert Leufen, Amirpasha Mozaffari, and Scarlet Stadtler. "Can deep learning beat numerical weather prediction?" Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 379, no. 2194 (2021): 20200097. https://doi.org/10.1098/rsta.2020.0097.
Schultz M. G., Betancourt C., Gong B. [и др.] Can deep learning beat numerical weather prediction? // Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. — 2021. — Vol. 379, № 2194. — P. 20200097. — URL: https://doi.org/10.1098/rsta.2020.0097
@article{schultz2021deep,
title = {{Can deep learning beat numerical weather prediction?}},
author = {Martin G. Schultz and Clara Betancourt and Bing Gong and Felix Kleinert and Michael Langguth and Lukas Hubert Leufen and Amirpasha Mozaffari and Scarlet Stadtler},
journal = {Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences},
year = {2021},
volume = {379},
number = {2194},
pages = {20200097},
doi = {10.1098/rsta.2020.0097},
url = {https://doi.org/10.1098/rsta.2020.0097}
}TY - JOUR AU - Schultz, Martin G. AU - Betancourt, Clara AU - Gong, Bing AU - Kleinert, Felix AU - Langguth, Michael AU - Leufen, Lukas Hubert AU - Mozaffari, Amirpasha AU - Stadtler, Scarlet TI - Can deep learning beat numerical weather prediction? JO - Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences VL - 379 IS - 2194 SP - 20200097 PY - 2021 DA - 2021/02/15 DO - 10.1098/rsta.2020.0097 UR - https://doi.org/10.1098/rsta.2020.0097 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.