TY  - JOUR
AU  - Schultz, Martin
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 of London / A
VL  - 379
IS  - 2194
SN  - 0080-4614
CY  - London
PB  - Royal Society
M1  - FZJ-2021-01034
SP  - 20200097
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 33583266
UR  - <Go to ISI:>//WOS:000649132600009
DO  - DOI:10.1098/rsta.2020.0097
UR  - https://juser.fz-juelich.de/record/890552
ER  -