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000890552 1001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b0$$eCorresponding author$$ufzj
000890552 245__ $$aCan deep learning beat numerical weather prediction?
000890552 260__ $$aLondon$$bRoyal Society$$c2021
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000890552 520__ $$aThe 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.
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000890552 7001_ $$0P:(DE-Juel1)171435$$aBetancourt, Clara$$b1$$ufzj
000890552 7001_ $$0P:(DE-Juel1)177767$$aGong, Bing$$b2$$ufzj
000890552 7001_ $$0P:(DE-Juel1)176602$$aKleinert, Felix$$b3$$ufzj
000890552 7001_ $$0P:(DE-Juel1)180790$$aLangguth, Michael$$b4$$ufzj
000890552 7001_ $$0P:(DE-Juel1)177004$$aLeufen, Lukas Hubert$$b5$$ufzj
000890552 7001_ $$0P:(DE-Juel1)166264$$aMozaffari, Amirpasha$$b6$$ufzj
000890552 7001_ $$0P:(DE-Juel1)180752$$aStadtler, Scarlet$$b7$$ufzj
000890552 770__ $$aMachine learning for weather and climate modelling
000890552 773__ $$0PERI:(DE-600)1462626-3$$a10.1098/rsta.2020.0097$$n2194$$p20200097$$tPhilosophical transactions of the Royal Society of London / A$$v379$$x0080-4614$$y2021
000890552 8564_ $$uhttps://juser.fz-juelich.de/record/890552/files/741183.pdf
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