Society has benefited enormously from the continuous advancement in numerical weather prediction that has occurred over many decades driven by a combination of outstanding scientific, computational and technological breakthroughs. Here, we demonstrate that data-driven methods are now positioned to contribute to the next wave of major advances in atmospheric science. We show that data-driven models can predict important meteorological quantities of interest to society such as global high resolution precipitation fields (0.25°) and can deliver accurate forecasts of the future state of the atmosphere without prior knowledge of the laws of physics and chemistry. We also show how these data-driven methods can be scaled to run on supercomputers with up to 1024 modern graphics processing units and beyond resulting in rapid training of data-driven models, thus supporting a cycle of rapid research and innovation. Taken together, these two results illustrate the significant potential of data-driven methods to advance atmospheric science and operational weather forecasting.
AbadiMAgarwalABarhamP, et al. (2015) TensorFlow: large-scale machine learning on heterogeneous systems. ArXiv preprint arXiv:1603.04467
2.
Aditya Sai SrinivasTSomulaRGovindaK, et al. (2020) Estimating rainfall using machine learning strategies based on weather radar data. International Journal of Communication Systems33(13): e3999.
3.
ArcomanoTSzunyoghIPathakJ, et al. (2020) A machine learning-based global atmospheric forecast model. Geophysical Research Letters47(9): e2020GL087776.
4.
AwanAAJainAAnthonyQ, et al. (2020) HyPar-Flow: exploiting MPI and Keras for scalable hybrid-parallel DNN training with tensorflow. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12151LNCS.
5.
BadrinarayananVKendallACipollaR (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence39(12): 2481–2495.
6.
BauerPDuebenPDHoeflerT, et al. (2021) The digital revolution of Earth-system science. Nature Computational Science1(2): 104–113.
7.
BauerPThorpeABrunetG (2015) The quiet revolution of numerical weather prediction. Nature525(7567): 47–55.
DuebenPDBauerP (2018) Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development11(10): 3999–4009.
10.
HernándezESanchez-AnguixVJulianV, et al. (2016) Rainfall prediction: a deep learning approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9648.
11.
HersbachHBellBBerrisfordP, et al. (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society146(730): 1999–2049.
12.
HochreiterSSchmidhuberJ (1997) Long short-term memory. Neural Computation9(8): 1735–1780.
KrizhevskyASutskeverIHintonGE (2012) ImageNet classification with deep convolutional neural networks. In: PereiraFBurgesCJCBottouLWeinbergerKQ (eds). Advances in Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc., Vol. 2, pp. 1097–1105.
15.
KurthTTreichlerSRomeroJ, et al. (2019) Exascale deep learning for climate analytics. In Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018, Dallas, TX, USA, 11-16 November 2018.
16.
LagerquistRMcGovernAMGagneDJ (2019) Deep learning for spatially explicit prediction of synoptic-scale fronts. Weather and Forecasting34(4).
17.
LarraondoPRenzulloLVan DijkA, et al. (2020) Optimization of deep learning precipitation models using categorical binary metrics. Journal of Advances in Modeling Earth Systems12(5): 1137–1160.
18.
LarraondoPRRenzulloLJInzaI, et al. (2019) A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks. ArXiv preprint arXiv:1903.10274
19.
LecunYBengioYHintonG (2015) Deep learning. Nature521: 436–444.
20.
LiuLOuyangWWangX, et al. (2020) Deep learning for generic object detection: a survey. International Journal of Computer Vision128(2): 261–318.
21.
NeumannPDübenPAdamidisP, et al. (2019) Assessing the scales in numerical weather and climate predictions: will exascale be the rescue?Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences377(2142): 20180148.
22.
PanBHsuKAghaKouchakA, et al. (2019) Improving precipitation estimation using convolutional neural network. Water Resources Research55(3): 2301–2321.
23.
RaspSDuebenPDScherS, et al. (2020) Weatherbench: a benchmark data set for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems12(11): e2020MS002203.
ReichsteinMCamps-VallsGStevensB, et al. (2019) Deep learning and process understanding for data-driven Earth system science. Nature566(7743): 195–204.
26.
RonnebergerOFischerPBroxT (2015) U-net: convolutional networks for biomedical image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Verlag: Springer, Vol. 9351, pp. 234–241.
27.
ScherSMessoriG (2018) Predicting weather forecast uncertainty with machine learning. Quarterly Journal of the Royal Meteorological Society144(717): 2830–2841.
28.
ScherSMessoriG (2019) Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground. Geoscientific Model Development12(7): 2797–2809.
29.
SergeevABalsoMD (2018) Horovod: fast and easy distributed deep learning in tensorflow. ArXiv preprint arXiv:1802.05799
30.
ShiXChenZWangH, et al. (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: PereiraFBurgesCJCBottouLWeinbergerKQ (eds). Advances in Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc., pp. 802–810
31.
SimmonsAJHollingsworthA (2002) Some aspects of the improvement in skill of numerical weather prediction. Quarterly Journal of the Royal Meteorological Society128(580): 647–677.
32.
SimonyanKZissermanA (2015) Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, San Diego, CA, 7–9 May 2015
33.
SønderbyCKEspeholtLHeekJ, et al. (2020) Metnet: a neural weather model for precipitation forecasting. ArXiv preprint arXiv:2003.12140
34.
Van EssenBKimHPearceR, et al. (2015) LBANN: Livermore big artificial neural network HPC toolkit. In: Proceedings of MLHPC 2015: Machine Learning in High-Performance Computing Environments - Held in Conjunction with SC 2015: The International Conference for High Performance Computing, Networking, Storage and Analysis, Austin, Texas, 15 November 2015. New York, NY, USA: Association for Computing Machinery, Inc. ISBN 9781450340069. doi:10.1145/2834892.2834897.
35.
WangBLuoHLuJ, et al. (2019) Deep uncertainty quantification: a machine learning approach for weather forecasting. In KDD '19: The 25th ACM SIGKDD conference on knowledge discovery and data mining anchorage, Alaska, USA, 4–8 August 2019, pp. 2087–2095. doi:10.1145/3292500.3330704.
36.
WeynJADurranDRCaruanaR (2019) Can machines learn to predict weather? Using deep learning to predict gridded 500-hPa geopotential height From historical weather data. Journal of Advances in Modeling Earth Systems11(8): 2680–2693.
37.
ZhangHPuZ (2010) Beating the uncertainties: ensemble forecasting and ensemble-based data assimilation in modern numerical weather prediction. Advances in Meteorology2010: 1–10.
