{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T12:54:52Z","timestamp":1768481692206,"version":"3.49.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["CCF Trans. HPC"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s42514-021-00071-y","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T22:02:43Z","timestamp":1626472963000},"page":"320-329","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Challenges and opportunities for a hybrid modelling approach to earth system science"],"prefix":"10.1007","volume":"3","author":[{"given":"Simon","family":"See","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9717-4215","authenticated-orcid":false,"given":"Jeff","family":"Adie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"71_CR1","doi-asserted-by":"crossref","unstructured":"Ackmann, J., Peter, D., Tim, P., and Piotr, S. 2020. Machine-learned Preconditioners for Linear Solvers in Geophysical Fluid Flows. arXiv. https:\/\/arxiv.org\/pdf\/2010.02866.pdf.","DOI":"10.5194\/egusphere-egu21-5507"},{"key":"71_CR2","unstructured":"Ammar, A., S. Labroue, E. Obligis, M. Crepon, and S. Thiria. 2016. Building a Learning Database for the Neural Network retrieval of Sea Surface Salinity from SMOS Brightness Temperatures. arXiv e-prints. https:\/\/ui.adsabs.harvard.edu\/abs\/2016arXiv160104296A."},{"key":"71_CR3","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-021-00023-0","author":"P Bauer","year":"2021","unstructured":"Bauer, P., Peter Dueben, T., Hoefler, T.Q., Schulthess, T., Wedi, N.: The digital revolution of earth-system science. Nat. Comput. Sci. (2021). https:\/\/doi.org\/10.1038\/s43588-021-00023-0","journal-title":"Nat. Comput. Sci."},{"key":"71_CR4","unstructured":"Beucler, Tom, Michael S. Pritchard, Stephan Rasp, Pierre Gentine, Jordan Ott, and Pierre Baldi. 2019. Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems. arXiv: Comput. Phys. Accessed 3 28, 2021. https:\/\/arxiv.org\/abs\/1909.00912."},{"key":"71_CR5","doi-asserted-by":"crossref","unstructured":"Bharaskaran, P., R. Kumar, R. Barman, and R. Muthalagu. 2013. A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks. J. Mar. Sci. Technol. 160\u2013175.","DOI":"10.1007\/s00773-009-0081-2"},{"key":"71_CR6","doi-asserted-by":"publisher","DOI":"10.1029\/2018MS001472","author":"T Bolton","year":"2019","unstructured":"Bolton, T., Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization. J. Adv. Model. Earth Syst. (JAMES) (2019). https:\/\/doi.org\/10.1029\/2018MS001472","journal-title":"J. Adv. Model. Earth Syst. (JAMES)"},{"key":"71_CR7","doi-asserted-by":"publisher","DOI":"10.1029\/2020MS002232","author":"M Bonavita","year":"2020","unstructured":"Bonavita, M., Laloyaux, P.: Machine learning for model error inference and correction. J Adv Model Earth Syst (2020). https:\/\/doi.org\/10.1029\/2020MS002232","journal-title":"J Adv Model Earth Syst"},{"key":"71_CR8","unstructured":"Brenowitz, N., and C. Bretherton. 2019. \"Peering into the black box of machine learning parameterization. AGU Fall Meeting. https:\/\/ui.adsabs.harvard.edu\/abs\/2019AGUFMIN44A..03B."},{"key":"71_CR9","doi-asserted-by":"publisher","unstructured":"Carmen, J., Clune T., Francis G., Govett M., Brian G.ss, Anke K., Tsendgar L., et al. 2017. Position Paper on High-Performance Computing for Earth System Models. https:\/\/doi.org\/10.7289\/V5862DH3.","DOI":"10.7289\/V5862DH3"},{"key":"71_CR10","doi-asserted-by":"crossref","unstructured":"Chantry, M., Sam, H., Pete,r D., Inn,a P., and Tim, P. 2021. Machine learning emulation of gravity wave drag in numerical weather forecasting. arXiv. https:\/\/arxiv.org\/pdf\/2101.08195.pdf.","DOI":"10.5194\/egusphere-egu21-7678"},{"key":"71_CR11","doi-asserted-by":"publisher","DOI":"10.1029\/2019GL083662","author":"W Chapman","year":"2019","unstructured":"Chapman, W., Subramanian, A., Dell Monache, L., Xie, S., Ralph, F.: Improving stmospheric river forecasts with machine learning. Geophys. Res. Lett. (2019). https:\/\/doi.org\/10.1029\/2019GL083662","journal-title":"Geophys. Res. Lett."},{"key":"71_CR12","unstructured":"Curcic, M. 2019. A parallel Fortran framework for neural networks and deep learning. arXiv. https:\/\/arxiv.org\/pdf\/1902.06714.pdf."},{"key":"71_CR13","doi-asserted-by":"publisher","unstructured":"Ducournau, A., and R. Fablet. 2016. Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data. 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS). Cancun, Mexico. https:\/\/doi.org\/10.1109\/PRRS.2016.7867019.","DOI":"10.1109\/PRRS.2016.7867019"},{"key":"71_CR14","doi-asserted-by":"publisher","unstructured":"Dueben, P., Bauer, P.: Challenges and design choices for global weather and climate models based on machine learning.Geosci Model (2018). https:\/\/doi.org\/10.5194\/gmd-11-3999-2018","DOI":"10.5194\/gmd-11-3999-2018"},{"key":"71_CR15","doi-asserted-by":"crossref","unstructured":"Fuhrer, O., Tarun, C., Torsten, H., Grzegorz, K., Xavier, L., David, L. Daniel L., et al. 2017. Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0. Geosci. Model Dev. 11 (4): 1665\u20131681. Accessed 3 27, 2021. https:\/\/geosci-model-dev.net\/11\/1665\/2018\/gmd-11-1665-2018.pdf.","DOI":"10.5194\/gmd-11-1665-2018"},{"key":"71_CR16","doi-asserted-by":"publisher","unstructured":"Galea, D., Lawrence, B., Kunkel, J.: detecting tropical cyclone using deep learning techniques.EGU General Assembly (2020). https:\/\/doi.org\/10.5194\/egusphere-egu2020-9870","DOI":"10.5194\/egusphere-egu2020-9870"},{"key":"71_CR17","unstructured":"Goodfellow, I., Jean P.-A., Mehdi M., Bing X, David W.-F., Sherjil O., Aaron C., Yoshua B.. 2014. Generative Adversarial Networks. Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). 2672\u20132680."},{"key":"71_CR18","unstructured":"Gronquist, P., Chengyuan, Y., Tal Ben-Nun, Peter Dueben, Shigang Li, and Torsten Hoefler. 2020. \"Deep Learning for Post-Processing Ensemble Weather Forecasts.\" arXiv. https:\/\/arxiv.org\/abs\/2005.08748."},{"key":"71_CR19","unstructured":"Hall, D., J. Stewart, C Bonfanti, M. Govett, S. Maksimovic, and L. Trailovic. 2019. \"Deep learning for improved use of satellite observations.\" Ninth Symposium on Advances in Modeling and Analysis using Python. Phoenix. https:\/\/ams.confex.com\/ams\/2019Annual\/meetingapp.cgi\/Paper\/353938."},{"key":"71_CR20","unstructured":"Hennigh, O., Susheela, N., Mohammad, N., Akshay, S., Kaustubh, T., Max, R., Jose del, A. F., Wonmin, B., Zhiwei, F., Sanjay, C. 2020. NVIDIA SimNet: an AI-accelerated multi-physics simulation framework. arXiv. https:\/\/arxiv.org\/abs\/2012.07938."},{"key":"71_CR21","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.egypro.2018.11.043","volume":"155","author":"K Higashiyama","year":"2018","unstructured":"Higashiyama, K., Fujimoto, Yu., Hayashi, Y.: Feature extraction of NWP data for Wind Power Forecasting using 3D-Convolutional Neural Networks. Energy Procedia 155, 350\u2013358 (2018)","journal-title":"Energy Procedia"},{"key":"71_CR22","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"71_CR23","doi-asserted-by":"publisher","DOI":"10.1029\/2018MS001364","author":"R Hogan","year":"2018","unstructured":"Hogan, R., Bozzo, A.: A flexible and efficient radiation scheme for the ECMWF model. J Adv Moldel Earth Syst (2018). https:\/\/doi.org\/10.1029\/2018MS001364","journal-title":"J Adv Moldel Earth Syst"},{"key":"71_CR24","unstructured":"James, S., Yushan, Z., and Ferghal, O. 2017. A Machine Learning Framework to Forecast Wave Conditions. arXiv. https:\/\/arxiv.org\/pdf\/1709.08725.pdf."},{"key":"71_CR25","doi-asserted-by":"publisher","unstructured":"Kadow, C., David, M.H., and Uwe, U. 2020 Artificial intelligence reconstructs missing climate information. Nat Geosci. https:\/\/doi.org\/10.1038\/s41561-020-0582-5.","DOI":"10.1038\/s41561-020-0582-5"},{"key":"71_CR26","unstructured":"Kim, S., Seungkyun, H., Minsu, J., Sa-Kwang, S. 2017a. Deeprain: ConvLSTM network for precipitation prediction using mulitchannel radar data. Clim Inform 89\u201392."},{"key":"71_CR27","unstructured":"Kim, S.K., Sasha, A., Jiwoo, L., Chengzhu, Z., Aaron, W., Dean, W. 2017b. Massive Scale Deep Learning for detecting Extreme Climate events. Clim Inform. Boulder."},{"key":"71_CR28","unstructured":"Kim, S., Ji-Sun, K., Minho, L., Sa-Kwang, S. 2018. DeepTC: ConvLSTM Network for Trajectory Prediction of Tropical Cyclone using Spatiotemporal Atmospheric Simulation. https:\/\/openreview.net\/forum?id=HJlCVoPAF7."},{"key":"71_CR29","doi-asserted-by":"publisher","unstructured":"Krasnopolsky, V., M. Fox-Rabinovitz, Y. Hou, S. Lord, and A. Belochitski. 2010. Accurate and Fast Neural Network Emulations of Model Radiation for the NCEP Coupled Climate Forecast System: Climate Simulations and Seasonal Predictions. Mon Weather Rev. Boston. https:\/\/doi.org\/10.1175\/2009MWR3149.1.","DOI":"10.1175\/2009MWR3149.1"},{"key":"71_CR30","doi-asserted-by":"publisher","DOI":"10.1145\/3065386","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional networks. Neural Imform Process Syst (2012). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Neural Imform Process Syst"},{"key":"71_CR31","doi-asserted-by":"crossref","unstructured":"Kumler-Bonfanti, C., Stewart, J., David, H., Mark, G. 2020. Tropical and Extratropical Cyclone Detection Using Deep Learning.","DOI":"10.1175\/JAMC-D-20-0117.1"},{"key":"71_CR32","doi-asserted-by":"crossref","unstructured":"Kurth, T, Sean, T., Joshua, R., Mayur, M, Nathan, L, Everett, P, Ankur, M., et al. 2018. \"Exascale deep learning for climate analytics. Proceedings of the Intenational Conference for High Performance Computing, Networking, Storage, and Analysis. Dallas, Texas: IEEE Press.","DOI":"10.1109\/SC.2018.00054"},{"key":"71_CR33","unstructured":"LeCun, Y., Yoshua,. B. 1995. Convolutional networks for images, speech and time series. The handbook of brain theory."},{"key":"71_CR34","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bernhard, B., John, D., Donnie, H., Richard, H., Wayne, H., Lawrence, J. 1989. Backpropogation applied to handwritten zip code recognition. Neural Computation. MIT Press. 541\u2013551.","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"71_CR35","unstructured":"Lee, Y.-J., Christina, B., Lidia, T., Brian, E., Mark, G., and Jebb, S. 2017. Using Deep Learning for Targeted Data Selection, Improving Satellite Observation Utilization for Model Initialization. AGU Fall Meeting."},{"key":"71_CR36","doi-asserted-by":"publisher","DOI":"10.1080\/15481603.2018.1489943","author":"CS Lee","year":"2019","unstructured":"Lee, C.S., Sohn, E., Park, J.D., Jang, J.-D.: Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea. Gisci Remote Sens (2019). https:\/\/doi.org\/10.1080\/15481603.2018.1489943","journal-title":"Gisci Remote Sens"},{"key":"71_CR37","doi-asserted-by":"publisher","DOI":"10.1186\/s40645-018-0245-y","author":"D Matsuoka","year":"2018","unstructured":"Matsuoka, D., Nakano, M., Sugiyama, D., Uchida, S.: Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model. Prog Earth Planet Sci (2018). https:\/\/doi.org\/10.1186\/s40645-018-0245-y","journal-title":"Prog Earth Planet Sci"},{"key":"71_CR38","unstructured":"Mestre, O., Michael, Z., Maxime, T., Marion, R., and Harold, P. 2018. Machine learning techniques applied to NWP post-processing. M\u00e9t\u00e9o-France."},{"key":"71_CR39","unstructured":"Mommert, M, Mario, S., Marcel, N., Linus, S., and Damian, B.. 2020. Characterization of Industrial Smoke Plumes from Remote Sensing Data. https:\/\/arxiv.org\/abs\/2011.11344v1."},{"key":"71_CR40","doi-asserted-by":"publisher","unstructured":"Montavon, G, Binder, A., Lapuschkin, S., Samek, W., and Muller, K. 2019. Layer-Wise Relevance Proogation: An Overview. Exaplainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science (Springer). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_10.","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"71_CR41","doi-asserted-by":"crossref","unstructured":"Mouatadid, S., Steve, E., and Andre, E. 2017. Non-uniforn spatial downscaling of climate variables. Clim Inform. 65\u201368.","DOI":"10.1109\/ICDMW.2017.49"},{"key":"71_CR42","doi-asserted-by":"publisher","unstructured":"Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization: How neural networks build up their understanding of images. doi:https:\/\/doi.org\/10.23915\/distill.00007","DOI":"10.23915\/distill.00007"},{"key":"71_CR43","unstructured":"Ouala, S., C\u00e9dric, H., and Ronan, . 2018. Sea surface temperature prediction and reconstruction using patch-level neural network representations. arXiv: Machine Learning. Accessed 3 29, 2021. https:\/\/arxiv.org\/abs\/1806.00144."},{"key":"71_CR44","unstructured":"Pal, A., and Norman, M. 2017. Emulating RRTMG Radiation with Deep Neural Networks for the Accelerated Model for Climate and Energy. AGU Fall Meeting. https:\/\/ui.adsabs.harvard.edu\/abs\/2017AGUFMNG33A0188P."},{"key":"71_CR45","doi-asserted-by":"publisher","unstructured":"Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, S., Arora, V., et al.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling.Hydrol. Earth Syst. Sci (2020). https:\/\/doi.org\/10.5194\/hess-24-1485-2020","DOI":"10.5194\/hess-24-1485-2020"},{"key":"71_CR46","unstructured":"Playout, C., and Asanobu, K.. 2019. Latent Space Representation and RNN for Image-based Typhoon Intensity Analysis and Prediction. Clim Inform. Paris. 47\u201352."},{"key":"71_CR47","doi-asserted-by":"publisher","unstructured":"Prabhat, K., Kashinath, M., Mudigonda, S., Kim, L., Kapp-Schwoerer, A., Graubner, E.K., et al.: ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather.Geosci Model Dev (2021). https:\/\/doi.org\/10.5194\/gmd-14-107-2021","DOI":"10.5194\/gmd-14-107-2021"},{"key":"71_CR48","doi-asserted-by":"publisher","unstructured":"Priya, R., and K. Vani. 2019. Deep Learning Based Forest Fire Classification and Detection in Satellite Images. 11th International Conference on Advanced Computing (ICoAC). Chennai. 61\u201365. https:\/\/doi.org\/10.1109\/ICoAC48765.2019.246817.","DOI":"10.1109\/ICoAC48765.2019.246817"},{"key":"71_CR49","doi-asserted-by":"crossref","unstructured":"Rahman, S. M., O. San, and A. Rasheed. 2018. A Hybrid Approach for Model Order Reduction of Barotropic Quasi-Geostrophic Turbulence. Fluids.","DOI":"10.3390\/fluids3040086"},{"key":"71_CR50","doi-asserted-by":"crossref","unstructured":"Raissi, M., Paris, P., George, K. 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Computational Physics 686\u2013707.","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"71_CR51","doi-asserted-by":"crossref","unstructured":"Rasp, S., Stephan, R., Michael, S.P., and Pierre, G. 2018. Deep learning to represent subgrid processes in climate models. Proceedings of the National Academy of Sciences of the United States of America 115 (39): 9684\u20139689. Accessed 3 28, 2021. https:\/\/pnas.org\/content\/115\/39\/9684.","DOI":"10.1073\/pnas.1810286115"},{"key":"71_CR52","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0912-1","author":"M Reichstein","year":"2019","unstructured":"Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Caravalhais, N., Prabhat: Deep learning and process understanding for data-driven Earth system science. Nature (2019). https:\/\/doi.org\/10.1038\/s41586-019-0912-1","journal-title":"Nature"},{"key":"71_CR53","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T.. 2015. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"71_CR54","unstructured":"Rudy, S. H., Alessandro, A., Steven, L. B., and Nathan Kutz, J.. 2018. Data-driven identification of parametric partial differential equations. arXiv: Numerical Analysis. Accessed 3 30, 2021. https:\/\/arxiv.org\/abs\/1806.00732."},{"key":"71_CR55","unstructured":"Rupe, A., James Crutchfield, Karthik Kashinath, and Prabhat. 2017. A physics-based approach to unsupervised discovery of coherent structures in spatiotemporal systems. Climate Informatics 101\u2013104."},{"key":"71_CR56","unstructured":"Rupe, A., Karthik, K., Nalini, K., Victor, L., Prabhat, and James, C. 2019. \"Towards Unsupervised Segmmentation of Extreme Weather events. Clim. Inform. Paris. 213\u2013218."},{"key":"71_CR57","unstructured":"Seo, S., Arash, M., George, B.-W., and Yan, L. 2017. Graph convolutional autoencoder with recurrent neural networks for spatiotemporal forecasting. Clim Inform. 85\u201388."},{"key":"71_CR58","unstructured":"Sever, G., Jeff, A., Stan, P., and Charlie, C.. 2019. Performance Evaluation of the Weather Research and Forecasting (WRF) Model on the DOE Summit Supercomputer. 9th International Symposium on Atmospheric Sciences (ATMOS 2019). Istanbul, Turkey. 743\u2013749."},{"key":"71_CR59","unstructured":"Singh, A., Adrian, A., and Brian, W. 2019. Downscaling numerical weather Models with GANs. Clim. Inform. Paris. 275\u2013278."},{"key":"71_CR60","unstructured":"Sonderby, C., Lasse, E., Jonathon, H., Mostafa, D., Avital, O., Tim, S., Shreya, A., Jason, H., and Nal, K. 2020. MetNet: A Neural Weather Model for Precipitation Forecasting. arXiv. https:\/\/arxiv.org\/abs\/2003.12140."},{"key":"71_CR61","doi-asserted-by":"publisher","DOI":"10.1029\/2020MS002226","author":"P Ukkonen","year":"2020","unstructured":"Ukkonen, P., Pincus, R., Hogan, R., Nielsen, K., Kaas, E.: Accelerating radiation computations for dynamical models with targeted machine learning and code optimization. J. Adv. Model. Earth Syst. (2020). https:\/\/doi.org\/10.1029\/2020MS002226","journal-title":"J. Adv. Model. Earth Syst."},{"key":"71_CR62","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.009","author":"M Veerman","year":"2020","unstructured":"Veerman, M., Pincus, R., Stoffer, R., van Leeuwen, C., Podereanu, D., varHeerwaarden, C.: Predicting atmospheric optical properties for radiative transfer computations using neural networks. Philos. Trans. r. Soc. (2020). https:\/\/doi.org\/10.1098\/rsta.2020.009","journal-title":"Philos. Trans. r. Soc."},{"key":"71_CR63","doi-asserted-by":"publisher","DOI":"10.3390\/rs9050408","author":"L Wang","year":"2017","unstructured":"Wang, L., Scott, K., Clausi, D.: Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote Sens (2017). https:\/\/doi.org\/10.3390\/rs9050408","journal-title":"Remote Sens"},{"key":"71_CR64","doi-asserted-by":"publisher","unstructured":"Wang, J., Balaprakash, P., Kotamarthi, R.: Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model.Geosci. Model Dev. (2019). https:\/\/doi.org\/10.5194\/gmd-12-4261-2019","DOI":"10.5194\/gmd-12-4261-2019"},{"key":"71_CR65","doi-asserted-by":"publisher","DOI":"10.1175\/MWR-D-18-0391.1","author":"A Wimmers","year":"2019","unstructured":"Wimmers, A., Velden, C., Cossuth, J.: Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery. Mon. Weather Rev. (2019). https:\/\/doi.org\/10.1175\/MWR-D-18-0391.1","journal-title":"Mon. Weather Rev."},{"key":"71_CR66","unstructured":"Yin, Y., Arthur, P., Emmanuel, D.B., and Patrick, G. 2019. Unsupervised Inpainting for Occluded Sea Surface Temperature Sequences. Clim. Inform."},{"key":"71_CR67","doi-asserted-by":"publisher","unstructured":"Zhang, Z.J., Karthikeyan, D. 2015. Machine learning methods for Data-Driven Turbulence Modeling. 22nd AIAA Computational Fluid Dynamics Conference. https:\/\/doi.org\/10.2514\/6.2015-2460.","DOI":"10.2514\/6.2015-2460"},{"key":"71_CR68","doi-asserted-by":"publisher","unstructured":"Zhao, W. L., Wen, L. Z., Pierre, G., Markus, R., Yao, Z., Sha, Z, Yeqiang, W, et al. 2019. \"Physics\u2010constrained machine learning of evapotranspiration. Geophys. Res. Lett.. Accessed 3 29, 2021. https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/pdf\/https:\/\/doi.org\/10.1029\/2019gl085291.","DOI":"10.1029\/2019gl085291"}],"container-title":["CCF Transactions on High Performance Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42514-021-00071-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42514-021-00071-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42514-021-00071-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:06:22Z","timestamp":1634763982000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42514-021-00071-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":68,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["71"],"URL":"https:\/\/doi.org\/10.1007\/s42514-021-00071-y","relation":{},"ISSN":["2524-4922","2524-4930"],"issn-type":[{"value":"2524-4922","type":"print"},{"value":"2524-4930","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,16]]},"assertion":[{"value":"5 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}