{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:08:07Z","timestamp":1770829687385,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030865160","type":"print"},{"value":"9783030865177","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86517-7_6","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T10:08:05Z","timestamp":1631182085000},"page":"87-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Network"],"prefix":"10.1007","author":[{"given":"Fengyang","family":"Xu","sequence":"first","affiliation":[]},{"given":"Wencheng","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Yunfei","family":"Du","sequence":"additional","affiliation":[]},{"given":"Zhiguang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yutong","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"6_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"6_CR2","unstructured":"Bernstein, L., Bosch, P., Canziani, O., Chen, Z., Christ, R., Riahi, K.: Ipcc, 2007: climate change 2007: synthesis report (2008)"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, A., Subel, A., Hassanzadeh, P.: Data-driven super-parameterization using deep learning: experimentation with multiscale lorenz 96 systems and transfer learning. J. Adv. Model. Earth Syst. 12(11), e2020MS002084 (2020)","DOI":"10.1029\/2020MS002084"},{"issue":"11","key":"6_CR4","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1175\/1520-0450(1998)037<1385:ANNAFA>2.0.CO;2","volume":"37","author":"F Chevallier","year":"1998","unstructured":"Chevallier, F., Ch\u00e9ruy, F., Scott, N., Ch\u00e9din, A.: A neural network approach for a fast and accurate computation of a longwave radiative budget. J. Appl. Meteorol. 37(11), 1385\u20131397 (1998)","journal-title":"J. Appl. Meteorol."},{"issue":"3","key":"6_CR5","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1175\/WAF-D-14-00105.1","volume":"30","author":"AE Cohen","year":"2015","unstructured":"Cohen, A.E., Cavallo, S.M., Coniglio, M.C., Brooks, H.E.: A review of planetary boundary layer parameterization schemes and their sensitivity in simulating southeastern us cold season severe weather environments. Weather Forecast. 30(3), 591\u2013612 (2015)","journal-title":"Weather Forecast."},{"key":"6_CR6","unstructured":"Crawshaw, M.: Multi-task learning with deep neural networks: a survey. arXiv preprint arXiv:2009.09796 (2020)"},{"issue":"2","key":"6_CR7","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1175\/1520-0493(1972)100<0093:POTPBL>2.3.CO;2","volume":"100","author":"JW Deardorff","year":"1972","unstructured":"Deardorff, J.W.: Parameterization of the planetary boundary layer for use in general circulation models. Mon. Weather Rev. 100(2), 93\u2013106 (1972)","journal-title":"Mon. Weather Rev."},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Gagne, D.J., Christensen, H.M., Subramanian, A.C., Monahan, A.H.: Machine learning for stochastic parameterization: generative adversarial networks in the lorenz\u201996 model. J. Adv. Model. Earth Syst. 12(3), e2019MS001896 (2020)","DOI":"10.1029\/2019MS001896"},{"key":"6_CR9","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323 (2011)"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Guo, Y., Li, Y., Wang, L., Rosing, T.: Adafilter: adaptive filter fine-tuning for deep transfer learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4060\u20134066 (2020)","DOI":"10.1609\/aaai.v34i04.5824"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., Feris, R.: Spottune: transfer learning through adaptive fine-tuning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4805\u20134814 (2019)","DOI":"10.1109\/CVPR.2019.00494"},{"issue":"9","key":"6_CR12","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1175\/MWR3199.1","volume":"134","author":"SY Hong","year":"2006","unstructured":"Hong, S.Y., Noh, Y., Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 134(9), 2318\u20132341 (2006)","journal-title":"Mon. Weather Rev."},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00277"},{"key":"6_CR15","unstructured":"Krasnopolsky, V.M.: Neural network-based forward model for direct assimilation of SSM\/I brightness temperatures. NASA (19980004608) (1997)"},{"issue":"2","key":"6_CR16","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neunet.2006.01.002","volume":"19","author":"VM Krasnopolsky","year":"2006","unstructured":"Krasnopolsky, V.M., Fox-Rabinovitz, M.S.: Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction. Neural Netw. 19(2), 122\u2013134 (2006)","journal-title":"Neural Netw."},{"issue":"5","key":"6_CR17","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1175\/MWR2923.1","volume":"133","author":"VM Krasnopolsky","year":"2005","unstructured":"Krasnopolsky, V.M., Fox-Rabinovitz, M.S., Chalikov, D.V.: New approach to calculation of atmospheric model physics: Accurate and fast neural network emulation of longwave radiation in a climate model. Mon. Weather Rev. 133(5), 1370\u20131383 (2005)","journal-title":"Mon. Weather Rev."},{"issue":"1","key":"6_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/env.2187","volume":"24","author":"W Leeds","year":"2013","unstructured":"Leeds, W., Wikle, C., Fiechter, J., Brown, J., Milliff, R.: Modeling 3-D spatio-temporal biogeochemical processes with a forest of 1-D statistical emulators. Environmetrics 24(1), 1\u201312 (2013)","journal-title":"Environmetrics"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Liu, P., Qiu, X., Huang, X.: Adversarial multi-task learning for text classification. arXiv preprint arXiv:1704.05742 (2017)","DOI":"10.18653\/v1\/P17-1001"},{"key":"6_CR20","unstructured":"Lorenz, E.N.: Predictability: a problem partly solved. In: Proceedings of Seminar on predictability, vol. 1 (1996)"},{"issue":"4","key":"6_CR21","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1002\/wcc.122","volume":"2","author":"N McFarlane","year":"2011","unstructured":"McFarlane, N.: Parameterizations: representing key processes in climate models without resolving them. Wiley Interdisc. Rev. Clim. Change 2(4), 482\u2013497 (2011)","journal-title":"Wiley Interdisc. Rev. Clim. Change"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"GI Parisi","year":"2019","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54\u201371 (2019)","journal-title":"Neural Netw."},{"issue":"39","key":"6_CR23","doi-asserted-by":"publisher","first-page":"9684","DOI":"10.1073\/pnas.1810286115","volume":"115","author":"S Rasp","year":"2018","unstructured":"Rasp, S., Pritchard, M.S., Gentine, P.: Deep learning to represent subgrid processes in climate models. Proc. Nat. Acad. Sci. 115(39), 9684\u20139689 (2018)","journal-title":"Proc. Nat. Acad. Sci."},{"key":"6_CR24","unstructured":"Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)"},{"key":"6_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1007\/978-3-030-01424-7_27","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"C Tan","year":"2018","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270\u2013279. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01424-7_27"},{"issue":"703","key":"6_CR26","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1002\/qj.2974","volume":"143","author":"T Thornes","year":"2017","unstructured":"Thornes, T., D\u00fcben, P., Palmer, T.: On the use of scale-dependent precision in earth system modelling. Q. J. R. Meteorol. Soc. 143(703), 897\u2013908 (2017)","journal-title":"Q. J. R. Meteorol. Soc."},{"issue":"sup1","key":"6_CR27","doi-asserted-by":"publisher","first-page":"234","DOI":"10.2307\/143141","volume":"46","author":"WR Tobler","year":"1970","unstructured":"Tobler, W.R.: A computer movie simulating urban growth in the detroit region. Econ. Geogr. 46(sup1), 234\u2013240 (1970)","journal-title":"Econ. Geogr."},{"key":"6_CR28","doi-asserted-by":"crossref","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. Geoscientific Model Dev. (Online) 12(10), 4261\u20134274 (2019)","DOI":"10.5194\/gmd-12-4261-2019"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Warner, T.T.: Numerical Weather and Climate Prediction. Cambridge University Press (2010)","DOI":"10.1017\/CBO9780511763243"},{"issue":"1837","key":"6_CR30","doi-asserted-by":"publisher","first-page":"2931","DOI":"10.1098\/rsta.2005.1676","volume":"363","author":"PD Williams","year":"2005","unstructured":"Williams, P.D.: Modelling climate change: the role of unresolved processes. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 363(1837), 2931\u20132946 (2005)","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"issue":"1","key":"6_CR31","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/nsr\/nwx105","volume":"5","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Yang, Q.: An overview of multi-task learning. Nat. Sci. Rev. 5(1), 30\u201343 (2018)","journal-title":"Nat. Sci. Rev."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86517-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:07:43Z","timestamp":1757369263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86517-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865160","9783030865177"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86517-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"210","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}