{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T12:44:03Z","timestamp":1769345043967,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,9,22]]},"DOI":"10.1145\/3429309.3429321","type":"proceedings-article","created":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T05:28:47Z","timestamp":1610429327000},"page":"79-85","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Understanding Deep Learning Decisions in Statistical Downscaling Models"],"prefix":"10.1145","author":[{"given":"Jorge","family":"Ba\u00f1o-Medina","sequence":"first","affiliation":[{"name":"Institute of Physics of Cantabria, ES"}]}],"member":"320","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Predicting Hurricane Trajectories using a Recurrent Neural Network. arXiv:1802.02548 [physics, stat] (Feb","author":"Alemany Sheila","year":"2018"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 9th International Workshop on Climate Informatics: CI","author":"Ba\u00f1o-Medina Jorge","year":"2019"},{"key":"e_1_3_2_1_3_1","unstructured":"Jorge Ba\u00f1o-Medina Rodrigo Manzanas and Jos\u00e9\u00a0Manuel Guti\u00e9rrez. [n.d.]. On the suitability of deep convolutional neural networks for downscaling climate change projections. Submitted to Climate Dynamics4 ([n.\u00a0d.]).  Jorge Ba\u00f1o-Medina Rodrigo Manzanas and Jos\u00e9\u00a0Manuel Guti\u00e9rrez. [n.d.]. On the suitability of deep convolutional neural networks for downscaling climate change projections. Submitted to Climate Dynamics4 ([n.\u00a0d.])."},{"key":"e_1_3_2_1_4_1","volume-title":"Configuration and intercomparison of deep learning neural models for statistical downscaling. Geoscientific Model Development 13, 4 (April","author":"Ba\u00f1o-Medina Jorge","year":"2020"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1175\/2008JHM960.1"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1029\/2017JD028200"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/qj.828"},{"key":"e_1_3_2_1_8_1","volume-title":"Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability. Atmospheric Research 203 (May","author":"Fu Guobin","year":"2018"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.5462"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3354\/cr013091"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0442(2002)015<1731:SDODTI>2.0.CO;2"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0442(2004)017<0640:SOLDTC>2.0.CO;2"},{"key":"e_1_3_2_1_13_1","volume-title":"The R-based climate4R open framework for reproducible climate data access and post-processing. Environmental Modelling & Software 111 (Jan","author":"Iturbide M.","year":"2019"},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the ICML Workshop on Deep Structured Prediction, PMLR.","volume":"70","author":"Larraondo Pablo\u00a0Rozas","year":"2017"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_16_1","volume-title":"Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. (May","author":"Liu Yunjie","year":"2016"},{"key":"e_1_3_2_1_17_1","unstructured":"Ankur Mahesh Maximilian Evans Garima Jain Mattias Castillo Aranildo Lima Brent Lunghino Himanshu Gupta Carlos Gaitan Jarrett\u00a0K Hunt Omeed Tavasoli Patrick\u00a0T Brown and V Balaji. [n.d.]. Forecasting El Ni\u00f1o with Convolutional and Recurrent Neural Networks. ([n.\u00a0d.]).  Ankur Mahesh Maximilian Evans Garima Jain Mattias Castillo Aranildo Lima Brent Lunghino Himanshu Gupta Carlos Gaitan Jarrett\u00a0K Hunt Omeed Tavasoli Patrick\u00a0T Brown and V Balaji. [n.d.]. Forecasting El Ni\u00f1o with Convolutional and Recurrent Neural Networks. ([n.\u00a0d.])."},{"key":"e_1_3_2_1_18_1","volume-title":"Statistical Downscaling and Bias Correction for Climate Research","author":"Maraun Douglas"},{"key":"e_1_3_2_1_19_1","volume-title":"VALUE: A framework to validate downscaling approaches for climate change studies. Earth\u2019s Future 3, 1 (Jan.","author":"Maraun Douglas","year":"2015"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-18-0195.1"},{"key":"e_1_3_2_1_21_1","volume-title":"Principal Component Analysis in Meteorology and Oceanography","author":"Preisendorfer W."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0912-1"},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 9th International Workshop on Climate Informatics","author":"Reimers Christian","year":"2019"},{"key":"e_1_3_2_1_24_1","volume-title":"DeepDownscale: A Deep Learning Strategy for High-Resolution Weather Forecast. In 2018 IEEE 14th International Conference on e-Science (e-Science). 415\u2013422","author":"Rodrigues Rocha","year":"2018"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1029\/2005JD007026"},{"key":"e_1_3_2_1_26_1","unstructured":"Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung Wai\u00a0Kin Wong and Wang-chun WOO. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Advances in neural information processing systems (June 2015).  Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung Wai\u00a0Kin Wong and Wang-chun WOO. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Advances in neural information processing systems (June 2015)."},{"key":"e_1_3_2_1_27_1","volume-title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. (April","author":"Simonyan Karen","year":"2014"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.5911"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098004"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-016-1956-x"},{"key":"e_1_3_2_1_31_1","volume-title":"Learning Deep Features for Discriminative Localization. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE","author":"Zhou Bolei","year":"2016"},{"key":"e_1_3_2_1_32_1","volume-title":"Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. ICLR (Feb","author":"Zintgraf M.","year":"2017"}],"event":{"name":"CI2020: 10th International Conference on Climate Informatics","location":"virtual United Kingdom","acronym":"CI2020"},"container-title":["Proceedings of the 10th International Conference on Climate Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3429309.3429321","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3429309.3429321","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:33Z","timestamp":1750197753000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3429309.3429321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":32,"alternative-id":["10.1145\/3429309.3429321","10.1145\/3429309"],"URL":"https:\/\/doi.org\/10.1145\/3429309.3429321","relation":{},"subject":[],"published":{"date-parts":[[2020,9,22]]},"assertion":[{"value":"2021-01-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}