{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T17:36:23Z","timestamp":1779212183989,"version":"3.51.4"},"reference-count":76,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,28]],"date-time":"2019-12-28T00:00:00Z","timestamp":1577491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2016M3C4A7952637"],"award-info":[{"award-number":["NRF-2016M3C4A7952637"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017M1A3A3A02015981"],"award-info":[{"award-number":["NRF-2017M1A3A3A02015981"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012261","name":"Ministry of the Interior and Safety","doi-asserted-by":"publisher","award":["2019-MOIS32-015"],"award-info":[{"award-number":["2019-MOIS32-015"]}],"id":[{"id":"10.13039\/501100012261","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["IITP-2019-2018-0-01424"],"award-info":[{"award-number":["IITP-2019-2018-0-01424"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs.<\/jats:p>","DOI":"10.3390\/rs12010108","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T05:49:41Z","timestamp":1577684981000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":103,"title":["Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Juhyun","family":"Lee","sequence":"first","affiliation":[{"name":"School of Urban &amp; Environmental Engineering in Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-6877","authenticated-orcid":false,"given":"Jungho","family":"Im","sequence":"additional","affiliation":[{"name":"School of Urban &amp; Environmental Engineering in Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong-Hyun","family":"Cha","sequence":"additional","affiliation":[{"name":"School of Urban &amp; Environmental Engineering in Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4289-0713","authenticated-orcid":false,"given":"Haemi","family":"Park","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science in the University of Tokyo, A building, 4 Chome-6-1 Komaba, Meguro City, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5426-1511","authenticated-orcid":false,"given":"Seongmun","family":"Sim","sequence":"additional","affiliation":[{"name":"School of Urban &amp; Environmental Engineering in Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,28]]},"reference":[{"key":"ref_1","unstructured":"Seneviratne, S.I., Nicholls, N., Easterling, D., Goodess, C.M., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., and Rahimi, M. (2012). Changes in climate extremes and their impacts on the natural physical environment. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_2","unstructured":"MassonDelmotte, V., Zhai, P., P\u00f6rtner, H.O., Roberts, D., Skea, J., Shukla, P.R., Pirani, A., Moufouma-Okia, W., P\u00e9an, C., and Pidcock, R. (2018). Impacts of 1.5 C Global Warming on Natural and Human Systems. Global Warming of 1.5 C: An. IPCC Special Report on the Impacts of Global Warming of 1.5 C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty, in press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1038\/ngeo779","article-title":"Tropical cyclones and climate change","volume":"3","author":"Knutson","year":"2010","journal-title":"Nat. Geosci."},{"key":"ref_4","unstructured":"World Bank (2012). Information, Communication Technologies, and infoDev (Program). Information and Communications for Development 2012: Maximizing Mobile, World Bank Publications."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/nclimate1357","article-title":"The impact of climate change on global tropical cyclone damage","volume":"2","author":"Mendelsohn","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/S0273-1177(97)00051-3","article-title":"Monitoring deep convection and convective overshooting with METEOSAT","volume":"19","author":"Schmetz","year":"1997","journal-title":"Adv. Space Res."},{"key":"ref_7","unstructured":"Dvorak, V.F. (1984). Tropical cyclone intensity analysis using satellite data, NOAA Technical Report NESDIS, 11."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1175\/1520-0477(1994)075<0757:IGITFO>2.0.CO;2","article-title":"Introducing GOES-I: The First of a New Generation of Geostationary Operational Environmental Satellites","volume":"75","author":"Menzel","year":"1994","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1175\/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2","article-title":"Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery","volume":"103","author":"Dvorak","year":"1975","journal-title":"Mon. Weather Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1175\/WAF975.1","article-title":"The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery","volume":"22","author":"Olander","year":"2007","journal-title":"Weather Forecast."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1175\/WAF-D-19-0007.1","article-title":"The Advanced Dvorak Technique (ADT) for Estimating Tropical Cyclone Intensity: Update and New Capabilities","volume":"34","author":"Olander","year":"2019","journal-title":"Weather Forecast."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1175\/1520-0434(1998)013<0172:DOAOST>2.0.CO;2","article-title":"Development of an Objective Scheme to Estimate Tropical Cyclone Intensity from Digital Geostationary Satellite Infrared Imagery","volume":"13","author":"Velden","year":"1998","journal-title":"Weather Forecast."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1175\/BAMS-87-9-1195","article-title":"The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method that Has Endured for over 30 Years","volume":"87","author":"Velden","year":"2006","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1175\/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2","article-title":"Upper-tropospheric winds derived from geostationary satellite water vapor observations","volume":"78","author":"Velden","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3574","DOI":"10.1109\/TGRS.2008.2000819","article-title":"Objective measures of tropical cyclone structure and intensity change from remotely sensed infrared image data","volume":"46","author":"Ritchie","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1175\/WAF-D-13-00133.1","article-title":"Satellite-derived tropical cyclone intensity in the north pacific ocean using the deviation-angle variance technique","volume":"29","author":"Ritchie","year":"2014","journal-title":"Weather Forecast."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1109\/TIP.2017.2766358","article-title":"Tropical cyclone intensity estimation using a deep convolutional neural network","volume":"27","author":"Pradhan","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Combinido, J.S., Mendoza, J.R., and Aborot, J. (2018, January 20\u201324). A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-based Infrared Images. Proceedings of the 2018 24th ICPR, Beijing, China.","DOI":"10.1109\/ICPR.2018.8545593"},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the ICLR 2015, San Diego, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1175\/MWR-D-18-0391.1","article-title":"Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery","volume":"147","author":"Wimmers","year":"2019","journal-title":"Mon. Weather Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1175\/1520-0493(1996)124<1374:VWSIOT>2.0.CO;2","article-title":"Vertical wind shear influences on tropical cyclone formation and intensification during TCM-92 and TCM-93","volume":"124","author":"Elsberry","year":"1996","journal-title":"Mon. Weather Rev."},{"key":"ref_22","unstructured":"LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., and Jackel, L.D. (US. 1990). Handwritten digit recognition with a back-propagation network. Advances in Neural Information Processing Systems, MITpress."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1080\/15481603.2018.1564499","article-title":"Semantic segmentation of high spatial resolution images with deep neural networks","volume":"56","author":"Yang","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1080\/15481603.2018.1457201","article-title":"Deep learning-based monitoring of overshooting cloud tops from geostationary satellite data","volume":"55","author":"Kim","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1080\/15481603.2017.1323377","article-title":"Deep learning in remote sensing scene classification: A data augmentation enhanced convolutional neural network framework","volume":"54","author":"Yu","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_27","unstructured":"Ou, M.L., and Jae-Gwang-Won, S.R.C. (2005, January 19). Introduction to the COMS Program and its application to meteorological services of Korea. Proceedings of the 2005 EUMETSAT Meteorological Satellite Conference, Dubrovnik, Croatia."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1175\/1520-0493(2004)132<1721:SACAOO>2.0.CO;2","article-title":"Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997\/98","volume":"132","author":"Ralph","year":"2004","journal-title":"Mon. Weather Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.1016\/j.saa.2003.11.050","article-title":"In situ sensing of the middle atmosphere with balloonborne near-infrared laser diodes","volume":"60","author":"Durry","year":"2004","journal-title":"Spectrochim. Acta Part A"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1175\/1520-0434(1997)012<0664:SAFPUG>2.0.CO;2","article-title":"Stratus and fog products using GOES-8\u20139 3.9-\u03bc m data","volume":"12","author":"Lee","year":"1997","journal-title":"Weather Forecast."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1175\/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2","article-title":"Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory","volume":"47","author":"Nakajima","year":"1990","journal-title":"J. Atmos. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"D04208","DOI":"10.1029\/2007JD008600","article-title":"Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase","volume":"113","author":"Rosenfeld","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9485","DOI":"10.5194\/acp-11-9485-2011","article-title":"Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature","volume":"11","author":"Martins","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_34","unstructured":"Lowry, M.R. (2008). Developing a Unified Superset in Quantifying Ambiguities among Tropical Cyclone Best Track Data for the Western North Pacific. [Master\u2019s Thesis, Dept. Meteorology, Florida State University]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1175\/1520-0434(1992)007<0328:JTWCAT>2.0.CO;2","article-title":"Joint Typhoon Warning Center and the Challenges of Multibasin Tropical Cyclone Forecasting","volume":"7","author":"Guard","year":"1992","journal-title":"Weather Forecast."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1175\/2009BAMS2755.1","article-title":"The International Best Track Archive for Climate Stewardship (IBTrACS)","volume":"91","author":"Knapp","year":"2010","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_37","unstructured":"Longadge, R., and Dongre, S. (2013). Class Imbalance Problem in Data Mining Review. arXiv."},{"key":"ref_38","first-page":"1349","article-title":"Unsupervised deep feature extraction for remote sensing image classification","volume":"54","author":"Romero","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/BF00339943","article-title":"\u201cNeural\u201d computation of decisions in optimization problems","volume":"52","author":"Hopfield","year":"1985","journal-title":"Biol. Cybern."},{"key":"ref_41","unstructured":"Amit, D.J. (1992). Modeling Simplified Neurophysiological Information. Modeling Brain Function: The World of Attractor Neural Networks, University Cambridge Press. [1st ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_43","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the NIPS 2012, Lake Tahoe, NV, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TGRS.2002.808238","article-title":"Cloud-drift and water vapor winds in the polar regions from MODIS","volume":"41","author":"Key","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S. (2014, January 23). CNN features off-the-shelf: An astounding baseline for recognition. Proceedings of the IEEE Conference on CVPR Workshop, Columbia, OH, USA.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref_48","unstructured":"Liu, Y., Racah, E., Correa, J., Khosrowshahi, A., Lavers, D., Kunkel, K., Wehner, M., and Collins, W. (2016). Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. arXiv."},{"key":"ref_49","unstructured":"Toms, B.A., Kashinath, K., and Yang, D. (2019). Deep Learning for Scientific Inference from Geophysical Data: The Madden-Julian Oscillation as a Test Case. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1080\/15481603.2019.1628412","article-title":"Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data","volume":"56","author":"Zhou","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/15481603.2018.1489943","article-title":"Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea","volume":"56","author":"Lee","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_52","unstructured":"LeCun, Y., and Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, MIT Press."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going Deeper With Contextual CNN for Hyperspectral Image Classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015, January 7\u201313). Multi-View Convolutional Neural Networks for 3D Shape Recognition. Proceedings of the IEEE ICCV 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"So\u00f3s, B.G., R\u00e1k, \u00c1., Veres, J., and Cserey, G. (2009). GPU Boosted CNN Simulator Library for Graphical Flow-Based Programmability. EURASIP J. Adv. Signal Process., 1\u201311.","DOI":"10.1155\/2009\/930619"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Potluri, S., Fasih, A., Vutukuru, L.K., Al Machot, F., and Kyamakya, K. (2011, January 25\u201327). CNN based high performance computing for real time image processing on GPU. Proceedings of the Joint INDS\u201911 & ISTET\u201911, Klagenfurt, Austria.","DOI":"10.1109\/INDS.2011.6024781"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Becchi, M., and Zong, Z. (2016, January 8\u201310). Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs. Proceedings of the 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), Atlanta, GA, USA.","DOI":"10.1109\/BDCloud-SocialCom-SustainCom.2016.76"},{"key":"ref_59","unstructured":"Hadjis, S., Zhang, C., Mitliagkas, I., Iter, D., and R\u00e9, C. (2016). Omnivore: An Optimizer for Multi-Device Deep Learning on Cpus and Gpus. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.enconman.2013.03.004","article-title":"General models for estimating daily global solar radiation for different solar radiation zones in mainland China","volume":"70","author":"Li","year":"2013","journal-title":"Energy Convers. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1061\/(ASCE)1084-0699(2006)11:6(597)","article-title":"Evaluation of the Nash\u2013Sutcliffe Efficiency Index","volume":"11","author":"McCuen","year":"2006","journal-title":"J. Hydrol. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"885","DOI":"10.13031\/2013.23153","article-title":"Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations","volume":"50","author":"Moriasi","year":"2007","journal-title":"Trans. ASABE."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/00431672.1974.9931702","article-title":"The Hurricane Disaster Potential Scale","volume":"27","author":"Simpson","year":"1974","journal-title":"Weatherwise"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and Understanding Convolutional Networks. Computer Vision\u2014ECCV 2014, Lecture Notes in Computer Science; Springer. Chapter 53.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_66","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., and Oermann, E.K. (2018). Confounding Variables Can Degrade Generalization Performance of Radiological Deep Learning Models. arXiv."},{"key":"ref_67","unstructured":"Dvorak, V.F., and A Technique for the Analysis and Forecasting of Tropical Cyclone Intensities from Satellite Pictures (2019, December 27). Technical Memorandum, Available online: https:\/\/repository.library.noaa.gov\/view\/noaa\/18546."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1175\/MWR-D-12-00077.1","article-title":"A Dynamical Initialization Scheme for Real-Time Forecasts of Tropical Cyclones Using the WRF Model","volume":"141","author":"Cha","year":"2013","journal-title":"Mon. Weather Rev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1175\/2007JAS2528.1","article-title":"Structure and formation of an annular hurricane simulated in a fully compressible, nonhydrostatic model\u2014TCM4","volume":"65","author":"Wang","year":"2008","journal-title":"J. Atmos. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1175\/JAS-D-14-0058.1","article-title":"Spiral rainbands in a numerical simulation of Hurricane Bill (2009). Part I: Structures and comparisons to observations","volume":"72","author":"Moon","year":"2009","journal-title":"J. Atmos. Sci."},{"key":"ref_71","unstructured":"Gal, Y., and Ghahramani, Z. (2016, January 19\u201324). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Proceedings of the ICML 2016, New York, NY, USA."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1177\/0278364918770733","article-title":"The limits and potentials of deep learning for robotics","volume":"37","author":"Brock","year":"2018","journal-title":"Int. J. Robot. Res."},{"key":"ref_73","unstructured":"Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., and Lipson, H. (2015). Understanding Neural Networks through Deep Visualization. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, X., Hovy, E., and Jurafsky, D. (2016). Visualizing and Understanding Neural Models in NLP. arXiv.","DOI":"10.18653\/v1\/N16-1082"},{"key":"ref_75","unstructured":"Samek, W., Wiegand, T., and M\u00fcller, K.R. (2017). Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. arXiv."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1631\/FITEE.1700808","article-title":"Visual interpretability for deep learning: A survey","volume":"19","author":"Zhang","year":"2018","journal-title":"Front. Inf. Technol. Electron. 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