{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:08:04Z","timestamp":1777651684146,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["Grant No. XDA19030402"],"award-info":[{"award-number":["Grant No. XDA19030402"]}]},{"name":"Key Special Projects for International Cooperation in Science and Technology Innovation between Governments","award":["Grant No. 2017YFE0133600"],"award-info":[{"award-number":["Grant No. 2017YFE0133600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance data of space-based satellites with universal coverage can be converted into a proxy field of RR. In this study, a convolutional neural network-based data-driven model is developed to convert the radiance data (infrared bands 07, 09, 13, 16, and 16\u201313) of Himawari-8 into the radar combined reflectivity factor (CREF). A weighted loss function is designed to solve the data imbalance problem due to the sparse convective pixels in the sample. The developed model demonstrates an overall reconstruction capability and performs well in terms of classification scores with 35 dBZ as the threshold. A five-channel input is more efficient in reconstructing the CREF than the commonly used one-channel input. In a case study of a convective event over North China in the summer using the test dataset, U-Net reproduces the location, shape and strength of the convective storm well. The present RR reconstruction technology based on deep learning and Himawari-8 radiance data is shown to be an efficient tool for producing high-resolution RR products, which are especially needed for regions without or with poor radar coverage.<\/jats:p>","DOI":"10.3390\/rs13163330","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T10:24:17Z","timestamp":1629714257000},"page":"3330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations"],"prefix":"10.3390","volume":"13","author":[{"given":"Mingshan","family":"Duan","sequence":"first","affiliation":[{"name":"College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China"},{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangjiang","family":"Xia","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0638-137X","authenticated-orcid":false,"given":"Zhongwei","family":"Yan","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6141-4595","authenticated-orcid":false,"given":"Lei","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"},{"name":"Institute of Urban Meteorology, CMA, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lejian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanmeng","family":"Xia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Yu","sequence":"additional","affiliation":[{"name":"Institut Pierre-Simon Laplace, 4 Place Jussieu, 75005 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1002\/wea.2569","article-title":"Analysis of aircraft flights near convective weather over Europe","volume":"70","author":"Proud","year":"2015","journal-title":"Weather"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1175\/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2","article-title":"Nowcasting Storm Initiation and Growth Using GOES-8 and WSR-88D Data","volume":"18","author":"Roberts","year":"2003","journal-title":"Weather Forecast."},{"key":"ref_3","unstructured":"Li, X. (2018). Remote Sensing Precipitation: Sensors, Retrievals, Validations, and Applications. Observation and Measurement of Ecohydrological Processes, Springer."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1175\/MWR3062.1","article-title":"Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery","volume":"134","author":"Mecikalski","year":"2006","journal-title":"Mon. Weather Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1175\/BAMS-88-10-1589","article-title":"Aviation Applications for Satellite-Based Observations of Cloud Properties, Convection Initiation, In-Flight Icing, Turbulence, and Volcanic Ash","volume":"88","author":"Mecikalski","year":"2007","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4899","DOI":"10.1175\/2008MWR2352.1","article-title":"A Statistical Evaluation of GOES Cloud-Top Properties for Nowcasting Convective Initiation","volume":"136","author":"Mecikalski","year":"2008","journal-title":"Mon. Weather Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1175\/JAMC-D-11-0246.1","article-title":"An Enhanced Geostationary Satellite-Based Convective Initiation Algorithm for 0\u20132-h Nowcasting with Object Tracking","volume":"51","author":"Walker","year":"2012","journal-title":"J. Appl. Meteor. Climatol."},{"key":"ref_8","first-page":"1162","article-title":"Satellite retrieval of precipitation: An overview","volume":"26","author":"Liu","year":"2011","journal-title":"Adv. Atmos. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1175\/1520-0493(1987)115<0051:TRBLSC>2.0.CO;2","article-title":"The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982\u20131984","volume":"115","author":"Arkin","year":"1987","journal-title":"Mon. Weather Rev."},{"key":"ref_10","first-page":"0479","article-title":"Retrieval of Precipitation by Using Himawari-8 Infrared Images","volume":"55","author":"Sun","year":"2019","journal-title":"Acta Sci. Nat. Univ. Pekinensis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s00376-020-0043-5","article-title":"Machine learning\u2212based weather support for the 2022 Winter Olympics","volume":"37","author":"Xia","year":"2020","journal-title":"Adv. Atmos. Sci."},{"key":"ref_12","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the third International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"Volume 39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, H.J., Kim, J.U., Lee, S., Kim, H.G., and Ro, Y.M. (2020, January 13\u201319). Structure Boundary Preserving Segmentation for Medical Image with Ambiguous Boundary. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00487"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wei, X., Liu, F., Chen, J., Zhou, Y., Shen, W., Fishman, E., and Yuille, A. (2020, January 13\u201319). Deep Distance Transform for Tubular Structure Segmentation in CT Scans. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00389"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Beusch, L., Foresti, L., Gabella, M., and Hamann, U. (2018). Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks. Remote Sens., 10.","DOI":"10.3390\/rs10060939"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1175\/JTECH-D-18-0010.1","article-title":"Creating Synthetic Radar Imagery Using Convolutional Neural Networks","volume":"35","author":"Veillette","year":"2018","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8612","DOI":"10.1109\/TGRS.2020.2989183","article-title":"Infrared Precipitation Estimation Using Convolutional Neural Network","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"1","article-title":"Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations","volume":"60","author":"Hilburn","year":"2020","journal-title":"J. Appl. Meteor. Climatol."},{"key":"ref_24","unstructured":"Yasuhiko, S., Hiroshi, S., Takahito, I., and Akira, S. (2017). Convective Cloud Information derived from Himawari-8 data, Meteorological Satellite Center Technical Note."},{"key":"ref_25","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). 2015: U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI, Munich, Germany, 5\u201319 November 2015, Springer."},{"key":"ref_26","first-page":"142","article-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","volume":"48","author":"Ioffe","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_27","unstructured":"Kingma, D., and Ba, J. (2015, January 7\u20139). ADAM: A method for stochastic optimization. Proceedings of the third International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1175\/2008WAF2222159.1","article-title":"Visualizing Multiple Measures of Forecast Quality","volume":"24","author":"Roebber","year":"2008","journal-title":"Weather Forecast."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1175\/1520-0434(1990)005<0576:OSMOSI>2.0.CO;2","article-title":"On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables","volume":"5","author":"Doswell","year":"1990","journal-title":"Weather Forecast."},{"key":"ref_30","first-page":"1245","article-title":"Application analysis of Himawari-8 in Monitoring Heavy Rain Convective Clouds","volume":"44","author":"Zhang","year":"2018","journal-title":"Meteor. Mon."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3330\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:49:40Z","timestamp":1760165380000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,23]]},"references-count":30,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163330"],"URL":"https:\/\/doi.org\/10.3390\/rs13163330","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,23]]}}}