000905743 001__ 905743
000905743 005__ 20230127125339.0
000905743 0247_ $$2Handle$$a2128/30503
000905743 037__ $$aFZJ-2022-00967
000905743 041__ $$aEnglish
000905743 1001_ $$0P:(DE-Juel1)187069$$aJi, Yan$$b0$$eCorresponding author
000905743 1112_ $$aSecond Symposium on Artificial Intelligence for Science, Industry and Society$$conline$$d2021-10-11 - 2021-10-15$$gAISIS 2021$$wMexico
000905743 245__ $$aDeep Learning for weather forecasts
000905743 260__ $$c2021
000905743 3367_ $$033$$2EndNote$$aConference Paper
000905743 3367_ $$2DataCite$$aOther
000905743 3367_ $$2BibTeX$$aINPROCEEDINGS
000905743 3367_ $$2DRIVER$$aconferenceObject
000905743 3367_ $$2ORCID$$aLECTURE_SPEECH
000905743 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1643025846_32038$$xAfter Call
000905743 520__ $$aAccurate weather predictions are highly demanded by society. This study explores the adaptation of state-of-the-art deep learning architectures for video frame prediction in the context of weather applications. Proof-of-concept case studies are performed to 2m temperature forecasts up to 12 hours over central Europe, and precipitation nowcasting up to 2 hours over south China. The pixel-wise loss-based convolutional Long Short Term Memory architectures (ConvLSTM) and GAN’s variant architecture, stochastic adversarial video prediction (SAVP), are used and compared with standard persistent forecasts for 2m temperature, and traditional optical flow method for precipitation, respectively. Mean square error (MSE), anomaly correlation coefficient (ACC), and Structural Similarity Index (SSIM) are utilized to evaluate the 2m temperature forecast. The method of object-based diagnostic evaluation (MODE) was particularly adopted for precipitation nowcasting to evaluate the attributions of rain events in terms of centroid, intensity, and shape. Finally, the sensitivity was performed to test the models' robustness to input variables, target regions, and the number of training samples.
000905743 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000905743 536__ $$0G:(BMBF)01IS18047A$$aVerbundprojekt DeepRain: Effiziente Lokale Niederschlagsvorhersage durch Maschinelles Lernen (01IS18047A)$$c01IS18047A$$x1
000905743 536__ $$0G:(EU-Grant)787576$$aIntelliAQ - Artificial Intelligence for Air Quality (787576)$$c787576$$fERC-2017-ADG$$x2
000905743 536__ $$0G:(EU-Grant)955513$$aMAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513)$$c955513$$fH2020-JTI-EuroHPC-2019-1$$x3
000905743 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x4
000905743 7001_ $$0P:(DE-Juel1)177767$$aGong, Bing$$b1
000905743 7001_ $$0P:(DE-Juel1)180790$$aLangguth, Michael$$b2
000905743 7001_ $$0P:(DE-Juel1)166264$$aMozaffari, Amirpasha$$b3
000905743 7001_ $$0P:(DE-Juel1)187076$$aMache, Karim$$b4
000905743 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b5
000905743 8564_ $$uhttps://aisis-2021.nucleares.unam.mx/sessions/session7/ji/
000905743 8564_ $$uhttps://juser.fz-juelich.de/record/905743/files/Yan_AISIS_2021-10-12.pdf$$yOpenAccess
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000905743 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177767$$aForschungszentrum Jülich$$b1$$kFZJ
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000905743 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)166264$$aForschungszentrum Jülich$$b3$$kFZJ
000905743 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)187076$$aForschungszentrum Jülich$$b4$$kFZJ
000905743 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)6952$$aForschungszentrum Jülich$$b5$$kFZJ
000905743 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000905743 9141_ $$y2021
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000905743 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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