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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/476862B
Repositorysid.inpe.br/mtc-m21d/2022/06.24.10.45   (restricted access)
Last Update2022:06.24.10.45.05 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2022/06.24.10.45.05
Metadata Last Update2023:01.03.16.46.08 (UTC) administrator
DOI10.1051/swsc/2022015
ISSN2115-7251
Citation KeyMuralikrishnaSantViei:2022:ExPoSo
TitleExploring possibilities for solar irradiance prediction from solar photosphere images using recurrent neural networks
Year2022
MonthJune
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size2908 KiB
2. Context
Author1 Muralikrishna, Amita
2 Santos, Rafael Duarte Coelho dos
3 Vieira, Luís Eduardo Antunes
Resume Identifier1
2 8JMKD3MGP5W/3C9JJ4N
ORCID1 0000-0001-9669-0576
2 0000-0002-8313-6688
3 0000-0002-9376-475X
Group1 COPDT-CGIP-INPE-MCTI-GOV-BR
2 COPDT-CGIP-INPE-MCTI-GOV-BR
3 DIHPA-CGCE-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 amita.mk@gmail.com
2 rafaeldcsantos@gmail.com
3 luis.vieira71@googlemail.com
JournalJournal of Space Weather and Space Climate
Volume12
Number19
Secondary MarkB5_GEOCIÊNCIAS
History (UTC)2022-06-24 10:45:21 :: simone -> administrator :: 2022
2022-07-05 16:21:39 :: administrator -> simone :: 2022
2022-12-20 12:10:37 :: simone -> administrator :: 2022
2023-01-03 16:46:08 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsSolar irradiance
TSI
SSI
Recurrent neural network
LSTM
GRU
AbstractStudies of the Sun and the Earth's atmosphere and climate consider solar variability as an important driver, and its constant monitoring is essential for climate models. Solar total and spectral irradiance are among the main relevant parameters. Physical semi-empirical and empirical models have been developed and made available, and they are crucial for the reconstruction of irradiance during periods of data failure or their absence. However, ionospheric and climate models would also benefit from solar irradiance prediction through prior knowledge of irradiance values hours or days ahead. This paper presents a neural network-based approach, which uses images of the solar photosphere to extract sunspot and active region information and thus generate inputs for recurrent neural networks to perform the irradiance prediction. Experiments were performed with two recurrent neural network architectures for short- and long-term predictions of total and spectral solar irradiance at three wavelengths. The results show good quality of prediction for total solar irradiance (TSI) and motivate further effort in improving the prediction of each type of irradiance considered in this work. The results obtained for spectral solar irradiance (SSI) point out that photosphere images do not have the same influence on the prediction of all wavelengths tested but encourage the bet on new spectral lines prediction.
AreaCOMP
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4. Conditions of access and use
Languageen
Target Fileswsc210045.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KTFK8
8JMKD3MGPCW/46KUES5
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.23.11 5
sid.inpe.br/mtc-m21/2012/07.13.14.58.32 1
sid.inpe.br/bibdigital/2022/04.03.17.52 1
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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