| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/4556G22 | |||
| Repository | sid.inpe.br/mtc-m21d/2021/07.20.18.36 | |||
| Last Update | 2021:07.20.18.36.51 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2021/07.20.18.36.51 | |||
| Metadata Last Update | 2022:04.03.23.14.02 (UTC) administrator | |||
| DOI | 10.3390/rs13132468 | |||
| ISSN | 2072-4292 | |||
| Citation Key | AnochiAlmeCamp:2021:MaLeCl | |||
| Title | Machine Learning for Climate Precipitation Prediction Modeling over South America  | |||
| Year | 2021 | |||
| Month | July | |||
| Access Date | 2024, May 17 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 2065 KiB | |||
| 2. Context | ||||
| Author | 1 Anochi, Juliana Aparecida 2 Almeida, Vinícius Albuquerque de 3 Campos Velho, Haroldo Fraga de | |||
| Resume Identifier | 1 2 3 8JMKD3MGP5W/3C9JHC3 | |||
| ORCID | 1 0000-0003-0769-9750 2 0000-0002-9645-7528 3 0000-0003-4968-5330 | |||
| Group | 1 DIPTC-CGCT-INPE-MCTI-GOV-BR 2 3 COPDT-CGIP-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Universidade Federal do Rio de Janeiro (UFRJ) 3 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 juliana.anochi@inpe.br 2 vinicius@lma.ufrj.br 3 haroldo.camposvelho@inpe.br | |||
| Journal | Remote Sensing | |||
| Volume | 13 | |||
| Number | 13 | |||
| Pages | e2468 | |||
| Secondary Mark | B3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I | |||
| History (UTC) | 2021-07-20 18:36:51 :: simone -> administrator :: 2021-07-20 18:36:51 :: administrator -> simone :: 2021 2021-07-20 18:37:10 :: simone -> administrator :: 2021 2022-04-03 23:14:02 :: administrator -> simone :: 2021 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Keywords | machine learningclimate precipitation predictionneural networksoptimal neural architecturedeep learning | |||
| Abstract | Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models have been developed to tackle different issues in society, but there is still a gap in applications to applied physics. Here, different machine learning models are evaluated for precipitation prediction over South America. Currently, numerical weather prediction models are unable to precisely reproduce the precipitation patterns in South America due to many factors such as the lack of region-specific parametrizations and data availability. The results are compared to the general circulation atmospheric model currently used operationally in the National Institute for Space Research (INPE: Instituto Nacional de Pesquisas Espaciais), Brazil. Machine learning models are able to produce predictions with errors under 2 mm in most of the continent in comparison to satellite-observed precipitation patterns for different climate seasons, and also outperform INPE's model for some regions (e.g., reduction of errors from 8 to 2 mm in central South America in winter). Another advantage is the computational performance from machine learning models, running faster with much lower computer resources than models based on differential equations currently used in operational centers. Therefore, it is important to consider machine learning models for precipitation forecasts in operational centers as a way to improve forecast quality and to reduce computation costs. | |||
| Area | COMP | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Machine Learning for... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Machine Learning for... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGP3W34T/4556G22 | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGP3W34T/4556G22 | |||
| Language | en | |||
| Target File | remotesensing-13-02468.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | allowpublisher allowfinaldraft | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/46KUATE 8JMKD3MGPCW/46KUES5 | |||
| Citing Item List | sid.inpe.br/mtc-m21/2012/07.13.14.49.40 8 sid.inpe.br/bibdigital/2022/04.03.23.11 2 sid.inpe.br/bibdigital/2022/04.03.22.23 1 | |||
| Dissemination | WEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project readpermission rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||