| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/47J5NA8 | |||
| Repository | sid.inpe.br/mtc-m21d/2022/09.05.17.07 (restricted access) | |||
| Last Update | 2022:09.05.17.07.36 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/09.05.17.07.36 | |||
| Metadata Last Update | 2023:01.03.16.46.15 (UTC) administrator | |||
| DOI | 10.1016/j.acags.2022.100099 | |||
| ISSN | 2590-1974 | |||
| Citation Key | SilvaFranRuivCamp:2022:WRMaLe | |||
| Title | Forecast of convective events via hybrid model: WRF and machine learning algorithms  | |||
| Year | 2022 | |||
| Month | Dec. | |||
| Access Date | 2024, May 17 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 14522 KiB | |||
| 2. Context | ||||
| Author | 1 Silva, Yasmin Uchôa da 2 França, Gutemberg Borges 3 Ruivo, Heloisa Musetti 4 Campos Velho, Haroldo Fraga de | |||
| Resume Identifier | 1 2 3 4 8JMKD3MGP5W/3C9JHC3 | |||
| Group | 1 2 3 DIIAV-CGCT-INPE-MCTI-GOV-BR 4 COPDT-CGIP-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Universidade Federal do Rio de Janeiro (UFRJ) 2 Universidade Federal do Rio de Janeiro (UFRJ) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 yasmin@lma.ufrj.br 2 3 helo_mr@hotmail.com 4 haroldo.camposvelho@inpe.br | |||
| Journal | Applied Computing and Geosciences | |||
| Volume | 16 | |||
| Pages | e100099 | |||
| History (UTC) | 2022-09-05 17:07:56 :: simone -> administrator :: 2022 2023-01-03 16:46:15 :: administrator -> simone :: 2022 | |||
| 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 | Atmospheric discharge Convective event Data mining Forecast Machine learning | |||
| Abstract | This presents a novel hybrid 24-h forecasting model of convective weather events based on numerical simulation and machine learning algorithms. To characterize the convective events, 13-year from 2008 up to 2020 of precipitation data from the main airport stations in Rio de Janeiro, Brazil, and atmospheric discharges from the surrounding area of around 150 km are investigated. The Weather Research and Forecasting (WRF) model was used to numerically simulate atmospheric conditions for every day in February, as it is the month with the greatest daily rate of atmospheric discharge for the data period. The p-value hypothesis test (with α=0.05) was applied to each grid point of the numerically predicted variables (defined as an independent attribute) to find those most associated with convective events using the output of the 3-D WRF grid. This one identified 36 attributes (or predictors) that were used as input in the machine learning algorithms' training-test process in this study. Several cross-validation training and testing experiments were carried out using the nine-selected categorical machine learning algorithms and the 36 defined predictors. After applying the boosting technique to the nine previously trained-tested algorithms, the results of the 24-h predictions of convective occurrences were deemed satisfactory. The RandomForest method produced the best results, with statistics values close to perfection, such as POD = 1.00, FAR = 0.02, and CSI = 0.98. The 24-h hindcast utilizing the nine algorithms for the 28 days of February 2019 was very encouraging because it was able to almost recreate the maturation phase of events and their eventual failures were noted during the formation and dissipation phases. The best and worst 24-h hindcast had POD = 0.97 and 0.88, FAR = 0.02 and 0.12, and CSI = 0.94 and 0.78, respectively. | |||
| Area | CST | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Forecast of convective... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Forecast of convective... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
| |||
| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | 1-s2.0-S2590197422000210-main.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Read Permission | deny from all and allow from 150.163 | |||
| 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 4 sid.inpe.br/bibdigital/2022/04.03.22.23 2 sid.inpe.br/bibdigital/2022/04.03.23.11 2 | |||
| Dissemination | PORTALCAPES; SCOPUS. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||