| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/47P5RRB | |||
| Repository | sid.inpe.br/mtc-m21d/2022/10.06.11.57 | |||
| Last Update | 2022:10.06.11.57.20 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/10.06.11.57.20 | |||
| Metadata Last Update | 2023:01.03.16.46.20 (UTC) administrator | |||
| DOI | 10.5194/gmd-2022-50 | |||
| ISSN | 1991-962X 1991-9611 | |||
| Citation Key | AlmeidaCampFranEbec:2022:NeNeDa | |||
| Title | Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro  | |||
| Year | 2022 | |||
| Month | Sept. | |||
| Access Date | 2024, May 16 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 1400 KiB | |||
| 2. Context | ||||
| Author | 1 Almeida, Vinícius Albuquerque de 2 Campos Velho, Haroldo Fraga de 3 França, Gutemberg Borges 4 Ebecken, Nelson Francisco Favilla | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHC3 | |||
| Group | 1 2 COPDT-CGIP-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Universidade Federal do Rio de Janeiro (UFRJ) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Universidade Federal do Rio de Janeiro (UFRJ) 4 Universidade Federal do Rio de Janeiro (UFRJ) | |||
| Author e-Mail Address | 1 vinicius@lma.ufrj.br 2 haroldo.camposvelho@inpe.br | |||
| Journal | Geoscientific Model Development Discussions | |||
| Volume | 2022 | |||
| Secondary Mark | B4_INTERDISCIPLINAR B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS | |||
| History (UTC) | 2022-10-06 11:57:20 :: simone -> administrator :: 2022-10-06 11:57:21 :: administrator -> simone :: 2022 2022-10-06 11:57:40 :: simone -> administrator :: 2022 2023-01-03 16:46:20 :: 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 | |||
| Abstract | The practical feasibility of neural networks models for data assimilation using local observations data in the WRF model for the Rio de Janeiro metropolitan region in Brazil is evaluated. Surface and multi-level variables retrieved from airport meteorological stations are used: air temperature, relative humidity, and wind (speed and direction). Also, 6-hour forecast from WRF high-resolution simulations are used domain centered in the Rio de Janeiro city with nested grids of 8 and 2.6 km. Periods of 168h from 2015-2019 are used with 6h and 12h assimilation cycles for surface and upper-air data, respectively, applied to 6-hour forecast fields. The observed data (interpolated to grid points close to airport locations and influence computed in its surroundings) and short-range forecasts are used as input for training model and the 3D-Var analysis on 6-hour forecast fields for each grid point is used as target variable. The neural network models are built using two different approaches: WEKA multilayer perceptron model and TensorFlows deep learning implementation. The year of 2019 is used as an independent dataset for forecast validation from the trained models. Results employing 6-hour forecast fields with neural network models are able to emulate the 3D-Var results for surface and multi-level variables, with better results for the NN-TensoFlow implementation. The main result refers to CPU time reduction enabled by the neural networks models, reducing the data assimilation CPU-time by 121 times and 25 times for NN-TensorFlow and NN-WEKA, respectively, in comparison to the 3D-Var method under the same hardware configurations. | |||
| Area | COMP | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Neural networks 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/47P5RRB | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGP3W34T/47P5RRB | |||
| Language | en | |||
| Target File | gmd-2022-50.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/46KUES5 | |||
| Citing Item List | sid.inpe.br/mtc-m21/2012/07.13.14.49.40 4 sid.inpe.br/bibdigital/2022/04.03.23.11 3 | |||
| Dissemination | PORTALCAPES | |||
| 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 keywords label lineage mark mirrorrepository nextedition notes number orcid pages 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 | ||||