Evidence map›Paper›PMID 41986349›Full record

ArticleScientific data2026

A multiresolution weather dataset for the Southwestern South Atlantic (2017-2018).

Luan Vieira, Lívia Sancho, Mauricio S Silva, Elisa Passos, Larissa F R Jacinto, Rebeca S Lyra, Nilton O Moraes, Carina S Bock, Douglas M Nehme, Raquel Toste and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Luan VieiraCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. luan@cos.ufrj.br.ORCID http://orcid.org/0009-0009-8124-0295
Lívia SanchoCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Mauricio S SilvaCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Elisa PassosFaculdade de Oceanografia, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil.
Larissa F R JacintoCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Rebeca S LyraCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Nilton O MoraesCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Carina S BockCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Douglas M NehmeCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Raquel TosteCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Jacques HonigbaumCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Rodrigo S LunaCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Carlos H BeislGeospatial Petroleum, Niterói, Brazil.
Patricia M SilvaCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Adriano O VasconcelosCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Rian C FerreiraCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Cédric EneauTotalEnergies OneTech, Courbevoie, France.
Fernando A RochinhaCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Luiz P F AssadCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Alvaro L G A CoutinhoCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Laura BahienseCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Luiz LandauCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Alexandre G EvsukoffCOPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Southwestern South Atlantic (SWSA) is a key region for climate research and renewable energy assessment, yet high-resolution meteorological data are scarce. We present a multiresolution dataset spanning February 2017-November 2018, combining Weather Research and Forecasting (WRF) simulations with Sentinel-1A/B Synthetic Aperture Radar (SAR) wind fields processed using the CMOD5 model. WRF outputs were generated every 30 minutes for three nested domains (9 km, 3 km, 1 km) through 325 short-term simulations. SAR/CMOD5 wind fields are provided at 500 m and 1 km resolution across 104 acquisition dates. Validation shows strong agreement: daily spatial averages of 10 m wind speed yield RMSE and MAE below 3 m/s on over 93% of acquisition days, while more than 91.5% of pixel-level deviations fall within ±3 m/s. In situ measurements from the Itajaí buoy further confirmed the reliability of both sources. The dataset supports regional climate studies, wind energy resource assessment, and machine-learning applications in forecasting and downscaling, with usage examples included to aid practical adoption.

Identifiers

PMID41986349
PMCPMC13260945

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.