A multiresolution weather dataset for the Southwestern South Atlantic (2017-2018)
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917100356894720 |
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| author | Silva, Luan C. V. Sancho, Lívia Silva, Mauricio S. Passos, Elisa Jacinto, Larissa F. R. Lyra, Rebeca S. Moraes, Nilton O. Bock, Carina S. Nehme, Douglas M. Toste, Raquel Honigbaum, Jacques Luna, Rodrigo S. Beisl, Carlos H. Silva, Patricia M. Vasconcelos, Adriano O. Ferreira, Rian C. Eneau, Cédric Rochinha, Fernando A. Assad, Luiz P. F. Coutinho, Alvaro L. G. A. Bahiense, Laura Evsukoff, Alexandre G. |
| author_facet | Silva, Luan C. V. Sancho, Lívia Silva, Mauricio S. Passos, Elisa Jacinto, Larissa F. R. Lyra, Rebeca S. Moraes, Nilton O. Bock, Carina S. Nehme, Douglas M. Toste, Raquel Honigbaum, Jacques Luna, Rodrigo S. Beisl, Carlos H. Silva, Patricia M. Vasconcelos, Adriano O. Ferreira, Rian C. Eneau, Cédric Rochinha, Fernando A. Assad, Luiz P. F. Coutinho, Alvaro L. G. A. Bahiense, Laura Evsukoff, Alexandre G. |
| contents | 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 975 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 residuals fall within $\pm$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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A multiresolution weather dataset for the Southwestern South Atlantic (2017-2018) Silva, Luan C. V. Sancho, Lívia Silva, Mauricio S. Passos, Elisa Jacinto, Larissa F. R. Lyra, Rebeca S. Moraes, Nilton O. Bock, Carina S. Nehme, Douglas M. Toste, Raquel Honigbaum, Jacques Luna, Rodrigo S. Beisl, Carlos H. Silva, Patricia M. Vasconcelos, Adriano O. Ferreira, Rian C. Eneau, Cédric Rochinha, Fernando A. Assad, Luiz P. F. Coutinho, Alvaro L. G. A. Bahiense, Laura Evsukoff, Alexandre G. Atmospheric and Oceanic Physics Geophysics 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 975 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 residuals fall within $\pm$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. |
| title | A multiresolution weather dataset for the Southwestern South Atlantic (2017-2018) |
| topic | Atmospheric and Oceanic Physics Geophysics |
| url | https://arxiv.org/abs/2511.18704 |