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

Luan C. V. Silva, 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, Jacques Honigbaum, Rodrigo S. Luna, Carlos H. Beisl, Patricia M. Silva, Adriano O. Vasconcelos, Rian C. Ferreira, Cédric Eneau, Fernando A. Rochinha, Luiz P. F. Assad, Alvaro L. G. A. Coutinho, Laura Bahiense, Alexandre G. Evsukoff

TL;DR

This work delivers a publicly accessible multiresolution wind dataset for the Southwestern South Atlantic by integrating high-temporal-resolution WRF simulations across three nested domains with Sentinel-1 SAR wind fields processed via CMOD5. The dataset spans 2017–2018 and is validated against the Itajaí buoy, showing strong cross-scale agreement with RMSEs around $1.8$ m s$^{-1}$ for global comparisons and buoy-based RMSEs near $2.0$ m s$^{-1}$. The combination of model and satellite wind fields, at resolutions of 500 m and 1 km, provides rich data for regional climate studies, wind energy assessment, and machine-learning applications in forecasting and downscaling, with practical usage guidance and code examples included. The data are hosted in Harvard Dataverse, and the authors provide workflows to facilitate adoption and integration into AI-enabled climate analyses and offshore wind resource studies.

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 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.

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

TL;DR

This work delivers a publicly accessible multiresolution wind dataset for the Southwestern South Atlantic by integrating high-temporal-resolution WRF simulations across three nested domains with Sentinel-1 SAR wind fields processed via CMOD5. The dataset spans 2017–2018 and is validated against the Itajaí buoy, showing strong cross-scale agreement with RMSEs around m s for global comparisons and buoy-based RMSEs near m s. The combination of model and satellite wind fields, at resolutions of 500 m and 1 km, provides rich data for regional climate studies, wind energy assessment, and machine-learning applications in forecasting and downscaling, with practical usage guidance and code examples included. The data are hosted in Harvard Dataverse, and the authors provide workflows to facilitate adoption and integration into AI-enabled climate analyses and offshore wind resource studies.

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 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 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.

Paper Structure

This paper contains 20 sections, 3 equations, 10 figures, 9 tables.

Table of Contents

  1. Background & Summary
  2. Methods
  3. Study area
  4. The Weather Research and Forecasting (WRF) model
  5. Sea surface temperature (SST) boundary condition.
  6. Wind field estimation from synthetic aperture radar (SAR) images
  7. SAR imagery.
  8. The CMOD5 geophysical model.
  9. Data records
  10. WRF data
  11. SAR/CMOD5 data
  12. Itajaí oceanographic buoy
  13. Technical Validation
  14. WRF and SAR/CMOD5 grid comparison
  15. Global analysis.
  16. ...and 5 more sections

Figures (10)

  • Figure 1: Study area. (a) Three-nested domains configured for the Weather Research and Forecasting model (WRF); D01 with 9 km resolution in blue, D02 with 3 km resolution in black, and D03 with 1 km resolution in red. (b) WRF model's most refined grid (D03), selected SAR region and the location of the Itajaí meteo-oceanographic buoy.
  • Figure 2: (a) Bright object detection mask produced by the SNAP platform, highlighting potential ship targets; (b) detailed view of the corresponding objects identified in the SAR image.
  • Figure 3: Approximate (pressure-dependent) height above mean sea level for each WRF-staggered vertical index, computed as $(PH + PHB)/g$, where $PH$ and $PHB$ are are the perturbation and base-state geopotential respectively, and $g$ is the gravitational acceleration.
  • Figure 4: Maps of the wind fields at 10 m from the 9km (D01), 3km (D02) and 1 km (D03) grids of the August 1st, 2017 run of the WRF model.
  • Figure 5: Maps of 2-metre temperature fields from the 9 km (D01), 3 km (D02) and 1 km (D03) grids of the August 1st, 2017, run of the WRF model.
  • ...and 5 more figures