Dataset on Bias Corrected Near-Surface Wind Speed Model
This dataset catalogs a long-term high-resolution data set characterizing the near-surface hourly wind speed variations at two solar sites in the Southwest U.S. This dataset demonstrates the potential for integrating improved near-surface wind data into CST and PV planning tools for better-informed site selection, improved stow strategies, and structural collector design optimization.
Site-specific wind conditions impact the collector field of concentrating solar power (CSP) plants and tracking photovoltaic (PV) utility plants during the design, installation, operation and maintenance of the field. Despite its importance. Most numerical weather models provide wind data at 10m or higher, and often at coarse spatial and temporal resolution. Wind conditions at solar collector heights of 3m are greatly impacted by surface properties, such as terrain and land use, and historical wind observation data close to the surface is not widely available.
In 2025-2026, NLR developed two Machine Learning (ML) models, Random Forest (RF) and Neural Network (NN), for bias correction of wind speed from the High Resolution Rapid Refresh (HRRR) model input 10m wind speed to more accurate historical near-surface wind data at 3m height relevant to CSP and PV. The corrected output wind speed time series from the ML models were tested and validated against long-term wind observation data records in the Southwest U.S. ranging from 1.5m to 8m heights with an average 20-year operation time and hourly resolution.
Citation Formats
TY - DATA
AB - This dataset catalogs a long-term high-resolution data set characterizing the near-surface hourly wind speed variations at two solar sites in the Southwest U.S. This dataset demonstrates the potential for integrating improved near-surface wind data into CST and PV planning tools for better-informed site selection, improved stow strategies, and structural collector design optimization.
Site-specific wind conditions impact the collector field of concentrating solar power (CSP) plants and tracking photovoltaic (PV) utility plants during the design, installation, operation and maintenance of the field. Despite its importance. Most numerical weather models provide wind data at 10m or higher, and often at coarse spatial and temporal resolution. Wind conditions at solar collector heights of 3m are greatly impacted by surface properties, such as terrain and land use, and historical wind observation data close to the surface is not widely available.
In 2025-2026, NLR developed two Machine Learning (ML) models, Random Forest (RF) and Neural Network (NN), for bias correction of wind speed from the High Resolution Rapid Refresh (HRRR) model input 10m wind speed to more accurate historical near-surface wind data at 3m height relevant to CSP and PV. The corrected output wind speed time series from the ML models were tested and validated against long-term wind observation data records in the Southwest U.S. ranging from 1.5m to 8m heights with an average 20-year operation time and hourly resolution.
AU - Emes, Matthew
A2 - Egerer, Ulrike
A3 - Abraham, Aliza
A4 - Arsalane, Walid
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Laboratory of the Rockies
DO -
KW - energy
KW - power
KW - wind speed
KW - atmosphere
KW - terrain
KW - elevation
KW - surface roughness
KW - model
KW - machine learning
KW - observations
KW - training
KW - testing
KW - southwest U.S.
LA - English
DA - 2026/06/02
PY - 2026
PB - National Laboratory of the Rockies (NLR)
T1 - Dataset on Bias Corrected Near-Surface Wind Speed Model
UR - https://data.openei.org/submissions/8802
ER -
Emes, Matthew, et al. Dataset on Bias Corrected Near-Surface Wind Speed Model. National Laboratory of the Rockies (NLR), 2 June, 2026, Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8802.
Emes, M., Egerer, U., Abraham, A., & Arsalane, W. (2026). Dataset on Bias Corrected Near-Surface Wind Speed Model. [Data set]. Open Energy Data Initiative (OEDI). National Laboratory of the Rockies (NLR). https://data.openei.org/submissions/8802
Emes, Matthew, Ulrike Egerer, Aliza Abraham, and Walid Arsalane. Dataset on Bias Corrected Near-Surface Wind Speed Model. National Laboratory of the Rockies (NLR), June, 2, 2026. Distributed by Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8802
@misc{OEDI_Dataset_8802,
title = {Dataset on Bias Corrected Near-Surface Wind Speed Model},
author = {Emes, Matthew and Egerer, Ulrike and Abraham, Aliza and Arsalane, Walid},
abstractNote = {This dataset catalogs a long-term high-resolution data set characterizing the near-surface hourly wind speed variations at two solar sites in the Southwest U.S. This dataset demonstrates the potential for integrating improved near-surface wind data into CST and PV planning tools for better-informed site selection, improved stow strategies, and structural collector design optimization.
Site-specific wind conditions impact the collector field of concentrating solar power (CSP) plants and tracking photovoltaic (PV) utility plants during the design, installation, operation and maintenance of the field. Despite its importance. Most numerical weather models provide wind data at 10m or higher, and often at coarse spatial and temporal resolution. Wind conditions at solar collector heights of 3m are greatly impacted by surface properties, such as terrain and land use, and historical wind observation data close to the surface is not widely available.
In 2025-2026, NLR developed two Machine Learning (ML) models, Random Forest (RF) and Neural Network (NN), for bias correction of wind speed from the High Resolution Rapid Refresh (HRRR) model input 10m wind speed to more accurate historical near-surface wind data at 3m height relevant to CSP and PV. The corrected output wind speed time series from the ML models were tested and validated against long-term wind observation data records in the Southwest U.S. ranging from 1.5m to 8m heights with an average 20-year operation time and hourly resolution.},
url = {https://data.openei.org/submissions/8802},
year = {2026},
howpublished = {Open Energy Data Initiative (OEDI), National Laboratory of the Rockies (NLR), https://data.openei.org/submissions/8802},
note = {Accessed: 2026-10-06}
}
Details
Data from Jun 2, 2026
Last updated Oct 6, 2026
Submission in progress
Organization
National Laboratory of the Rockies (NLR)
Contact
Matthew Emes
Authors
Research Areas
Keywords
energy, power, wind speed, atmosphere, terrain, elevation, surface roughness, model, machine learning, observations, training, testing, southwest U.S.DOE Project Details
Project Name Improving Near-Surface Wind Data to Support Solar Siting
Project Number EE0053645
