Bias Corrected Near-Surface Wind Data for Solar Field Siting
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. Finally, the ML model was applied to generate a 10-year historical wind speed time series at two sites in the Southwest U.S. with solar resource potential: (1) Generation 3 Particle Pilot Plant (G3P3) at the National Solar Thermal Test Facility (NSTTF), Sandia National Laboratories, New Mexico, (2) Flatirons Campus (FC) at the National Laboratory of the Rockies, Colorado.
Resources
2 resources available
-
FC Timeseries Windspeed RF NN Corrected.csv
CSV -
G3P3 Timeseries Windspeed RF NN Corrected.csv
CSV
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Matthew Emes", "@type": "vcard:Contact", "hasEmail": "mailto:matthew.emes@nlr.gov" } |
| dataQuality |
true
|
| description | 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. Finally, the ML model was applied to generate a 10-year historical wind speed time series at two sites in the Southwest U.S. with solar resource potential: (1) Generation 3 Particle Pilot Plant (G3P3) at the National Solar Thermal Test Facility (NSTTF), Sandia National Laboratories, New Mexico, (2) Flatirons Campus (FC) at the National Laboratory of the Rockies, Colorado. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "FC Timeseries Windspeed RF NN Corrected.csv", "format": "csv", "mediaType": "text/csv", "description": "Hourly averaged 10m height wind speed data and features from HRRR model at FC station from 2015-2024, and bias corrected wind speed data at 3m height from Random Forest (RF) and Neural Network (NN) models. First 2 rows show station features that do not vary with time and variable units. Rows 4-87675 show hourly average time series of variables in Row 3.", "downloadURL": "https://data.openei.org/files/8709/FC_timeseries_windspeed_RF_NN_corrected_zhgCc.csv" }, { "@type": "dcat:Distribution", "title": "G3P3 Timeseries Windspeed RF NN Corrected.csv", "format": "csv", "mediaType": "text/csv", "description": "Hourly averaged 10m height wind speed data and features from HRRR model at G3P3 station from 2015-2024, and bias corrected wind speed data at 3m height from Random Forest (RF) and Neural Network (NN) models. First 2 rows show station features that do not vary with time and variable units. Rows 4-87675 show hourly average time series of variables in Row 3.", "downloadURL": "https://data.openei.org/files/8709/G3P3_timeseries_windspeed_RF_NN_corrected_rSWv1.csv" } ] |
| identifier | https://data.openei.org/submissions/8709 |
| issued | 2026-06-12T06:00:00Z |
| keyword |
[ "Atmosphere", "Bias Correction", "CST", "Flatirons Campus", "Generation 3 Particle Pilot Plant", "HRRR", "ML", "Machine Learning", "NLR", "NSTTF", "Neural Network", "PV", "Random Forest", "Southwest U.S.", "Surface Wind", "Terrain", "Turbulence", "United States", "data", "energy", "near-surface wind", "power", "processed data", "site selection", "solar collector field", "stow strategies", "structural collector design", "wind", "wind speed" ] |
| landingPage | https://data.openei.org/submissions/8709 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2026-07-15T15:42:28Z |
| programCode |
[ "019:000", "019:008", "019:010" ] |
| projectNumber | EE0053645 |
| projectTitle | Improving Near-Surface Wind Data to Support Solar Siting |
| publisher |
{ "name": "National Laboratory of the Rockies (NLR)", "@type": "org:Organization" } |
| spatial |
"{"type":"Polygon","coordinates":[[[-106.522136963664,34.953175483461],[-105.216704198338,34.953175483461],[-105.216704198338,39.9217553101432],[-106.522136963664,39.9217553101432],[-106.522136963664,34.953175483461]]]}"
|
| title | Bias Corrected Near-Surface Wind Data for Solar Field Siting |
