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Once you’ve gathered sufficient local measurements at your potential wind farm site, it’s time to make some critical decisions—one of the most important being the MCP process for long-term extrapolation of wind speed time series. Vortex SERIES provides historical wind speed time series with excellent correlation for most sites, but it’s up to you to validate its performance for your specific wind resource assessment. To help you make an informed choice, we provide a 6-month wind speed time series sample download, so you can test the data before committing to a purchase.
Reliable wind speed time series are a cornerstone of robust wind resource assessment once on-site measurements are available. Vortex SERIES complements short- and mid-term measured data by providing historical wind speed time series specifically designed to support MCP-based long-term extrapolation workflows. By extending local observations into multi-decadal reference periods, engineers can reduce uncertainty, assess interannual variability, and improve the bankability of wind energy projects.
The dataset is particularly valuable for evaluating long-term wind behavior under different large-scale climatic conditions, enabling consistency checks between measured data and reanalysis-driven historical wind data. This allows project developers to identify potential biases, seasonal deviations, and long-term trends that may influence energy yield estimates and financial assumptions. Vortex SERIES is therefore well suited for detailed correlation analysis, representativeness studies, and sensitivity testing within standard wind resource assessment methodologies.
When you have local measurements available and need reliable historical wind speed time series for long-term wind resource extrapolation.
Vortex SERIES is recommended when local wind measurements are available and project development requires a robust wind speed time series for long-term extrapolation, uncertainty reduction, and advanced wind resource assessment studies supporting wind farm development.
Once you’ve gathered sufficient local measurements at your potential wind farm site, it’s time to make a critical decision: how to run the MCP process for long-term extrapolation of your wind speed time series. Vortex SERIES provides historical wind speed time series with strong correlation for most sites. To help you validate its performance before committing, we offer a free 6-month wind speed time series sample, allowing you to compare the dataset directly with your own on-site measurements before starting your MCP analysis.
Reliable historical wind speed time series are a cornerstone of robust wind resource assessment once on-site measurements are available. By extending short- and mid-term observations into long-term reference periods, developers can significantly reduce uncertainty and improve confidence in future energy production estimates.
Vortex SERIES complements measured wind data by extending local observations into 10-, 20- or 30-year historical wind speed time series. This long-term reference enables engineers and consultants to perform more reliable MCP (Measure-Correlate-Predict) analyses, evaluate interannual variability, reduce uncertainty and improve the bankability of wind energy projects during technical due diligence and financing.
Historical wind speed datasets also provide an independent reference for validating measurement campaigns and ensuring that short monitoring periods accurately represent the site’s long-term wind climate.
Reliable historical wind speed time series are a cornerstone of robust wind resource assessment once on-site measurements are available. By extending short- and mid-term observations into long-term reference periods, developers can significantly reduce uncertainty and improve confidence in future energy production estimates.
The dataset is particularly valuable for evaluating long-term wind behaviour under different large-scale climatic conditions. Engineers can compare measured data with reanalysis-based historical wind speed time series to identify seasonal deviations, long-term trends and systematic biases that could otherwise affect energy yield predictions.
This additional validation layer helps improve financial models, reduces uncertainty during project development and increases confidence before investment decisions are made, particularly in regions with strong interannual wind variability.
It's a historical wind speed time series product designed to support MCP-based long-term extrapolation once local measurements are already available at your site.
MCP (Measure-Correlate-Predict) is the process of correlating short-term local measurements with long-term historical data to estimate the long-term wind resource at your site.
Yes, Vortex SERIES is recommended specifically for sites where local wind measurements are already available and long-term extrapolation is required.
Data is provided at 3 km resolution, centered on your selected coordinate, for any location worldwide, onshore or offshore.
You can select 10-, 20- or 30-year historical wind speed time series with hourly data.
NCEP, NASA and ECMWF are available as selectable data sources for your time series.
Wind speed and direction, temperature, pressure, and the stability variable RMOL (Monin-Obukhov Length inverse).
Yes, a free 6-month wind speed time series sample is available so you can validate correlation and accuracy for your specific site first.
Yes, it's updated monthly at no additional cost, conditioned to reanalysis data availability.
TXT files compatible with WindPro, WindFarmer, Windographer and other standard wind analysis software.
It's an enhanced MCP technique (based on Tortosa et al. 2014) that improves the correlation between measured and modeled data for more accurate long-term extrapolation.
Use SERIES when you already have local measurements and need long-term wind resource extrapolation; choose LES instead when no reliable measured data exists and you need modeled turbulence characterization.
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Modeled wind resource data for the wind industry.
At any site around the world. Onshore and offshore.
