How to analyse wind data

So you've got wind data. Maybe you're scouting for a wind farm site, or just curious about the breeze in your backyard. Whatever the reason, raw numbers from those spinning cups on a mast won't tell you much by themselves. You gotta dig in. The whole point is turning messy measurements into something useful — like "will this spot actually make money?" or "is the wind strong enough to justify building here?" It's part statistics, part modeling, and honestly, a bit of an art form.

What are the first steps in wind data analysis?

First thing's first. You can't trust the data. Not yet. Anemometers freeze up, sensors get struck by lightning, birds do stupid things. So before anything else, you clean house. This is where you play detective, looking for the usual suspects:

• Data Completeness: How much of the time was your equipment actually working? If you're missing more than 10% of the data, your analysis is basically garbage. Aim for 90% recovery or better.
• Range Checks: Wind doesn't blow at 100 m/s. At least not on Earth. Anything above 60 m/s is probably a sensor glitch. Direction should be between 0 and 360 — simple enough.
• Icing Events: When the reading doesn't change for hours, that's ice. Dead giveaway. The anemometer is literally frozen in place.
• Vertical Wind Shear: Got sensors at different heights? They should tell a consistent story. If they don't, something's wrong with one of them.

What is the Weibull distribution and why is it important?

Once your data is clean, you need to understand the wind's personality. How often does it blow at 5 m/s versus 15 m/s? That's where the Weibull distribution comes in. It's basically the industry standard for describing wind regimes. Two parameters do all the work: shape (k) and scale (c).

With these two numbers, you can estimate how many hours per year the wind will blow at any given speed. That's critical for figuring out how much energy a turbine will actually produce. Without the Weibull, you're just guessing.

How do you calculate wind power density?

Mean wind speed is a nice number, but it's a bit of a lie. Because power isn't proportional to wind speed — it's proportional to the cube of wind speed. Double the wind, get eight times the power. That's why wind power density (WPD) matters more.

WPD = 0.5 * ρ * v³

Where:

• ρ (rho) = Air density (kg/m³), which decreases with altitude and increases with temperature.
• v = Wind speed (m/s).

A high WPD means serious wind resource. In practice, you don't just plug in the average speed — you cube each individual measurement, average those cubes, then multiply by air density. Usually done on 10-minute or hourly intervals to capture the real variability. It matters more than you'd think.

What is wind shear and how is it calculated?

Wind doesn't blow the same at 10 meters as it does at 100. Friction from the ground slows it down near the surface. That change with height is wind shear, and it's crucial for extrapolating your measurements up to turbine hub height. The power law is the go-to method:

v₂ = v₁ * (h₂ / h₁)ᵅ

Where:

• v₂ = Wind speed at height h₂.
• v₁ = Wind speed at height h₁.
• α (alpha) = Wind shear exponent.

This alpha is site-specific. Over flat water (offshore), it's around 0.10 to 0.14. Over forests or hills, it can hit 0.25 or higher. You calculate it by solving the equation using data from two or more measurement heights. Simple math, but the results matter a lot.

Expert Insight: "Never rely on a single average shear exponent. Seasonal and diurnal variations in wind shear can be significant. A thorough analysis calculates shear on a monthly or even hourly basis to understand the site's stability profile."

How do you handle wind direction data?

Direction data is all about layout. Where should you place the turbines? Which directions bring the strongest winds? The wind rose is your best friend here — it shows frequency and strength by direction. The process is pretty straightforward:

1. Binning: Split the compass into sectors. 12 sectors (30 degrees each) or 16 (22.5 degrees) are common choices.
2. Frequency Calculation: Count how often the wind comes from each sector. Simple percentages.
3. Energy Rose: This one's more useful for turbine placement. It shows the percentage of total wind energy from each sector, not just frequency. A sector with rare but strong winds might dominate your energy production.

You're also looking for gaps — directions with almost no wind — or signs of turbulence from nearby obstacles. Trees, buildings, hills. They all leave fingerprints in the data.

Checklist for a complete wind data analysis

• Data recovery rate > 90%
• Outlier and icing events flagged and removed
• Weibull distribution parameters (k and c) calculated
• Mean wind speed and wind power density computed
• Wind shear exponent (α) calculated for all heights
• Wind rose and energy rose created
• Seasonal and diurnal patterns identified
• Long-term correlation performed (MCP) to adjust for inter-annual variability
• Turbulence intensity (TI) calculated for each direction sector

Frequently Asked Questions