Siting and exposure: why location changes the reading

The international standard for surface wind measurement, as defined by the World Meteorological Organisation (WMO), specifies a height of 10 metres above ground level in an open, level area. 'Open terrain' implies that the distance to any obstacle should be at least 10 times the height of the obstacle. This standard aims to ensure that measurements are representative of the general wind flow over a region, minimising local disturbances.

For example, if a weather station has a mast with an anemometer at 10 metres, and there is a building 15 metres tall nearby, the WMO guidance suggests the building should be at least 150 metres away (10 × 15 m). If it is closer, the wind readings at that station may be influenced by the building's wake.

This standard is critical for comparability between stations and for calibrating numerical weather prediction models, which typically resolve atmospheric processes at a scale larger than individual obstacles. Deviations from this standard can lead to systematic biases in reported wind speeds and directions.

The '10 times obstacle height' rule is a widely accepted guideline for avoiding significant flow distortion. When wind encounters an obstacle, it is forced to flow over and around it, creating areas of acceleration, deceleration, and turbulence in the wake. The size and shape of the obstacle, as well as the wind speed and direction, determine the extent of this disturbed zone.

Consider a 5-metre-tall tree. To minimise its impact on a 10-metre anemometer, the tree should ideally be at least 50 metres distant. If the anemometer is placed closer, for instance, 20 metres from the tree, the readings are likely to be lower than the true undisturbed wind speed, especially when the wind blows from the direction of the tree towards the anemometer.

This principle is applied in various engineering and meteorological contexts, from siting wind turbines to positioning air quality monitoring stations. The goal is always to obtain data that is as free as possible from local, non-representative influences. The Wind Agent's evidence records document the siting of its reference stations where possible, allowing users to assess the representativeness of the data.

Mounting anemometers on rooftops or masts attached to buildings is common due to accessibility and security. However, this introduces specific biases. Wind flow over and around a building is complex, with acceleration typically occurring over the roof and around corners, and deceleration and turbulence in the lee.

A common effect is an overestimation of wind speed due to the acceleration of flow over the building's leading edge. For example, an anemometer mounted 3 metres above a flat roof of a 15-metre-tall building might experience speeds 10–30% higher than the true 18-metre wind speed, depending on the building's geometry and wind direction. The WMO recommends that anemometers on buildings should be at a height at least 1.5 times the height of the building itself, and ideally 10 metres above the roofline, to reduce these effects.

Furthermore, the direction of the wind can be significantly altered by the building's presence, leading to 'channeling' effects or preferential directions. The Wind Agent flags stations known to be on rooftops or masts on buildings, advising caution when interpreting their data in isolation.

Coastal locations present unique challenges for wind measurement. Proximity to the sea typically means lower surface roughness, leading to higher wind speeds compared to inland sites, especially for winds blowing directly offshore. However, coastal sites can also suffer from 'sector blocking' or 'sheltering' from land features.

For instance, a station on a headland might be highly exposed to winds from the sea but significantly sheltered from winds blowing from the landward side due to cliffs or hills. This creates a directional bias in the observed wind climatology. A wind rose from such a station would show a pronounced difference in frequency and speed for different wind directions.

Consider a station on the west coast of Ireland. It would likely show strong, frequent westerly winds, but if there's a large hill immediately to its east, easterly winds might appear weaker and less frequent than they truly are in the regional flow. The instrument's wind rose chart can highlight these directional biases, showing how certain sectors are under-represented or exhibit lower speeds.

A wind rose graphically displays the frequency and strength of winds from different directions. For a well-exposed site, the shape of the wind rose should broadly reflect the regional climatology, with dominant directions and speeds consistent with synoptic patterns. However, a sheltered site will exhibit noticeable distortions.

Worked Example: Imagine a station located in a valley running north-south, with high ground to the east and west. The wind rose for this station might show:

• Dominant North and South components: Wind is channelled along the valley, appearing more frequent and potentially stronger from these directions.
• Suppressed East and West components: Winds from the east or west are blocked by the high ground, appearing less frequent and significantly weaker, or even absent, in the wind rose. The 'petals' of the rose for these directions would be short or missing, even if regional models predict strong winds from those directions.

Such a wind rose immediately signals that the station's data is not representative of the open-terrain wind flow for all directions. The Wind Agent's wind rose feature allows users to visually inspect these patterns for specific locations, aiding in the interpretation of observed data.

Numerical weather prediction (NWP) models, such as those used by The Wind Agent, calculate wind speeds for a grid cell, which can be several kilometres across. The model's output for a specific location within that cell represents an average or 'effective' wind speed for the entire cell, accounting for its dominant land cover and topography. It does not resolve individual buildings or small-scale terrain features.

This means that a model's forecast for a specific point might differ from a local observation if the observation site is not representative of the broader grid cell. For example, a model might forecast 10 m/s for a grid cell that includes both open farmland and a sheltered harbour. An anemometer in the sheltered harbour might consistently measure 6 m/s, while an exposed one in the farmland measures 9 m/s. Both observations could be 'correct' for their immediate surroundings, but neither perfectly matches the model's grid-average.

The Wind Agent's 'Agreement Spine' and obs_vs_model chart allow users to compare local observations against model forecasts, helping to identify systematic differences that might be due to siting or exposure issues. A consistent offset between observed and modelled data can indicate a representativeness mismatch.

While ideal siting is preferred, it is not always possible. In such cases, methods exist to attempt to correct for exposure effects, though these are complex and introduce uncertainty. One common approach involves developing a 'site calibration' or 'exposure correction factor'. This typically requires a period of simultaneous measurement at the non-standard site and a nearby, well-exposed reference site.

For example, if a sheltered anemometer consistently reads 20% lower than a reference anemometer in open terrain for a given wind direction, a correction factor of 1.25 could be applied to its readings for that direction. These factors are often direction-dependent, as sheltering effects vary with wind angle.

Another method involves using computational fluid dynamics (CFD) models to simulate wind flow over complex terrain or around buildings, but this requires detailed site geometry and significant computational resources. The Wind Agent does not apply these corrections automatically to raw observed data, as they are site-specific and introduce their own assumptions. Instead, it provides the tools for users to understand and interpret the data in context, such as the obs_vs_model chart and the Agreement Spine.

The Wind Agent strives for transparency regarding the quality and representativeness of its data sources. For each observation station, where information is available, we assess its siting against WMO guidelines and publicly available metadata.

Stations identified as having significant exposure issues, such as being located on rooftops without sufficient height above the roofline, or in areas with known topographical sheltering, are flagged within the instrument. This flagging system provides an indication of potential biases in the reported wind data.

It is important to note that a 'poorly exposed' flag does not mean the data is incorrect for that specific point; rather, it suggests that the data may not be representative of the broader, open-terrain wind conditions. Users are encouraged to exercise caution and cross-reference with model forecasts or data from better-exposed nearby stations when making critical decisions. The goal is to provide sufficient context for informed interpretation, rather than to discard valuable local measurements.