# Resultant Wind Direction 0–360° ERA5 (`Wind_Degree_Direction_2020_2024_ERA5`) — EDIS AI Agent Dataset View

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## 1. Dataset Metadata
- **Raster Name**: Resultant Wind Direction 0–360° ERA5
- **Slug**: `Wind_Degree_Direction_2020_2024_ERA5`
- **Units**: `0–360° (azimuth)`
- **Spatial Extent**: UL(90.125, -180.0) to LR(-90.125, 180.125)
- **Summary**: This dataset represents the global mean resultant wind direction at 10 meters above ground level derived from ERA5 reanalysis data for the period 2020–2024.
- **Included in Flagship Map Package**: `Global_ISO9223_2020_2024_LE_V4` (`GET https://secure.engineeringdirector.com/api/bundles/Global_ISO9223_2020_2024_LE_V4/point_query`)

### Color Table & Value Breaks
- `(0.0, (0.0, 51.0, 204.0))`
- `(3.0, (0.0, 66.3, 209.1))`
- `(6.0, (0.0, 81.6, 214.2))`
- `(9.0, (0.0, 96.9, 219.3))`
- `(12.0, (0.0, 112.2, 224.4))`
- `(15.0, (0.0, 127.5, 229.5))`
- `(18.0, (0.0, 142.8, 234.6))`
- `(21.0, (0.0, 158.10000000000002, 239.7))`
- `(24.0, (0.0, 173.4, 244.8))`
- `(27.0, (0.0, 188.70000000000002, 249.9))`
- `(30.0, (0.0, 204.0, 255.0))`
- `(30.0, (0.0, 204.0, 255.0))`

# Mean Resultant Wind Direction (2020–2024)

## Overview

This dataset represents the global mean resultant wind direction at 10 meters above ground level derived from ERA5 reanalysis data for the period 2020–2024.

Wind direction is an important environmental variable influencing:

- Atmospheric chloride transport
- Marine aerosol movement
- Coastal exposure severity
- Atmospheric corrosion patterns
- Pollutant transport pathways
- Environmental exposure assessment

The raster represents long-term prevailing wind direction and provides a climatological characterization of dominant atmospheric circulation patterns.

Unlike simple arithmetic averaging of directional angles, this dataset preserves physical wind behavior by computing wind direction from vector-averaged wind components.

**Units:**

- Degrees (0–360°)

---

## Background

Wind direction is a key environmental parameter within atmospheric corrosion and environmental exposure modeling because it influences the direction and persistence of airborne contaminants, marine aerosols, and atmospheric moisture transport.

Wind direction directly affects:

- Chloride transport pathways
- Coastal exposure gradients
- Marine aerosol penetration inland
- Atmospheric pollutant transport
- Wind-driven environmental forcing
- Corrosion exposure patterns

This dataset preserves dominant atmospheric circulation features including:

- Trade Winds
- Mid-Latitude Westerlies
- Polar Easterlies
- Monsoonal Circulation
- Coastal Wind Regimes
- Regional Transport Patterns

This dataset was developed to provide global prevailing wind direction estimates suitable for atmospheric corrosion assessment, environmental modeling, and GIS-based exposure analysis.

---

## Modeling Methodology

The wind direction framework utilizes ERA5 hourly reanalysis data to characterize long-term prevailing wind direction at 10 meters above ground level.

Primary inputs include:

- ERA5 10-meter zonal wind component (u10)
- ERA5 10-meter meridional wind component (v10)

The modeling framework incorporates:

### Wind Vector Averaging

Hourly wind vectors were aggregated to compute long-term mean wind components:

- Mean U Component (ū)
- Mean V Component (v̄)

This approach preserves the physical behavior of atmospheric flow and avoids directional averaging errors.

### Resultant Wind Direction

Wind direction was calculated from the vector-averaged wind components using the meteorological convention:

- 0° = North
- 90° = East
- 180° = South
- 270° = West

Values indicate the direction **from which the wind originates**.

### Temporal Aggregation

Hourly wind observations were aggregated to produce:

- Annual prevailing wind surfaces
- Multi-year climatological averages
- Mean resultant wind direction for 2020–2024

### Spatial Processing

- ERA5 data acquisition
- Global temporal aggregation
- Vector averaging
- Spatial harmonization
- Resampling to approximately 1 km resolution

The resulting dataset supports atmospheric transport analysis, chloride deposition modeling, coastal exposure assessment, and environmental severity mapping.

---

## Interpretation Guidelines

### 8-Point Compass Classification

| Degrees Range | Direction |
|---|---|
| 337.5°–360° | N |
| 0°–22.5° | N |
| 22.5°–67.5° | NE |
| 67.5°–112.5° | E |
| 112.5°–157.5° | SE |
| 157.5°–202.5° | S |
| 202.5°–247.5° | SW |
| 247.5°–292.5° | W |
| 292.5°–337.5° | NW |

### Meteorological Convention

| Direction | Degrees |
|---|---|
| North | 0° |
| East | 90° |
| South | 180° |
| West | 270° |

Values represent the direction from which the wind originates.

---

## Spatial Resolution

| Property | Value |
|---|---|
| Coverage | Global |
| Native Resolution | ~0.25° |
| Published Resolution | ~1 km |
| Coordinate System | WGS 84 |
| EPSG Code | 4326 |
| Temporal Coverage | 2020–2024 |

---

## Data Sources

Primary environmental inputs include:

- [ERA5 Reanalysis Dataset](https://cds.climate.copernicus.eu/)
- [European Centre for Medium-Range Weather Forecasts (ECMWF)](https://www.ecmwf.int/)
- [Copernicus Climate Change Service (C3S)](https://climate.copernicus.eu/)

ERA5 variables utilized:

- 10-meter U Wind Component (u10)
- 10-meter V Wind Component (v10)

Derived environmental layers supported by this dataset include:

- Wind Speed
- WindRIX Terrain–Wind Exposure Index
- Chloride Deposition
- Atmospheric Corrosion Layers
- Environmental Exposure Layers

---

## Intended Applications

This dataset may be used for:

- Atmospheric corrosion assessment
- Chloride deposition modeling
- Coastal exposure analysis
- Marine aerosol transport studies
- Pollutant transport analysis
- Wind exposure assessment
- Environmental severity assessment
- GIS visualization
- Environmental modeling
- Enterprise API workflows

---

## Related Datasets

### Corrosion Layers

- [ISO 9223 Steel Corrosion Rate](https://secure.engineeringdirector.com/public/map/ISO9223_Steel_2020_2024_1km)
- [ISO 9223 Zinc Corrosion Rate](https://secure.engineeringdirector.com/public/map/ISO9223_zinc_2020_2024_1km)
- [ISO 9223 Aluminum Corrosion Rate](https://secure.engineeringdirector.com/public/map/ISO9223_Aluminum_2020_2024_1km)
- [ISO 9223 Copper Corrosion Rate](https://secure.engineeringdirector.com/public/map/ISO9223_Copper_2020_2024_1km)

### Supporting Atmospheric Layers

- [Mean Chloride Deposition](https://secure.engineeringdirector.com/public/map/ISO9223_Chlorides_2020_2024_1km)
- [Mean Sulfate Deposition](https://secure.engineeringdirector.com/public/map/ISO9223_Sulfates_2020_2024_1km)
- [Mean Annual Temperature](https://secure.engineeringdirector.com/public/map/ISO9223_Temp_2020_2024_1km)
- [Mean Relative Humidity](https://secure.engineeringdirector.com/public/map/ISO9223_Humidity_2020_2024_1km)
- [Time of Wetness (TOW)](https://secure.engineeringdirector.com/public/map/tow_global_2020_2024_ERA5)

### Supporting Coastal & Terrain Layers

- [Distance to Coast](https://secure.engineeringdirector.com/public/map/Distance_to_Coast_2013_1km)
- [Bathymetry 2024 – Terrain Elevation](https://secure.engineeringdirector.com/public/map/Bathymetry_2024_1km)
- [WindRIX Terrain–Wind Exposure Index](https://secure.engineeringdirector.com/public/map/windrix_style_2020_2024_ERA5)
- [Wind Resultant Direction (0–360°)](https://secure.engineeringdirector.com/public/map/Wind_Degree_Direction_2020_2024_ERA5)
- [Wind Speed](https://secure.engineeringdirector.com/public/map/wind_speed_2020_2024_ERA5)

---

## Attribution

**Joseph Mazzella**  
AtmosphericIQ LLC  
Engineering Director, Inc.

---

## Dataset Citation

> Mazzella, J. (2026). *Mean Resultant Wind Direction Raster (2020–2024) Derived from ERA5 Reanalysis Data.* AtmosphericIQ LLC / Engineering Director, Inc.

---

## Supporting Dataset Citations

### ERA5 Reanalysis

> Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., et al. (2020). *The ERA5 global reanalysis.* Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049.  
> https://doi.org/10.1002/qj.3803

### Copernicus Climate Data Store

> Copernicus Climate Change Service (C3S). *ERA5 Hourly Data on Single Levels (10 m U and V Wind Components).*  
> https://cds.climate.copernicus.eu/

### ECMWF

> European Centre for Medium-Range Weather Forecasts (ECMWF). *ERA5 Reanalysis Dataset.*  
> https://www.ecmwf.int/

---

## Version Information

| Property | Value |
|---|---|
| Dataset Name | Mean Resultant Wind Direction |
| Dataset Version | 1.0 |
| Publication Year | 2026 |
| Author | Joseph Mazzella |
| Organization | AtmosphericIQ LLC / Engineering Director, Inc. |
| Temporal Coverage | 2020–2024 |
| Native Resolution | ~0.25° |
| Published Resolution | ~1 km |
| Units | Degrees (0–360°) |
| Coordinate System | WGS 84 (EPSG:4326) |
| Coverage | Global |
| Source Dataset | ERA5 Reanalysis |
| Variables Used | u10, v10 |
| Averaging Method | Vector Mean |
| Wind Convention | Meteorological (From Direction) |
| Measurement Height | 10 m Above Ground Level |

---

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