Total Column Water Vapour (TCWV)

Interactive map with scientific data analysis, point lookup, and detailed environmental information

Map Information

Total atmospheric water vapor content integrated through the vertical column, influencing atmospheric attenuation of solar radiation.

Data Source:
Environmental Data
Units:
kg/m²
Coverage:
GLOBAL
Citation:
Mazzella, J. (2026). Total Column Water Vapour Raster, 2020–2024. AtmosphericIQ LLC / Engineering Director, Inc. Derived from ERA5 TCWV. Hersbach et al., 2020 — ERA5 Reanalysis
Data Legend
Values are displayed with colors from lowest (left) to highest (right)
Interactive Environmental Data Map
Click anywhere on the map to get data values for that location
Location Analysis
Technical Specifications

Total Column Water Vapour (TCWV) – Mean Atmospheric Water Vapor Column

Global | ~1 km Resolution | 2020–2024


Short Description

This dataset represents the spatial distribution of total column water vapour (TCWV), defined as the total atmospheric water vapor integrated vertically through the atmospheric column. TCWV influences solar radiation absorption and atmospheric transmissivity.


Technical Specification

The dataset is derived from ERA5 total column water vapour (TCWV) fields and represents mean atmospheric water vapor content for the period 2020–2024.

ERA5 hourly TCWV values were aggregated across the 2020–2024 period to produce a multi-year climatological mean.

This dataset is developed by AtmosphericIQ LLC and processed for distribution and deployment by Engineering Director, Inc.


Key Characteristics

  • Units: kilograms per square meter (kg/m²)
  • Represents vertically integrated atmospheric water vapor content
  • Derived from ERA5 TCWV fields
  • Represents climatological mean conditions for 2020–2024

Spatial Characteristics

  • Coordinate System: GCS_WGS_1984
  • Native Resolution: ~0.25°
  • Resampled Resolution: ~1 km (30 arc-second)
  • Coverage: Global (±85.0511° for web compatibility)

Role in SSI™ Model

Total Column Water Vapour (TCWV) is one of several atmospheric variables incorporated into the Solar Suitability Index (SSI™) model developed by AtmosphericIQ LLC.

As a measure of atmospheric moisture content, TCWV influences solar radiation absorption, scattering, and overall atmospheric transmissivity, contributing to the SSI™ suitability scoring framework.

This dataset is provided as a supporting climatic input layer and does not independently represent solar suitability.


Applications

  • Atmospheric and climatological analysis
  • Solar radiation attenuation studies
  • Environmental and exposure modeling
  • Geospatial screening workflows

Data Source

Derived from Copernicus Climate Change Service (C3S) ERA5 reanalysis data, with processing including temporal aggregation (2020–2024), spatial resampling to ~1 km resolution, and web-compatible clipping.


Related SSI™ Dataset

This dataset is a supporting climatic input to the Solar Suitability Index (SSI™), a global multi-parameter model developed by AtmosphericIQ LLC.

For the full solar suitability model and derived classification datasets, see:
Global Solar Suitability Index (SSI™) – Continuous Score (1–100)


Data Access & API Services

This dataset is available through Engineering Director, Inc. platforms:
www.engineeringdirector.com

Use beyond visualization, including API access, automated queries, or integration, is subject to the Engineering Director API Terms of Service.


Legal & Licensing

© 2026 AtmosphericIQ LLC and Engineering Director, Inc. All rights reserved.

This dataset is provided for informational and analytical use only and is supplied "as is" without warranty of any kind. Users assume all risk associated with its use.

Data Distribution Analysis

These histograms show the distribution of pixel values across the entire raster dataset, helping you understand the range and frequency of different measurements.

Linear Scale Distribution
Shows the actual frequency distribution of values using a standard linear scale.
Logarithmic Scale Distribution
Shows the same data using a logarithmic scale, making it easier to see patterns in data with large value ranges.