Mean Annual Temperature

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Map Information

This dataset represents modeled global mean annual atmospheric temperature developed to support ISO 9223 atmospheric corrosivity modeling for the period 2020–2024.

Data Source:
Environmental Data
Units:
°C
Coverage:
CONTINENTAL
Citation:
Mazzella, J., Hayden, T. (2026). Mean Annual Temperature Raster (2020–2024). AtmosphericIQ LLC / Engineering Director, Inc.
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Location Analysis
Technical Specifications

Mean Annual Temperature (2020–2024)

Overview

This dataset represents modeled global mean annual atmospheric temperature developed to support ISO 9223 atmospheric corrosivity modeling for the period 2020–2024.

Atmospheric temperature is one of the primary environmental variables influencing:

  • Atmospheric corrosion rates
  • Electrochemical reaction kinetics
  • Chloride transport and persistence
  • Sulfate deposition chemistry
  • Time of Wetness (TOW)
  • Atmospheric moisture dynamics

The raster represents estimated long-term mean annual atmospheric temperature conditions at approximately 1 km spatial resolution.

Units:

  • Degrees Celsius (°C)

Background

Atmospheric temperature is a core environmental variable within the ISO 9223 atmospheric corrosivity framework and strongly influences corrosion kinetics, atmospheric chemistry, and moisture-related corrosion processes.

Temperature directly affects:

  • Electrochemical reaction rates
  • Atmospheric moisture retention
  • Evaporation rates
  • Chloride transport dynamics
  • Sulfate reaction chemistry
  • Time of Wetness behavior

This dataset was developed to provide global temperature estimates suitable for corrosion engineering, environmental modeling, and GIS-based exposure assessment.


Modeling Methodology

The temperature framework integrates atmospheric observations, climate reanalysis products, offshore measurements, and spatial interpolation methods.

Primary temperature inputs include:

  • Surface meteorological observations
  • Offshore buoy observations
  • Climate reanalysis datasets
  • Satellite-derived atmospheric products

The modeling framework incorporates:

Atmospheric Observations

  • NOAA Integrated Surface Database (ISD)
  • Government of Canada climate stations
  • National Data Buoy Center (NDBC) observations

Climate Reanalysis

  • NASA MERRA-2 atmospheric products
  • Regional climate datasets
  • Long-term climatological averages

Spatial Modeling

  • Ordinary Kriging
  • Empirical Bayesian Kriging
  • Spatial interpolation methods
  • Elevation correction techniques

Environmental Integration

  • Terrain effects
  • Coastal influences
  • Regional climate gradients
  • Atmospheric consistency checks

The resulting framework was designed to improve representation of ambient atmospheric temperature behavior across coastal, inland, mountainous, and remote environments.


Interpretation Guidelines

Temperature (°C) Interpretation
< -10 Polar / Extremely Cold
-10 to 0 Cold Continental
0 to 10 Cool Temperate
10 to 20 Moderate Temperate
20 to 30 Warm / Subtropical
>30 Hot / Tropical / Desert

Temperature influences corrosion behavior differently depending on humidity, chloride deposition, sulfate deposition, and moisture persistence.


Spatial Resolution

Property Value
Coverage Global
Resolution ~1 km
Coordinate System WGS 84
EPSG Code 4326
Temporal Coverage 2020–2024

Data Sources

Primary environmental inputs include:

Derived environmental layers include:

  • Relative Humidity
  • Chloride Deposition
  • Sulfate Deposition
  • Time of Wetness (TOW)
  • Atmospheric Corrosion Layers

Intended Applications

This dataset may be used for:

  • Atmospheric corrosion assessment
  • ISO 9223 modeling
  • Climate exposure analysis
  • Environmental severity assessment
  • Corrosion engineering
  • GIS visualization
  • Environmental modeling
  • Enterprise API workflows

Related Datasets

Corrosion Layers

Supporting Atmospheric Layers

Supporting Coastal & Terrain Layers


Attribution

Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.


Dataset Citation

Mazzella, J. (2026). Mean Annual Temperature Raster (2020–2024). AtmosphericIQ LLC / Engineering Director, Inc.


Supporting Dataset Citations

NOAA ISD

NOAA National Centers for Environmental Information (NCEI). Integrated Surface Database (ISD).
https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database

NASA MERRA-2

NASA Global Modeling and Assimilation Office (GMAO). MERRA-2 Atmospheric Reanalysis Dataset.
https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/

Government of Canada Climate Data

Environment and Climate Change Canada. Historical Climate Data.
https://climate.weather.gc.ca/

National Data Buoy Center

National Oceanic and Atmospheric Administration (NOAA). National Data Buoy Center (NDBC).
https://www.ndbc.noaa.gov/

ISO 9223 Standard

ISO 9223:2012. Corrosion of metals and alloys — Corrosivity of atmospheres — Classification, determination and estimation.
https://www.iso.org/standard/53499.html


Version Information

Property Value
Dataset Name Mean Annual Temperature
Dataset Version 1.0
Publication Year 2026
Author Joseph Mazzella
Organization AtmosphericIQ LLC / Engineering Director, Inc.
Temporal Coverage 2020–2024
Resolution ~1 km
Units °C
Coordinate System WGS 84 (EPSG:4326)
Coverage Global

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.