Mean radiant temperature from global-scale numerical weather prediction models

Area of Science:

• Human biometeorology
• Atmospheric sciences
• Environmental modeling

Background:

• Estimating mean radiant temperature (MRT) in outdoor environments is a significant challenge in human biometeorology.
• Existing methods often lack global scale applicability or require extensive observational data.

Purpose of the Study:

• To present a general framework for computing global-scale MRT for human subjects in outdoor settings.
• To integrate solar and thermal radiation data from numerical weather prediction (NWP) models into MRT estimation.

Main Methods:

• Developed a framework utilizing radiation fluxes (direct and diffuse solar and thermal) from NWP models.
• Incorporated solar position variations impacting radiation components.
• Generated gridded, globe-wide MRT maps.

Main Results:

• Successfully computed global MRT distributions using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data.
• Demonstrated good agreement between NWP-based MRT and ground-based measurements (R² > 0.88, bias = 0.42°C).

Conclusions:

• The proposed framework offers a viable method for large-scale MRT estimation.
• NWP-based MRT can serve as a valuable proxy for direct observations in various application studies.