Mean radiant temperature from global-scale numerical weather prediction models

[thumbnail of Open Access]
Preview
Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.
| Preview
Available under license: Creative Commons Attribution

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Di Napoli, C., Hogan, R. J. orcid id iconORCID: https://orcid.org/0000-0002-3180-5157 and Pappenberger, F. (2020) Mean radiant temperature from global-scale numerical weather prediction models. International Journal of Biometeorology, 64 (7). pp. 1233-1245. ISSN 1432-1254 doi: 10.1007/s00484-020-01900-5

Abstract/Summary

In human biometeorology, the estimation of mean radiant temperature (MRT) is generally considered challenging. This work presents a general framework to compute the MRT at the global scale for a human subject placed in an outdoor environment and irradiated by solar and thermal radiation both directly and diffusely. The proposed framework requires as input radiation fluxes computed by numerical weather prediction (NWP) models and generates as output gridded globe-wide maps of MRT. It also considers changes in the Sun’s position affecting radiation components when these are stored by NWP models as an accumulated-over-time quantity. The applicability of the framework was demonstrated using NWP reanalysis radiation data from the European Centre for Medium-Range Weather Forecasts. Mapped distributions of MRT were correspondingly computed at the global scale. Comparison against measurements from radiation monitoring stations showed a good agreement with NWP-based MRT (coefficient of determination greater than 0.88; average bias equal to 0.42 °C) suggesting its potential as a proxy for observations in application studies.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/89996
Identification Number/DOI 10.1007/s00484-020-01900-5
Refereed Yes
Divisions Life Sciences > School of Agriculture, Policy and Development > Department of Agri-Food Economics & Marketing
Publisher Springer
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar