Methodology for mean radiant temperature (Tmrt) estimation from weather station and radiation satellite-derived data representative for standard weather station locations

This repository includes the code to apply the Appendix A of the journal article: The Development of an Adaptive Heat Stress Compensability Classification Applied to the United States, published in the 4th SNP special issue in the International Journal of Biometeorology.

This Python Module code is a methodology for mean radiant temperature (T_mrt) estimation using data from WMO standard weather stations and radiation satellite-derived data representative from standard weather station locations. For this application we used Global horizontal irradiation (GHI), direct normal irradiation (DNI), and diffuse horizontal irradiation (DHI) from the National Solar Radiation Database (NSRDB) (Sengupta et al., 2018). 

For these estimates we assume to be representative of an open space with no close obstacles (sky view factor = 1) over a flat weathered concrete surface with albedo  = 0.186, like the concrete roof surface in Vanos et al. (2021) and emissivity = 0.92, like the C002 material in Kotthaus et al. (2014).  estimates are obtained from mean radiant flux density approach as a weighted combination of 6-directional short- and long-wave radiant fluxes to a standing man using the Stefan-Boltzmann law (VID, 1994) adapted as Middel et al. (2023).  The estimation method was calibrated using Vanos et al. (2021) observations recorded in Tempe, Arizona, USA (33.426°N, 111.940°W) from Aug 21^st through Nov 8^th, 2016, with predominantly low cloud cover, obtaining an  T_mrt RMSE of 2.95°C. 

Please for questions related to this dataset/code contact Gisel Guzman-Echavarria (gguzma20@asu.edu).

VDI (1994) VDI 3789, Part 2: environmental meteorology, interactions between atmosphere and surface; calculation of short-and long wave radiation. VDI/DIN-Handbuch Reinhaltung der Luft, Band 1b, Düsseldorf.

Kotthaus, S., Smith, T. E. L., Wooster, M. J., & Grimmond, C. S. B. (2014). Derivation of an urban materials spectral library through emittance and reflectance spectroscopy. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 194–212. https://doi.org/10.1016/j.isprsjprs.2014.05.005

Middel, A., Huff, M., Krayenhoff, E. S., Udupa, A., & Schneider, F. A. (2023). PanoMRT: Panoramic infrared thermography to model human thermal exposure and comfort. Science of The Total Environment, 859, 160301. https://doi.org/10.1016/J.SCITOTENV.2022.160301

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., & Shelby, J. (2018). The National Solar Radiation Data Base (NSRDB). Renewable and Sustainable Energy Reviews, 89(March 2018), 51–60. https://doi.org/10.1016/j.rser.2018.03.003

Vanos, J. K., Rykaczewski, K., Middel, A., Vecellio, D. J., Brown, R. D., & Gillespie, T. J. (2021). Improved methods for estimating mean radiant temperature in hot and sunny outdoor settings. International Journal of Biometeorology, 65(6), 967–983. https://doi.org/10.1007/s00484-021-02131-y