Evidence map›Paper›PMID 32274575›Full record

ArticleInternational journal of biometeorology2020

Mean radiant temperature from global-scale numerical weather prediction models.

Claudia Di Napoli, Robin J Hogan, Florian Pappenberger

Abstract read
In one paragraph

Article in International journal of biometeorology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
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  9. Heat stress in the Caribbean: Climatology, drivers, and trends of human biometeorology indices.International journal of climatology : a journal of the Royal Meteorological Society · 2023
    Article
  10. Article
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  14. Perspectives on biometeorological research on the African continent.International journal of biometeorology · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Claudia Di NapoliEuropean Centre for Medium-Range Weather Forecasts, Reading, UK. claudia.dinapoli@ecmwf.int.ORCID https://orcid.org/0000-0002-4901-3641
Robin J HoganEuropean Centre for Medium-Range Weather Forecasts, Reading, UK.ORCID https://orcid.org/0000-0002-3180-5157
Florian PappenbergerEuropean Centre for Medium-Range Weather Forecasts, Reading, UK.ORCID https://orcid.org/0000-0003-1766-2898

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

MeteorologySolar EnergyHumansSunlightTemperatureWeatherHuman comfortMean radiant temperatureNumerical weather predictionRadiationValidation

Identifiers

PMID32274575
PMCPMC7295834

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.