<figure><img src="image_1.png" /></figure>

HAL
open science

# Impact of biometeorological variable uncertainties for rational planning: example from a tropical urban environment, Singapore

Manon Kohler, Matthias Roth, Cécile Tannier, Winston Chow

► To cite this version:

Manon Kohler, Matthias Roth, Cécile Tannier, Winston Chow. Impact of biometeorological variable uncertainties for rational planning: example from a tropical urban environment, Singapore. ICUC12 - 12th International Conference on Urban Climate, Jul 2025, Rotterdam, Netherlands. hal-05262915

HAL Id: hal-05262915

https://hal.science/hal-05262915v1

Submitted on 16 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Impact of biometeorological variable uncertainties for rational planning: example from a tropical urban environment, Singapore.

## KOHLER¹,² M., M. ROTH², C. TANNIER¹, and W.T.L. CHOW³

1. CNRS, Marie et Louis Pasteur University, Laboratoire ThéMA, UMR6049, Besançon

2. National University of Singapore, Department of Geography, Singapore.

3. Singapore Management University, College of Integrative Studies, Singapore.

## Background and Purpose

Climate-responsive designs in master plans usually follow rational steps that include: **defining a climate issue** (e.g., overheating), **setting planning objectives** (reducing neighbourhood's heat exposure), and evaluating alternative design solutions with microclimate models using **performance criteria** (the provision of minimum time in acceptable heat conditions), **performance metrics and indices** (outdoor thermal comfort autonomy; heat stress indices) **as well as performance thresholds** (no-stress conditions for at least 50% of the time).

While based on numbers and rational, this approach is prone to errors or uncertainties—such as defining ‘heat acceptability’ (comfort or stress), selecting an appropriate heat stress index (here, the mPET index for Tropics), and setting values to its no-stress zone boundaries that can be locally adapted using temperature sensation votes and statistical inferential or a probabilistic model (OTC scale).

**AIM:** Assess the sensitivity of the mPET index, OTC scale and OTCA metrics to input uncertainties to identify sources of error in the planning rational approach for climate-responsive designs.

## Sensitivity tests and bias analyses

### a) Sensitivity of the mPET index (86 scenarios)

**Personal data scenarios**

3 atmospheric condition clusters:

* cluster 1: warm (29.6 °C) and humid (28.9 hPa), surface=air temperature

* cluster 2: intermediate (mean radiant temperature: 40 °C)

* cluster 3: warm, humid (31.5 hPa), hot surface (60 °C) but calm wind (> 1m.s⁻¹)

<table><thead><tr><th>Body anthropometry</th><th>Metabolic extra-heat (QM)</th><th>Clothing insulation (1 clo = 0,155 m².K.W⁻¹)</th></tr></thead><tbody><tr><td>48 scenarios</td><td>22 scenarios</td><td>16 scenarios</td></tr><tr><td>2 sexes: male and female</td><td>2 sexes: male and female</td><td>2 sexes: male and female</td></tr><tr><td>2 ages:<br>21 or 65 year old</td><td>3 body weights:<br>• underweight<br>• healthy<br>• obese</td><td></td></tr><tr><td>12 scenarios:<br>4 local BMIs<br>3 body heights</td><td>8 scenarios:<br>from 60 to 120 W.m⁻² (QM= +10 W.m⁻²)<br>214 W.m⁻²</td><td>8 scenarios:<br>from 0.3 clo to 1 clo<br>+ 0.1 clo</td></tr></tbody></table>

**Meteorological input data scenarios**

(Personal conditions = PET index)

* Case M1: 27 scenarios of all combinations X/X/X⁺ of errors provided by the meteorological sensor manufacturer

* Case M2: 2 scenarios considering the local placement of the sensors in field

**Criteria:** A change in mPET is significant when mPET varies more than +/- 0.5 °C

<caption>Table 1. Sensitivity tests for the mPET index. When unspecified, mPET parameters are the same as for PET.</caption>

### b) Biases of air temperature sensation votes (TSV) (4 survey sites for 449 commuters)

<figure><img src="image_3.png" /><figcaption>TSV modalities</figcaption></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Other sensation modalities</figcaption></figure>

### c) Sensitivity of the OTC scale and OTCA index (presented here for the Duxton Park in September)

* Ordinary least square regression + CI90 % (classical approach)
  → drawback: binning data (here, every 1°C) = loss of data; presumed same cognitive distance between two consecutive sensations.

* Ordinal logistic regression (Brager et al., 1993, ORL)
  → drawback: no binning but requires at least 10 votes per sensation modality; votes should follow an ordinal pattern.

* Outdoor Thermal Comfort Autonomy in % (OTCA, Nazarian et al., 2019) using September 14th and 25th 2018 as examples:

$$OTCA = \frac{1}{N} \frac{1}{(hf - hi)} \sum_{k=1}^{N} \sum_{hr=hi}^{hf} mPET_{k,hr}$$

N: number of days considered

k and hr: a given day and given hour hr

hi and hf: start and end hours of the commuting time periods

mPET<sub>k,hr</sub> counts for 1 if included in the no-stress zone, 0 otherwise.

<figure><img src="image_8.png" /><figcaption>## Biometeorological campaigns beyond the reference scenario (REF)</figcaption></figure>

Source: Goggle Earth 2016

<figure><img src="image_9.png" /><figcaption>Figure 1. Measurement sites in Tanjong Pagar district (Singapore, Köppen climate Af)</figcaption></figure>

8 days of investigation:

* in September 2018 and 2019 (unusual light breeze and clear sky)

* in February/March 2019 (driest conditions since 1929)

= mPET [23 to 48.4 °C] for all sites

<figure><img src="image_10.png" /><figcaption>Figure 2. Ordinal logistic regression.</figcaption></figure>

* Warm and hot modalities are gathered to ensure the ordinal pattern of the data (Harrell, 2015).

* The sum of all TSV probabilities is equal to 1 for a given mPET, OTCA can be computed as the weighted average of the probabilities of TSV(-1) and TSV(1) for given mPET values observed during the period of evaluation:

<table><thead><tr><th>Scenarios</th><th>OTCA (%)</th></tr></thead><tbody><tr><td>Reference case</td><td>39.2</td></tr><tr><td>Extra metabolic heat:<br>sedentary activity 70 W.m<sup>-2</sup><br>walking 110 W.m<sup>-2</sup></td><td>35.2<br>44.5</td></tr><tr><td>Tg<sup>+</sup></td><td>41.7</td></tr><tr><td>Tg<sup>-</sup></td><td>39.3</td></tr><tr><td>Tg<sup>+</sup>T<sub>air</sub><sup>+</sup>e<sup>+</sup></td><td>41.3</td></tr><tr><td>Tg<sup>-</sup>T<sub>air</sub><sup>-</sup>e<sup>-</sup></td><td>30.6</td></tr><tr><td>Tg<sup>-</sup>T<sub>air</sub><sup>-</sup>e<sup>-</sup></td><td>36.4</td></tr></tbody></table>

## Take away

**While mPET improves on the PET index, it remains:**

* relatively insensitive to personal variable inputs and air humidity,

* sensitive to extra-metabolic heat (which should account for body weight),

* highly sensitive to underestimations of globe and air temperature.

**For the OTC scale and OTCA index:**

* Least squares regression may cause overlapping sensation classes when TSVs vary widely.

* Ordinal logistic regression better accounts for personal TSV differences but needs more survey participants.

<figure><img src="image_11.png" /></figure>

## References:

Brager et al., (1993). A comparison of methods for assessing thermal sensation and acceptability in the field. Proceedings, Thermal comfort: Past, Present, Future, N.Oseland, ed. Building Research Establishment, Watford, United Kingdom, July 1993.

Nazarian et al., (2019). Outdoor thermal comfort autonomy: Performance metrics for climate-conscious urban design. Building and Environment, 155, 145-160.

Harrell, Jr, F. E. (2015). Ordinal logistic regression. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis, Springer series in statistics, 582 p

## Acknowledgment:

The authors are thankful to all research assistants that helped during the fieldwork, the urban climate lab of the NUS geography department for its supports and the provision of meteorological equipment. Data were acquired during the Cooling Singapore CS1.0 and CS1.5 projects under NRF grant and ethic permit S-18-058.