Pith. sign in

REVIEW 3 major objections 4 minor 41 references

Undulating patterns of Hysteresis loops in diurnal seasonality of air temperature in Urban Heat Island effect: Insights from Paris and Madrid

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Paris and Madrid, in different climates, trace the same air-temperature UHI hysteresis loops—clockwise up at night, anticlockwise down by day—pointing to shared time-lag physics.

desk verdict Useful new observation of air-temperature UHI hysteresis in Paris and Madrid, but the mask/reference sensitivity and purely visual comparison keep it short of established. read the letter →

arxiv 2507.13075 v1 pith:OCDLQFXG submitted 2025-07-17 physics.soc-ph physics.data-an

Suman Dharmasthala , Vittal Hari , Rohini Kumar This is my paper · ORCID
classification physics.soc-phphysics.data-an
keywords urbanheatislandairtemperaturehysteresisloopsdiurnalcycleseasonaltimelagParisMadrid
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the seasonal rhythm of urban heat island intensity has a shape common to cities in different climates. It compares Paris (temperate oceanic) with Madrid (cold semi-arid) using ten years of modelled air temperatures at three-hour resolution, and reports that despite large differences in UHI magnitude and seasonal distribution, the two cities' hysteresis loops have the same directions and slopes: nighttime loops are upward-sloping and clockwise, daytime loops are downward-sloping and anticlockwise, and dawn/dusk loops form a twisted figure-eight. If the claim holds, the fundamental shape of air-temperature UHI hysteresis is set by universal time lags in solar radiation and heat storage, with local climate and urban form only stretching or thickening the loop. That would allow planners to design mitigation around shared hysteresis signatures without abandoning city-specific measures.

What carries the argument

The central object is the seasonal hysteresis loop obtained by plotting the urban–rural air-temperature difference $\langle UHI \rangle = \langle T_u\rangle - \langle T_b\rangle$ against the background temperature $\langle T_b\rangle$ for each three-hour clock time, averaged over the 2008–2017 record and over months. Traversal direction (clockwise or anticlockwise) encodes whether urban warmth leads or lags the background seasonal cycle; loop slope encodes how strongly UHI scales with background temperature, and loop thickness and length encode the amplitude of the seasonal excursion. This decomposition lets the paper separate universal phase behaviour from local magnitude effects.

What would settle it

Recompute the loops using a stricter background, for example pixels at least several kilometres outside the urban mask or pixels selected by land-cover class. If the clockwise-night/anticlockwise-day directions or their slopes change substantially, the claimed universality is an artifact of reference-pixel choice; if a third city in another climate yields the same directions, the universal-mechanism claim is supported.

Watch

Extended reading notes

Core claim

Plotting the ten-year average three-hourly urban–rural air-temperature difference $\langle UHI \rangle$ against the background temperature $\langle T_b\rangle$ produces loops whose direction and slope are nearly the same in Paris and Madrid, even though the cities' UHI magnitudes, seasonal peaks, and diurnal variability differ sharply. Nighttime points (21:00–05:00) form an upward-sloping clockwise loop: winter nights have lower UHI than summer nights, and spring nights exceed autumn nights at the same background temperature. Daytime points (09:00–17:00) form a downward-sloping anticlockwise loop: winter days have higher UHI than summer days, and autumn days exceed spring days at the same background temperature. Dawn and dusk points trace a twisted eight. The paper interprets the shared directions as evidence that air-temperature UHI hysteresis is governed by time-dependent mechanisms—solar radiation, heat storage, and release—common to all cities, while differences in loop thickness and length reflect local climate and urban morphology.

Load-bearing premise

All of the shared loop directions rest on the assumption that the reference 'background' pixels are genuinely non-urban; the paper itself concedes the supplied masks may include fringe areas where UHI persists, and a contaminated reference could distort both loop slope and loop direction.

Editorial extensions

If this is right

  • Nighttime UHI should peak in summer and bottom out in winter, with spring nights warmer than autumn nights at equal background temperature—a signature of stored-heat release.
  • Daytime UHI should be strongest in winter and weakest in summer, with autumn days ahead of spring days at equal background temperature, reflecting the solar-radiation and vegetation cycle.
  • The twisted dawn/dusk loops should appear in any city where radiative forcing switches rapidly between storage and release regimes.
  • Loop shape differences (thickness and length) can serve as a local fingerprint, so mitigation can pair shared time-dependent strategies with city-specific interventions.
  • Cities can plausibly be grouped by hysteresis signature for transferable, group-based heat mitigation planning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper does not run: the same signature should appear in a third city with a strictly rural reference; if it does not, the similarity may be an artifact of the shared dataset's urban-rural masks rather than of physics.
  • The loop direction is equivalent to saying the urban temperature lags the rural temperature by a few weeks to months in its seasonal phase; estimating that lag directly by cross-correlation would give a quantitative, model-free check of the proposed mechanism.
  • Madrid's daytime oasis effect combined with the shared loop directions suggests loop slope is controlled mainly by the background temperature range, while loop thickness is controlled by the urban–rural phase difference; the paper does not quantify this decomposition.
  • One could define a dimensionless hysteresis index (loop area, slope sign, direction) and cluster many cities on it; the paper stops at proposing classification in principle.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper analyzes ten years (2008–2017) of hourly, UrbClim-modelled air temperatures for Paris and Madrid, aggregates them to three-hourly intervals, and computes urban heat island intensity (UHI) as the difference between area-weighted urban and background temperatures. It then constructs hysteresis loops by plotting UHI against background temperature, averaged over all years and months for each three-hour period. The central claim is that, despite pronounced climatic and diurnal UHI differences between the two cities, the hysteresis loops show remarkably similar directions (clockwise night-time, anticlockwise daytime) and slopes, with dawn/dusk figure-eight shapes, suggesting universal time-dependent mechanisms such as solar radiation and heat storage. The paper also acknowledges limitations, including the unclear demarcation of urban/rural masks and the use of modelled rather than observed data.

Significance. If the claimed universality of air-temperature UHI hysteresis loop directions were robust, it would provide a useful step toward classifying cities by hysteresis signatures and toward transferring mitigation strategies across climatic contexts. The study benefits from a long, high-resolution dataset and a clear focus on a relatively underexplored air-temperature hysteresis analysis, complementing prior surface-UHI work. However, the central comparison is entirely visual, with no quantitative metrics, uncertainty quantification, or validation of the modelled background temperature against station observations. The acknowledged ambiguity of the urban/rural masks, which are the same data source for both cities, implies that the shared loop directions could be a methodological artifact rather than a universal physical mechanism. These issues materially weaken the strength of the conclusions as currently presented.

major comments (3)
  1. [Section 3 (Figs. 3a–3d)] The central claim that Paris and Madrid exhibit 'remarkably similar' hysteresis loop directions and slopes is supported only by visual inspection. No quantitative descriptors—such as loop slope estimates, rotation direction metrics, loop area, or seasonal phase offsets—are provided, and no confidence intervals or significance tests are given. Because the paper's main conclusion rests on this comparison, the authors should report quantitative measures of loop geometry and uncertainty, and ideally a statistical test of whether the loop directions/slopes differ between the two cities.
  2. [Section 2.3 (Methodology) and Section 3 (Discussion)] The paper explicitly acknowledges that the urban and rural masks are 'not clearly demarcated' and may include peri-urban areas where UHI is still present. Since UHI is defined as T_u − T_b and each hysteresis loop is plotted against exactly T_b, any urban influence in the background mask directly biases the loop's slope, rotation direction, and seasonal phase. The manuscript provides no sensitivity analysis, no alternative mask definition, and no validation of the modelled background temperature against rural station observations. Given that the same mask source (UrbClim) is used for both cities, the shared loop directions could be an artifact of the reference definition rather than evidence of universal mechanisms. This is a load-bearing issue for the study's central claim and must be addressed before the conclusion can be accepted.
  3. [Section 3, final paragraph and Section 4] The interpretation that similar loop directions imply 'universal principles' or 'invariant mechanisms' transcends what the data can support. Only two cities are compared, and the two share the same model and mask generation pipeline, so the similarity may reflect methodological commonalities rather than physical universality. The manuscript should either limit the conclusions to the specific mechanisms demonstrated (e.g., time-of-day dependence) or explicitly discuss and test alternative explanations, such as common biases in the modelled background temperature or in the urban-rural classification.
minor comments (4)
  1. [Throughout] The paper uses inconsistent spelling: 'Koppen' appears in the text and figure caption, while the standard spelling is 'Köppen'; also 'Semi-Srid' in Section 3 should be 'Semi-Arid'.
  2. [Section 2.3] The definitions of daytime (09:00–17:00), nighttime (21:00–05:00), and dawn/dusk windows (06:00–08:00 and 18:00–20:00) appear in the Figure 2 caption and in Section 3, but not in the methodology section; these time-window choices should be stated and justified in the methodology.
  3. [Figure 3] The caption for panels (e) and (f) says 'mean hysteresis averaged across the day,' while the text refers to 'mean hysteresis averaged across the day' only vaguely; clarify what is being averaged (e.g., all 3-hour bins? seasonal means?) and describe the shading/thickness of the loops.
  4. [References] Reference [32] and [35] contain placeholder text 'Accessed: [Insert Date]'; these should be completed and consistently formatted.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the hysteresis comparison is a self-contained empirical analysis, and the acknowledged mask limitation is a validity concern rather than circular reasoning.

full rationale

The paper's analysis chain is entirely data-driven: it computes the urban heat island as <T_uhi> = <T_u> - <T_b> (Sec. 2.3), then plots monthly/diurnally averaged values of <T_uhi> against the background temperature <T_b> for Paris and Madrid. No parameters are fitted, no inversion is performed, and the target conclusion does not enter the definition of UHI. The observed loop directions and slopes are descriptive properties of the two time series; the interpretation that time-dependent mechanisms such as solar radiation and heat storage govern air-temperature UHI is an explanatory claim, not a circular derivation. The paper explicitly acknowledges in Sec. 2.3 and the Conclusions that the urban/rural masks are 'not clearly demarcated' and that the background may include peri-urban areas where UHI effects remain; this is a data-quality and external-validity limitation that could affect loop slopes or phases, but it does not make the reasoning circular. References to prior hysteresis studies such as Zhou et al. and Manoli et al. are external works by non-overlapping authors and are used for context and interpretation, not to define the loop geometry. Consequently, no circular step can be exhibited from the paper's equations or citations.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

No invented entities are introduced. The analysis uses existing constructs (UHI, hysteresis, oasis effect) and public modelled data; the hand-chosen elements are the time-window definitions and averaging choices recorded above.

free parameters (1)
  • Daytime/nighttime/dawn/dusk time-window definitions = day 09-17, night 21-05, dawn 06-08, dusk 18-20
    These hand-chosen windows define the trimodal UHI distribution and the grouping of hysteresis loops by time of day; alternative boundaries could change the reported loop shapes and pattern descriptions.
assumptions (4)
  • domain assumption UrbClim modelled 2 m air temperature accurately represents urban and background air temperatures for UHI analysis
    Invoked in Sections 2.2 and 2.3 without station-based validation; the paper acknowledges modelled data may differ from observations. If the model bias differs between urban and rural pixels, UHI and its hysteresis are biased.
  • domain assumption The urban/rural masks supplied with the UrbClim dataset correctly separate urban from non-urban areas
    Used throughout to compute weighted averages; the paper states in Section 2.3 that the domains are not clearly demarcated and may include peri-urban areas, so the background reference may contain urban influence.
  • domain assumption Averaging hourly data into three-hour bins and then across years and months preserves the orientation of the hysteresis loops
    This averaging is done in Section 2.3 before plotting; if the averaging smooths phase differences between urban and background temperature differently across seasons, the loop direction could be an artefact.
  • domain assumption Visual inspection of loop direction and slope is sufficient to establish similarity
    The central similarity claim in Section 3 is supported only by visual comparison of Figures 3a to 3f; no quantitative slope or orientation statistics are reported.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Undulating patterns of Hysteresis loops in diurnal seasonality of air temperature in Urban Heat Island effect: Insights from Paris and Madrid." pith.science (2026). https://pith.science/paper/OCDLQFXG

@misc{pith2026250713075,
  author       = {Pith},
  title        = {Pith review of: Undulating patterns of Hysteresis loops in diurnal seasonality of air temperature in Urban Heat Island effect: Insights from Paris and Madrid},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OCDLQFXG}},
  note         = {Machine review of arXiv:2507.13075}
}
read the original abstract

This study examines the dynamics of the urban heat island (UHI) effect by conducting a comparative analysis of air temperature hysteresis patterns in Paris and Madrid, two major European cities with distinct climatic and urban characteristics. Utilizing high-resolution modelled air temperature data aggregated at a fine temporal resolution of three-hour intervals from 2008 to 2017, we investigate how diurnal and seasonal hysteresis loops reveal both unique and universal aspects of UHI variability. Paris, located in a temperate oceanic climate, and Madrid, situated in a cold semi-arid zone, display pronounced differences in UHI intensity, seasonal distribution, and diurnal patterns. Despite these contrasts, both cities exhibit remarkably similar hysteresis loop directions and slopes, suggesting that time-dependent mechanisms such as solar radiation and heat storage fundamentally govern air temperature UHI across diverse urban contexts. Our findings underscore the importance of considering both local climate and universal physical processes in developing targeted, climate-resilient urban strategies. The results pave the way for group-based interventions and classification of cities by hysteresis patterns to inform urban planning and heat mitigation efforts.

Figures

Figures reproduced from arXiv: 2507.13075 by the authors.

Figure 1
Figure 1. The map on the left (1a) contains the location of Paris and Madrid in Europe; colours show the Koppen-Gieger [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. (a) shows similar seasonal peaks and troughs in cities of Paris and Madrid UHI, although Paris is much higher [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. (a) and (b) show hysteresis plots of seasonal diurnality (3 hourly); (c) and (d) show the individual time-period [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages

  1. [1]

    Cambridge University Press, 2017

    Timothy R Oke, Gerald Mills, Andreas Christen, and James A V oogt.Urban climates. Cambridge University Press, 2017

    work page 2017

  2. [2]

    Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island

    A John Arnfield. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology: a Journal of the Royal Meteorological Society, 23(1):1–26, 2003

    work page 2003

  3. [3]

    The energetic basis of the urban heat island

    Timothy R Oke. The energetic basis of the urban heat island. Quarterly journal of the royal meteorological society, 108(455):1–24, 1982

    work page 1982

  4. [4]

    Salvatore Eugenio Pappalardo, Carlo Zanetti, and Valeria Todeschi. Mapping urban heat islands and heat-related risk during heat waves from a climate justice perspective: A case study in the municipality of padua (italy) for inclusive adaptation policies. Landscape and Urban Planning, 238:104831, 2023

    work page 2023

  5. [5]

    Giannopoulou, I

    K. Giannopoulou, I. Livada, M. Santamouris, M. Saliari, M. Assimakopoulos, and Y . Caouris. The influence of air temperature and humidity on human thermal comfort over the greater Athens area. Sustainable Cities and Society, 10:184–194, Feb 2014

    work page 2014

  6. [6]

    Santamouris

    M. Santamouris. Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energy Build., 207:109482, Jan 2020

    work page 2020

  7. [7]

    Changes in year-round air temperature and annual energy consumption in office building areas by urban heat-island countermeasures and energy-saving measures

    Tomohiko Ihara, Yukihiro Kikegawa, Kazutake Asahi, Yutaka Genchi, and Hiroaki Kondo. Changes in year-round air temperature and annual energy consumption in office building areas by urban heat-island countermeasures and energy-saving measures. Appl. Energy, 85(1):12–25, Jan 2008

    work page 2008

  8. [8]

    Impact of urban heat island on energy demand in buildings: Local climate zones in nanjing

    Xiaoshan Yang, Lilliana LH Peng, Zhidian Jiang, Yuan Chen, Lingye Yao, Yunfei He, and Tianjing Xu. Impact of urban heat island on energy demand in buildings: Local climate zones in nanjing. Applied Energy, 260:114279, 2020

    work page 2020

Show all 41 references
  1. [9]

    Urban heat island (uhi) intensity and magnitude estimations: A systematic literature review

    Se Woong Kim and Robert D Brown. Urban heat island (uhi) intensity and magnitude estimations: A systematic literature review. Science of the Total Environment, 779:146389, 2021

    2021

  2. [10]

    Kamruzzaman, and Yan Liu

    Kaveh Deilami, Md. Kamruzzaman, and Yan Liu. Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int. J. Appl. Earth Obs. Geoinf., 67:30–42, May 2018

    2018

  3. [11]

    On the linkage between urban heat island and urban pollution island: Three-decade literature review towards a conceptual framework

    Giulia Ulpiani. On the linkage between urban heat island and urban pollution island: Three-decade literature review towards a conceptual framework. Sci. Total Environ., 751:141727, Jan 2021

    2021

  4. [12]

    Magnitude of urban heat islands largely explained by climate and population

    Gabriele Manoli, Simone Fatichi, Markus Schläpfer, Kailiang Yu, Thomas W Crowther, Naika Meili, Paolo Burlando, Gabriel G Katul, and Elie Bou-Zeid. Magnitude of urban heat islands largely explained by climate and population. Nature, 573(7772):55–60, 2019

    2019

  5. [13]

    Urban heat island intensity: A literature review

    Anna Tzavali, John P Paravantis, Giouli Mihalakakou, Angeliki Fotiadi, and Eleni Stigka. Urban heat island intensity: A literature review. Fresenius Environmental Bulletin, 24(12b):4537–4554, 2015

    2015

  6. [14]

    Analyzing the influence of urban forms on surface urban heat islands in europe

    Nina Schwarz and Ameur M Manceur. Analyzing the influence of urban forms on surface urban heat islands in europe. Journal of Urban Planning and Development, 141(3):A4014003, 2015

    2015

  7. [15]

    Exploring indicators for quantifying surface urban heat islands of european cities with modis land surface temperatures

    N Schwarz, S Lautenbach, and R Seppelt. Exploring indicators for quantifying surface urban heat islands of european cities with modis land surface temperatures. Remote sensing of environment, 115(12):3175–3186, 2011

    2011

  8. [16]

    Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city

    Chaobin Yang, Fengqin Yan, and Shuwen Zhang. Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city. Journal of environmental management, 265:110563, 2020

    2020

  9. [17]

    Urban heat island (uhi) variations within a city boundary: A systematic literature review

    Se Woong Kim and Robert D Brown. Urban heat island (uhi) variations within a city boundary: A systematic literature review. Renewable and Sustainable Energy Reviews, 148:111256, 2021

    2021

  10. [18]

    An investigation of urban heat island intensity (uhii) as an indicator of urban heating

    Rizwan Ahmed Memon, Dennis YC Leung, and Chun-Ho Liu. An investigation of urban heat island intensity (uhii) as an indicator of urban heating. Atmospheric Research, 94(3):491–500, 2009

    2009

  11. [19]

    Assessment of heat exposure in cities: Combining the dynamics of temperature and population

    Leiqiu Hu, Olga V Wilhelmi, and Christopher Uejio. Assessment of heat exposure in cities: Combining the dynamics of temperature and population. Science of the total environment, 655:1–12, 2019

    2019

  12. [20]

    Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity

    Yuan Shi, Lutz Katzschner, and Edward Ng. Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity. Science of the Total Environment, 618:891–904, 2018

    2018

  13. [21]

    Urban heat island impacts on heat-related cardiovascular morbidity: A time series analysis of older adults in us metropolitan areas

    Stephanie E Cleland, William Steinhardt, Lucas M Neas, J Jason West, and Ana G Rappold. Urban heat island impacts on heat-related cardiovascular morbidity: A time series analysis of older adults in us metropolitan areas. Environment international, 178:108005, 2023. 8

    2023

  14. [22]

    Impact of land cover data on the simulation of urban heat island for berlin using wrf coupled with bulk approach of noah-lsm

    Huidong Li, Michael Wolter, Xun Wang, and Sahar Sodoudi. Impact of land cover data on the simulation of urban heat island for berlin using wrf coupled with bulk approach of noah-lsm. Theoretical and applied climatology, 134:67–81, 2018

    2018

  15. [23]

    Surface urban heat island of ias, i city (romania) and its differences from in situ screen-level air temperature measurements

    Lucian Sfîc˘a, Claudiu-S, tef˘anel CreT, U, Pavel Ichim, Robert Hri T, AC, and Iuliana-Gabriela Breab ˘an. Surface urban heat island of ias, i city (romania) and its differences from in situ screen-level air temperature measurements. Sustainable Cities and Society, 94:104568, 2023

    2023

  16. [24]

    The seasonality of surface urban heat islands across climates

    Panagiotis Sismanidis, Benjamin Bechtel, Mike Perry, and Darren Ghent. The seasonality of surface urban heat islands across climates. Remote Sensing, 14(10):2318, 2022

    2022

  17. [25]

    Seasonal hysteresis of surface urban heat islands

    Gabriele Manoli, Simone Fatichi, Elie Bou-Zeid, and Gabriel G Katul. Seasonal hysteresis of surface urban heat islands. Proceedings of the National Academy of Sciences, 117(13):7082–7089, 2020

    2020

  18. [26]

    Assessing seasonality in the surface urban heat island of london.Journal of Applied Meteorology and Climatology, 55(3):493– 505, 2016

    Bin Zhou, Dirk Lauwaet, Hans Hooyberghs, Koen De Ridder, Jürgen P Kropp, and Diego Rybski. Assessing seasonality in the surface urban heat island of london.Journal of Applied Meteorology and Climatology, 55(3):493– 505, 2016

    2016

  19. [27]

    The hysteresis effect on surface-air temperature relationship and its implications to urban planning: An examination in phoenix, arizona, usa

    Jiyun Song, Zhi-Hua Wang, Soe W Myint, and Chuyuan Wang. The hysteresis effect on surface-air temperature relationship and its implications to urban planning: An examination in phoenix, arizona, usa. Landscape and Urban Planning, 167:198–211, 2017

    2017

  20. [28]

    On the statistics of urban heat island intensity

    Bin Zhou, Diego Rybski, and Jürgen P Kropp. On the statistics of urban heat island intensity. Geophysical research letters, 40(20):5486–5491, 2013

    2013

  21. [29]

    Present and future köppen-geiger climate classification maps at 1-km resolution

    Hylke E Beck, Niklaus E Zimmermann, Tim R McVicar, Noemi Vergopolan, Alexis Berg, and Eric F Wood. Present and future köppen-geiger climate classification maps at 1-km resolution. Scientific data, 5(1):1–12, 2018

    2018

  22. [30]

    Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in paris (france)

    Aude Lemonsu, Vincent Viguié, Marguerite Daniel, and Valéry Masson. Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in paris (france). Urban Climate, 14:586–605, 2015

    2015

  23. [31]

    Urban ecosystem services assessment along a rural-urban gradient: A cross-analysis of european cities

    Neele Larondelle and Dagmar Haase. Urban ecosystem services assessment along a rural-urban gradient: A cross-analysis of european cities. Ecological Indicators, 29:179–190, 2013

    2013

  24. [32]

    Population density in paris

    INSEE. Population density in paris. https://www.insee.fr, 2020. Accessed: [Insert Date]

    2020

  25. [33]

    Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects

    José A Sobrino, Rosa Oltra-Carrió, Guadalupe Sòria, Remo Bianchi, and Marc Paganini. Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sensing of Environment, 117:50–56, 2012

    2012

  26. [34]

    Experimental investigation on the thermal comfort in the city: Relationship with the green areas, interaction with the urban microclimate

    Francisco Gómez, Luis Gil, and Javier Jabaloyes. Experimental investigation on the thermal comfort in the city: Relationship with the green areas, interaction with the urban microclimate. Building and Environment, 39(9):1077–1086, 2004

    2004

  27. [35]

    Population density in madrid

    Instituto Nacional de Estadística (INE). Population density in madrid. https://www.ine.es, 2020. Accessed: [Insert Date]

    2020

  28. [36]

    Climate variables for cities in europe from 2008 to 2017

    Copernicus Climate Change Service. Climate variables for cities in europe from 2008 to 2017. https://cds. climate.copernicus.eu/cdsapp#!/dataset/sis-urban-climate-cities?tab=overview , December

    2008

  29. [37]

    Urbclim–a fast urban boundary layer climate model

    Koen De Ridder, Dirk Lauwaet, and Bino Maiheu. Urbclim–a fast urban boundary layer climate model. Urban Climate, 12:21–48, 2015

    2015

  30. [38]

    Local climate zones (lcz) for urban temperature studies

    Iain D Stewart and Timothy R Oke. Local climate zones (lcz) for urban temperature studies. Bulletin of the American Meteorological Society, 93(12):1879–1900, 2012. Explicitly classifies air temperature UHI using in-situ measurements

    1900

  31. [39]

    Diverse seasonal hysteresis of surface urban heat islands across chinese cities: Patterns and drivers

    Chen Yang and Shuqing Zhao. Diverse seasonal hysteresis of surface urban heat islands across chinese cities: Patterns and drivers. Remote Sensing of Environment, 294:113644, 2023

    2023

  32. [40]

    Work title: A global typology of daytime and nighttime suhii hysteresis cycles

    Panagiotis Sismanidis, Benjamin Bechtel, Mike Perry, Iphigenia Keramitsoglou, and Darren Ghent. Work title: A global typology of daytime and nighttime suhii hysteresis cycles. Joint Urban Remote Sensing Event, 2022. 9

    2022

  33. [2019]

    (Accessed on 10/07/2020)

    2020

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.