A new approach to study the long-term urban heat island evolution using time-dependent spectroscopy

Varentsova, Svetlana A.; Varentsov, Mikhail

doi:10.1016/j.uclim.2021.101026

Cited by 13 publications

(9 citation statements)

References 64 publications

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“…Considering the negative impact of WPSHI on summer CUHII, it can be inferred that WPSHI contributes to this asymmetrical growth. However, the seasonal asymmetry of CUHI development in Beijing is in contrast to the conclusion of a CUHII study in Moscow (Varentsova & Varentsov, 2021), where the summer weather conditions were found to have become more favorable for CUHII development, with the summer heat island developing faster than the winter CUHI. This suggests that different cities, due to their different local geographic and climatic environments, have different weather conditions that affect the CUHII, which may be due to differences in factors influencing the heat island, such as temperature, humidity, and wind speed, which vary among the same seasons (Priyadarsini et al, 2008;L.…”

Section: Discussioncontrasting

confidence: 91%

“…From a physical perspective, during CW events, the atmosphere is characterized by cold and dry conditions, stable atmospheric stratification, and clear skies, leading to minimal weather changes. This leads to an extended period of elevated CUHII levels (Konstantinov et al., 2018; Varentsova & Varentsov, 2021; J. Yang & Bou‐Zeid, 2018), causing the weather‐scale fluctuations of CUHII to become less pronounced, thereby weakening the weather‐scale spectral intensity of CUHII.…”

Section: Resultsmentioning

confidence: 99%

“…Current research on the causes of CUHI formation mainly focuses on the analysis of anthropogenic factors, including anthropogenic heat discharge (Kato & Yamaguchi, 2005; Shahmohamadi et al., 2011), urban underlying surface changes (Zhou & Chen, 2018; C. Zhao et al., 2018), building structure (Erdem Okumus & Terzi, 2021; Yuan et al., 2020), and atmospheric aerosols (Cao et al., 2016; Han et al., 2020; Y. Yang et al., 2020). However, changes in background weather and climate fields can also directly affect the CUHI (Y. Yang et al., 2022, 2023), resulting in temporal fluctuations of the CUHI phenomenon (Varentsova & Varentsov, 2021). The intensity of the CUHI phenomenon varies depending on the season, month, and weather conditions (Chow & Roth, 2006; Haashemi et al., 2016; Kidder & Essenwanger, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…However, changes in background weather and climate fields can also directly affect the CUHI (Y. Yang et al, 2022Yang et al, , 2023, resulting in temporal fluctuations of the CUHI phenomenon (Varentsova & Varentsov, 2021). The intensity of the CUHI phenomenon varies depending on the season, month, and weather conditions (Chow & Roth, 2006;Haashemi et al, 2016;Kidder & Essenwanger, 1995).…”

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confidence: 99%

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Elucidating the Multi‐Timescale Variability of a Canopy Urban Heat Island by Using the Short‐Time Fourier Transform

Zhang,

Luo,

Pan

et al. 2024

Geophysical Research Letters

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Taking the megacity of Beijing as an example, a short‐time Fourier transform (STFT) method was employed to extract the multi‐timescale evolution pattern of the canopy urban heat island intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the evolution of the CUHII in Beijing and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual‐scale spectrum of CUHII exhibited an increasing trend with obvious seasonal variation of the canopy urban heat island (CUHI). The intra‐daily‐scale spectrum of CUHII showed an increasing trend with the nighttime CUHI developing faster. Increasing Western Pacific Subtropical High intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII is controlled by weather system evolution, showing that the frequency of cold/heat waves (CWs/HWs) in Beijing was significantly negatively correlated with the weather‐scale spectral intensity of the CUHII. CWs and HWs can increase the CUHII for a long duration.

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Section: Discussioncontrasting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Elucidating the Multi‐Timescale Variability of a Canopy Urban Heat Island by Using the Short‐Time Fourier Transform

Zhang,

Luo,

Pan

et al. 2024

Geophysical Research Letters

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“…The urban-induced meteorological phenomena of Moscow are easy to detect against the homogeneous surroundings and quasi-symmetric urban planning features, which makes the city a convenient site for urban climate research. The city experiences an intense UHI with an increasing magnitude trend over the last decades [45][46][47], with a present-day annual-mean UHI magnitude of 2 K peaking at more than 10 K during calm and clear nights [3,45,46].…”

Section: Study Areamentioning

confidence: 99%

Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions

Varentsov,

Krinitskiy,

Stepanenko

2023

Climate

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This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors.

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Application of time-series analysis to urban climate change assessment

Liu,

Li,

Yang

et al. 2024

Int. J. Environ. Sci. Technol.

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A new approach to study the long-term urban heat island evolution using time-dependent spectroscopy

Cited by 13 publications

References 64 publications

Elucidating the Multi‐Timescale Variability of a Canopy Urban Heat Island by Using the Short‐Time Fourier Transform

Elucidating the Multi‐Timescale Variability of a Canopy Urban Heat Island by Using the Short‐Time Fourier Transform

Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions

Application of time-series analysis to urban climate change assessment

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