Li et al. (2025) Weather-Regime-Based Heatwave Risk Typing and Urban Climate Resilience Assessment in New Delhi (1997–2016)

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Identification

• Journal: Atmosphere
• Year: 2025
• Date: 2025-10-13
• Authors: Yukai Li, Chenglong Zhong, Z. G. Deng, Zeyun Jiang
• DOI: 10.3390/atmos16101179

Research Groups

Not explicitly stated in the paper text.

Short Summary

This paper develops a physically interpretable and computationally efficient typology of heatwave risk in Delhi using aggregated station observations, identifying three distinct weather regimes with varying heatwave incidences to support early warning and management.

Objective

• To develop a physically interpretable and computationally efficient typology of heatwave risk in Delhi using aggregated station observations of daily meteorological variables.

Study Configuration

• Spatial Scale: Delhi, North Indian Plain.
• Temporal Scale: 1997 to 2016 (20 years) using daily observations.

Methodology and Data

• Models used: k-means clustering, Principal Component Analysis (PCA).
• Data sources: Aggregated station observations of daily mean temperature, relative humidity, wind speed, and pressure.

Main Results

• Three distinct weather regimes were identified using k-means clustering (k=3) based on quality-controlled, standardized daily meteorological features.
• A dry–hot, high-pressure regime (41% of days) accounted for 63% of heatwave days, with a mean temperature of 33.4 °C and a median duration of approximately 17 days.
• A mild–humid background regime (59% of days) yielded an approximate 8% heatwave incidence.
• A rare blocking-driven dry intrusion regime (<1% of days) produced heatwaves each time, with mean temperatures exceeding 35 °C and episodes persisting for 30 days or more.
• Regime–heatwave relationships were statistically significant and robust across various sensitivity tests.

Contributions

• Provides a physically interpretable and computationally efficient typology of heatwave risk for rapidly urbanizing regions like Delhi.
• Establishes a transparent four-stage workflow (data preparation, feature extraction, regime classification, heatwave risk attribution) that is transferable to other areas.
• Offers a basis for regime-aware early warning systems, demand-side energy management, and public health protection strategies.

Funding

Not explicitly stated in the paper text.

Citation

@article{Li2025WeatherRegimeBased,
  author = {Li, Yukai and Zhong, Chenglong and Deng, Z. G. and Jiang, Zeyun},
  title = {Weather-Regime-Based Heatwave Risk Typing and Urban Climate Resilience Assessment in New Delhi (1997–2016)},
  journal = {Atmosphere},
  year = {2025},
  doi = {10.3390/atmos16101179},
  url = {https://doi.org/10.3390/atmos16101179}
}