Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

Li, Peiyuan; Yu, Yin; Huang, Daning; Wang, Zhihua; Sharma, Ashish

doi:10.1029/2023gl103405

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…Li et al. (2023) demonstrated the spatial interaction of observation stations in heatwave events. Other than spatial structure, GNNs can also be applied to represent the coupling of physical variables such as air‐sea interaction (e.g., Mu et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Wu et al (2021Wu et al ( , 2022 utilized GNN to construct the spatial relation between rain gauges and forecast the rainfall in the East of China. Li et al (2023) demonstrated the spatial interaction of observation stations in heatwave events. Other than spatial structure, GNNs can also be applied to represent the coupling of physical variables such as air-sea interaction (e.g., Mu et al, 2021).…”

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confidence: 97%

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Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network

Chen,

Wang,

Huang

et al. 2024

Geophysical Research Letters

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Precipitation exerts far‐reaching impacts on both socio‐economic fabric and individual well‐being, necessitating concerted efforts in accurate forecasting. Deep learning (DL) models have increasingly demonstrated their prowess in forecasting meteorological elements. However, traditional DL prediction models often grapple with heavy rainfall forecasting. In this study, we propose physics‐informed localized graph neural network models called ω‐GNN and ω‐EGNN, constrained by the coupling of physical variables and climatological background to predict precipitation in China. These models exhibit notable and robust improvements in identifying heavy rainfall while maintaining excellent performance in forecasting light rain by comparing to numerical weather prediction (NWP) and other DL models with multiple perturbation experiments in different data sets. Surprisingly, within a certain range, even when a DL model utilizes more input variables, GNN can still maintain its advantage. The methods to fuse physics into DL model demonstrated in this study may be promising and call for future studies.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 97%

Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network

Chen,

Wang,

Huang

et al. 2024

Geophysical Research Letters

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“…GNN is a recent variant of deep learning algorithms and has a specialty in the modeling of unstructured data defined on graphs or networks (Scarselli et al, 2009). Its applications to climate science have covered a wide range of topics, including the predictions of global weather (Keisler, 2022;Lam et al, 2023), regional heatwaves (P. Li, Y. Yu, et al, 2023), air quality (S. Wang et al, 2020;Ejurothu et al, 2023;Ma et al, 2023), frost (Lira et al, 2022), and precipitation (Y. Chen et al, 2024), which demonstrates a high potential to tackle the complex urban environment with extensive geospatial datasets.…”

Section: Introductionmentioning

confidence: 99%

Street-level temperature estimation using Graph Neural Networks: Performance, feature embedding and interpretability

Yu,

Li,

Huang

et al. 2024

Preprint

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**The manuscript has been submitted to Urban Climate for peer review. Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. In this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted an in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN’s high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

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Toward a Learnable Climate Model in the Artificial Intelligence Era

Huang,

Wang,

Ham

et al. 2024

Adv. Atmos. Sci.

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Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

Cited by 12 publications

References 49 publications

Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network

Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network

Street-level temperature estimation using Graph Neural Networks: Performance, feature embedding and interpretability

Toward a Learnable Climate Model in the Artificial Intelligence Era

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