Inferring Urban Air Temperatures From Land Surface Temperatures With the E3SM Land Model (uELM), Satellite Observations, and Measurement Campaign
This study investigates how urban land-cover variability—from dense built areas to predominantly vegetated neighborhoods—affects the relationship between land surface temperature (LST) and near-surface air temperature (T2M) using an ultrahigh-resolution land model (uELM). 125 s...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10891798/ |
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| Summary: | This study investigates how urban land-cover variability—from dense built areas to predominantly vegetated neighborhoods—affects the relationship between land surface temperature (LST) and near-surface air temperature (T2M) using an ultrahigh-resolution land model (uELM). 125 simulations adjusting fractions of urban vs. vegetation, mid-rise vs. low-rise buildings and tree vs. lawn cover reveal strong LST-T2M coupling, as well as its dependency on land cover and diurnal variations. These physics-based findings support a machine-learning, inverse-modeling approach, training a XGBoost algorithm on simulated LST-T2M pairs to estimate T2M from satellite-based LST. Applied to the Chicago region with GOES-16 data and compared against vehicle-based measurements, the model and measurements agrees relatively well midday but shows evening mismatches tied to uneven cooling and hyperlocal factors during observation. Despite these discrepancies, blending mechanistic modeling with data-driven inversion has potential to refine urban T2M estimates, informing heat mitigation strategies and advancing urban climate research. |
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| ISSN: | 2169-3536 |