Estimation of Hourly Air Temperature in China Based on LightGBM and Himawari-8

Zhou, Siqin; Wang, Yuan; Yuan, Qiangqiang

doi:10.1109/igarss46834.2022.9884582

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Our proposed novel two-stage method filled in these weaknesses and allowed for further exploration of estimating continuous, hourly air temperature while maintaining high accuracy. Zhou, S. et al explored the use of explanatory variables to build an interpretable hourly air temperature estimation model based on light gradient boosting machine (LightGBM) [51]. However, since this model relies on data from geostationary meteorological satellites, it cannot be applied globally.…”

Section: The Performance Of Regression Modelsmentioning

confidence: 99%

Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature

et al. 2023

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High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 °C, R-squared of 0.949, MAE of 1.115 °C, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.

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Section: The Performance Of Regression Modelsmentioning

confidence: 99%

Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature

et al. 2023

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HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003–2020

Zhang,

Luo,

Zhan

et al. 2024

Sci Data

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Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

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Estimation of Hourly Air Temperature in China Based on LightGBM and Himawari-8

Cited by 2 publications

References 6 publications

Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature

Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature

HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003–2020

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