The poly‐log Weibull model applied to space‐time interpolation of temperature

Mota, Ali; Lima, Máx Sousa de; Demarqui, Fábio N.; Duczmal, Luiz Henrique

doi:10.1002/env.2520

Cited by 1 publication

(1 citation statement)

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“…And it can capture spatial correlation: data points at spatially neighboring locations may have correlated features, which is particularly important for spatiotemporal interpolation problems. Using PLW, spatiotemporal interpolation of 20-year monthly mean temperatures from 14 meteorological stations in the Brazilian Amazon generates interpolation results that are not significantly different from the IDW interpolation results, but the PLW model can be used as a spatially shifted model for interpolation of meteorological factor minima, and it can also be used efficiently for spatiotemporal forecasting [55].…”

Section: Poly-log Weibullmentioning

confidence: 99%

Research Progress on Spatiotemporal Interpolation Methods for Meteorological Elements

Wang,

Liu,

Liu

et al. 2024

Water

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With the development of mathematical statistics, people have developed the spatiotemporal interpolation methods based on the spatial interpolation method or the temporal interpolation method. These methods fully consider the comprehensive effects of time series changes and spatial distribution to better handle complicated and changeable meteorological element data. This article systematically reviews the current research progress of spatiotemporal interpolation methods for spatiotemporal sampling data of meteorological origin. Spatiotemporal interpolation methods of meteorological elements are classified into three categories: spatiotemporal geostatistical interpolation methods, spatiotemporal deterministic interpolation methods, and spatiotemporal mixed interpolation methods. This article summarizes the theoretical concept and practical application of the spatiotemporal interpolation methods of meteorological elements, analyzes the advantages and disadvantages of using spatiotemporal interpolation methods for estimating or forecasting meteorological elements, combined through some measures and their application to explain the accuracy of the spatiotemporal interpolation methods; and discusses the problems and challenges of spatiotemporal interpolation. Finally, the future research focus of spatiotemporal interpolation methods is proposed. This article provides a valuable method reference for estimating or predicting meteorological elements such as precipitation in unsampled points.

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Section: Poly-log Weibullmentioning

confidence: 99%