Optimizing the Interpolation of Temperatures by GIS: A Space Analysis Approach

Loubier, Jean-Christophe

doi:10.1002/9780470612262.ch8

Cited by 2 publications

(1 citation statement)

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“…The idea behind these methods is that temperature measured at particular points can provide information about the temperatures at points where direct measurements are not available. Methods used to interpolate among these measured points to create a smooth spatial surface of temperature, or to estimate temperatures at nearby locations, include kriging, inverse distance weighting methods, regression methods (Vicente-Serrano et al, 2003), an interpolation optimization method (Loubier, 2007), smoothing splines (Luo et al, 1998), and spatial-temporal modeling (Im et al, 2009). Secondary information related to temperature has also been incorporated into these techniques to improve estimation, including such variables as distance to water bodies (Im et al, 2009), topographic and geographic variables such as elevation, longitude and latitude (Vicente-Serrano et al, 2003), solar radiation (Ninyerola et al, 2000), quantitative climate/meteorological model predictions (Degaetano and Belcher, 2006) and satellite-derived information such as land surface temperature (Vogt et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region

Zhang

Oswald

Brown

et al. 2011

Environmental Research

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Background Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale. Objective This study examined whether satellite-derived impervious surface and meteorological conditions from multiple sites can improve characterization of spatial variability of temperature within an urban area. Methods Temperature was measured at 17 outdoor sites throughout the Detroit metropolitan area during the summer of 2008. Kriging and linear regression were applied to daily temperatures and secondary information, including impervious surface and distance-to-water. Performance of models in predicting measured temperatures was evaluated by cross-validation. Variograms derived from several scenarios were compared to determine whether high-resolution impervious surface information could capture fine-scale spatial structure of temperature in the study area. Results Temperatures measured at the sites were significantly different from each other, and all kriging techniques generally performed better than the two linear regression models. Impervious surface values and distance-to-water generally improved predictions slightly. Restricting models to days with lake breezes and with less cloud cover also somewhat improved the predictions. In addition, incorporating high-resolution impervious surface information into cokriging or universal kriging enhanced the ability to characterize fine-scale spatial structure of temperature. Conclusions Meteorological and satellite-derived data can better characterize spatial variability in temperature across a metropolitan region. The data sources and methods we used can be applied in epidemiological studies and public health interventions to protect vulnerable populations from extreme heat events.

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Section: Introductionmentioning

confidence: 99%

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region

Zhang

Oswald

Brown

et al. 2011

Environmental Research

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Custom Methodology to Improve Geospatial Interpolation at Regional Scale with Open-Source Software

Massarelli¹,

Campanale²,

Uricchio³

2022

Knowledge

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This study shows a methodological approach to improve geospatial interpolation carried out with the Inverse Distance Weighted algorithm using distances and other parameters to which we attribute relative weights such as elevation. We also provide reliable information about better data output by elaborating a more realistic confidence interval with various percentages of reliability. We tested the methodology to monthly accumulated rainfall and temperatures recorded by multiple monitoring stations in the Puglia region in South Italy. The whole procedure has been called Augmented Inverse Distance Weighted and is tested with the ultimate goal of predicting missing values at a regional scale based on cross-validation techniques applied to a dataset consisting of ten years of precipitation data and five years of temperature data. The efficacy of this approach is evaluated using statistical scores regularly employed in the model’s evaluation studies. Results show that the improvements over the classical approach are remarkable and that the “augmented” method provides more accurate measurements of environmental variables. The main application of this algorithm is the possibility to provide the spatialisation of values of precipitation and temperature, or any other based on its own needs, at every point of the territory, playing a very important role in agricultural decision support systems and letting us identify frosts, drought events, climatic trends, accidental events, cyclicality and seasonality.

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Optimizing the Interpolation of Temperatures by GIS: A Space Analysis Approach

Cited by 2 publications

References 1 publication

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region

Custom Methodology to Improve Geospatial Interpolation at Regional Scale with Open-Source Software

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