Space–time modeling of 20 years of daily air temperature in the Chicago metropolitan region

Abstract

SUMMARYWe analyze 20 years of daily minimum and maximum air temperature data in the Chicago metropolitan region and propose a parsimonious model that describes their mean function and the space-time covariance structure. The mean function contains a long-term trend, annual and semiannual harmonics, and physical covariates such as latitude, distance to the Lake Michigan, and winds, each interacted with the harmonic terms, thus allowing the effects of physical covariates to vary smoothly over time. The temporal correlation at a given location is described using an ARMA(1,2) model. The residuals (innovations) from this models are treated as independent replications of a spatial process with covariance structure in the Matérn class. The space-time covariance structure parameters are allowed to vary seasonally. Using the estimated covariance structure, we interpolate the temperature to a fine grid in the Chicago metropolitan region. This procedure borrows information from temporally and spatially adjacent data. The methods presented in this paper should be useful to approach other environmental problems where the data are discrete and regular in time but irregular in space.

Determining the influence of Itaipu Lake on thermal conditions for soybean development in adjacent lands

“…The derived intercept and slope of the times series models for all seasons studied showed the same trends for each station, that is, no significant difference in the fitted parameters, indicating similarity in the temperature time series between stations within the 10-km transect. This was contrary to the expected trend of decreasing minimum (increasing maximum) air temperature with distance from lakeshore typically observed for larger water bodies (Im et al 2009 ). Likely large-scale synoptic factors had an overriding effect producing similar air temperature conditions at all stations.…”

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

“…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).…”

Implementing spatiotemporal kriging interpolation based on 3D model

“…The initial value of nugget is 3D_nugget which takes the minimum value of varigram function. The actual sill, range and nugget are returned to the array m, sill is stored in m [2,2], range is stored in m [2,3], nugget is stored in m [1,2]. The spatiotemporal variogram function curve is showed as Fig.…”

“…In fact, a large number of environmental phenomena may be regarded as represent of spatiotemporal random fields. This has led to the development and application of spatiotemporal geostatistical models in many domains such as the rainfall analysis [1], temperature prediction [2], health analysis [3], environment pollution [4] and soil analysis [5]. Because the sample points are sparse usually, a continuous spatiotemporal data continuum must be constructed by interpolation when further research need be done.…”