Spatial Interpolation of Ewert’s Index of Continentality in Poland

Abstract

Abstract-The article presents methodological considerations on the spatial interpolation of Ewert's index of continentality for Poland. The primary objective was to perform spatial interpolation and generate maps of the index combined with selection of an optimal interpolation method and validation of the use of the decision tree proposed by Szymanowski et al. (Meteorol Z 22:577-585, 2013). The analysis involved four selected years and a multi-year average of the period 1981-2010 and was based on data from 111 meteorological stations. Three regression models: multiple linear regression (MLR), geographically weighted regression (GWR), and mixed geographically weighted regression were used in the analysis as well as extensions of two of them to the residual kriging form. The regression models were compared demonstrating a better fit of the local model and, hence, the non-stationarity of the spatial process. However, the decisive role in the selection of the interpolator was assigned to the possibility of extension of the regression model to residual kriging. A key element here is the autocorrelation of the regression residuals, which proved to be significant for MLR and irrelevant for GWR. This resulted in exclusion of geographically weighted regression kriging from further analysis. The multiple linear regression kriging was found as the optimal interpolator. This was confirmed by cross validation combined with an analysis of improvement of the model in accordance with the criterion of the mean absolute error (MAE). The results obtained facilitate modification of the scheme of selection of an optimal interpolator and development of guidelines for automation of interpolation of Ewert's index of continentality for Poland.

The genetic and environmental variance of radial increment in Scots pine of south-eastern Baltic provenances in response to weather extremes

“…The standardized precipitation evapotranspiration index (SPEI; Vicente-Serrano et al 2010) was calculated to characterize the drought conditions with the respect to three-month period. Ewert's thermal continentality index (Szymanowski et al 2017) was calculated for the description of the climate of the trials. For the period 1987-2017, z-scores were calculated for the weather variables to evaluate their deviance from the "norm", hence the anomaly.…”

Detecting a Midlatitude Island Climate Signature in the Great Lakes Coastal Region of Ontario, Canada

“…Coasts 2024, 4, FOR PEER REVIEW 3 to the diurnal temperature range (DTR), a metric inspired by continentality measures [3][4][5][6]. This is the difference between the maximum temperature of the day and the minimum temperature of the day, a scaled version of continentality that is measured by the difference of the maximum monthly temperature and minimum monthly temperature developed by [3].…”

“…A frequently used metric is the difference between the mean monthly temperature of the coldest month and the warmest month of the year [4]. Accounting for latitude-dependent seasonality, other forms of this metric have been employed, such as normalizing the temperature difference by dividing by the sine of the latitude to account for seasonal variations in insolation [3][4][5]. Ref.…”

“…The key metric was the day-to-day variability of the minimum temperature of the day (DTDTmin) which was modified in [2] to account for the radiatively distinct winter season. In addition, we will compare this method to the diurnal temperature range (DTR), a metric inspired by continentality measures [3][4][5][6]. This is the difference between the maximum temperature of the day and the minimum temperature of the day, a scaled version of continentality that is measured by the difference of the maximum monthly temperature and minimum monthly temperature developed by [3].…”