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Open AccessArticle

Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

GTE Research Group, School of Industrial Engineering, University of Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Pontevedra, Spain
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Infrastructures 2020, 5(2), 15; https://doi.org/10.3390/infrastructures5020015
Received: 18 December 2019 / Revised: 9 January 2020 / Accepted: 30 January 2020 / Published: 1 February 2020
(This article belongs to the Special Issue Resilience of Inland Transport Networks to Extreme Events)
Mapping of meteorological conditions surrounding road infrastructures is a critical tool to identify high-risk spots related to harsh weather. However, local or regional data are not always available, and researchers and authorities must rely on coarser observations or predictions. Thus, choosing a suitable method for downscaling global data to local levels becomes essential to obtain accurate information. This work presents a deep analysis of the performance of two of these methods, commonly used in meteorology science: Universal Kriging geostatistical interpolation and Weather Research and Forecasting numerical weather prediction outputs. Estimations from both techniques are compared on 11 locations in central continental Portugal during January 2019, using measured data from a weather station network as the ground truth. Results show the different performance characteristics of both algorithms based on the nature of the specific variable interpolated, highlighting potential correlations to obtain the most accurate data for each case. Hence, this work provides a solid foundation for the selection of the most appropriate tool for mapping of weather conditions at the local level over linear transport infrastructures. View Full-Text
Keywords: weather data; numerical weather prediction; Global Forecast System (GFS); Kriging; interpolation; Weather Research and Forecasting (WRF) weather data; numerical weather prediction; Global Forecast System (GFS); Kriging; interpolation; Weather Research and Forecasting (WRF)
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López Gómez, J.; Troncoso Pastoriza, F.; Granada Álvarez, E.; Eguía Oller, P. Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping. Infrastructures 2020, 5, 15.

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