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

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...

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Bibliographic Details
Published in:Infrastructures (Basel) 2020-02, Vol.5 (2), p.15
Main Authors: López Gómez, Javier, Troncoso Pastoriza, Francisco, Granada Álvarez, Enrique, Eguía Oller, Pablo
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Climate change
Fatalities
Geostatistics
global forecast system (gfs)
Infrastructure
interpolation
kriging
Mapping
Meteorology
Numerical weather forecasting
numerical weather prediction
Traffic accidents & safety
Variables
Weather
weather data
Weather forecasting
weather research and forecasting (wrf)
Weather stations
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Summary: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.
ISSN:2412-3811
2412-3811
DOI:10.3390/infrastructures5020015