Comparison between geostatistical interpolation and numerical weather model predictions for meteorological conditions mapping
| Autor: | Francisco Manuel Troncoso Pastoriza, Enrique Granada Álvarez, Pablo Eguía Oller, Javier López Gómez |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Meteorology Computer science 0211 other engineering and technologies 2509.19 Análisis del tiempo meteorológico numerical weather prediction 02 engineering and technology lcsh:Technology 01 natural sciences Weather station Kriging General Materials Science 021108 energy Global Forecast System (GFS) 1209.03 Análisis de Datos weather data 0105 earth and related environmental sciences Civil and Structural Engineering Ground truth lcsh:T Building and Construction Geotechnical Engineering and Engineering Geology Numerical weather prediction interpolation Computer Science Applications Variable (computer science) 13. Climate action Weather data 2509.09 Predicción Numérica Meteorológica Weather Research and Forecasting (WRF) Interpolation Downscaling |
| Zdroj: | Investigo. Repositorio Institucional de la Universidade de Vigo Universidade de Vigo (UVigo) Infrastructures Volume 5 Issue 2 Infrastructures, Vol 5, Iss 2, p 15 (2020) |
| Popis: | 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. Universidade de Vigo | Ref. 00VI 131H 641.02 Xunta de Galicia | Ref. IN852A 2018/37 |
| Databáze: | OpenAIRE |
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