The Sensitivity of Heatwave Climatology to Input Gridded Datasets: A Case Study of Ukraine
In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived from high-quality stat...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Rovira i virgili (URV) |
| Repositorio: | Repositori Institucional de la Universitat Rovira i Virgili |
| OAI Identifier: | oai:urv.cat:imarina:9452279 |
| Acceso en línea: | https://hdl.handle.net/20.500.11797/imarina9452279 |
| Access Level: | acceso abierto |
| Palabra clave: | Atmospheric Science,Environmental Science (Miscellaneous),Environmental Sciences,Meteorology & Atmospheric Sciences Gridded climate data Heatwave Heatwave metrics Sensitivity Ukrain Ukraine Uncertainty Verification Atmospheric science Ciências ambientais Educação Educação física Engenharias i Engenharias ii Engenharias iii Environmental science (miscellaneous) Environmental sciences Geociências Matemática / probabilidade e estatística Meteorology & atmospheric sciences |
| Sumario: | In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived from high-quality station time series. For the first task, we used a mini statistical ensemble of gridded datasets of the daily maximum air temperature (TX). The ensemble included the following: ClimUAd and E-OBS (Ukrainian and European observation-based gridded data, respectively), reanalyzes ERA5, ERA5-Land, NOAA-CIRES 20CR V2c and V3, and NCEP-NCAR R1. For the second task, the same gridded data were used along with 178 quality-controlled and homogenized TX station time series from Ukraine. HWs and their metrics were defined according to the approach summarized by Perkins and Alexander (2013). All calculations were performed for the period 1950-2014. Our results showed that, depending on the gridded dataset, the calculated values of HW metrics might differ significantly. Even after averaging over the study period and the territory of Ukraine, the ranges between the max and min values of HW metrics remain large. For instance, the spread in HW number per year may be up to six events. However, the differences in the trend slopes of HW metrics are less pronounced. In addition, the comparison of HW calculations derived using gridded and station data showed that E-OBS, ERA5, and ERA5-Land provide similar verification statistics. The evaluation statistics for 20CRV3 are worse compared to E-OBS, ERA5, and ERA5-Land, but significantly better than for 20CRV2c and NCEP-NCAR R1. Our findings can aid in selecting gridded datasets for calculating reliable HW climatology and, consequently, contribute to develo |
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