Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy

Vanella, Daniela; Longo-Minnolo, Giuseppe; Belfiore, Oscar Rosario; Ramírez-Cuesta, Juan Miguel; Pappalardo, Salvatore; Consoli, Simona; D’Urso, Guido; Chirico, Giovanni Battista; Coppola, Antonio; Comegna, Alessandro; Toscano, Attilio; Quarta, R.; Provenzano, Giuseppe; Ippolito, Matteo; Castagna, Alessandro; Gandolfi, Claudio

doi:10.1016/j.ejrh.2022.101182

Cited by 36 publications

(19 citation statements)

References 32 publications

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“…More specifically, in mountainous areas or valleys, ERA5 tends to have large biases, whereas, in plain areas, the biases are usually small. This is consistent with other studies [8,20,42]. To examine the influence of topography on the quality of ERA5, we first employ the method proposed by Winstral et al [20] who utilized TPI to assess the performance of COSMO-REA6.…”

Section: Data Preprocessingsupporting

confidence: 78%

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Scholz

Yeligeti

et al. 2023

Environ. Res. Lett.

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Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km x 31 km to 1 km x 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our model, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.

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Section: Data Preprocessingsupporting

confidence: 78%

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Scholz

Yeligeti

et al. 2023

Environ. Res. Lett.

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“…As regards precipitation, it is more difficult to represent its erratic spatial distribution; a study over Central Italy (period: 1951-2019), has shown that ERA5 generally overestimates the annual rainfall, except on the north-central Apennines where it is underestimated [10] . Anyway, with reference to several Italian irrigation districts, a general good agreement was obtained between observed and reanalysis (ERA5 and ERA5 Land) derived agrometeorological variables at both daily and seasonal scales [9] . For these reasons, the dataset presented here is derived from ERA5.…”

Section: Experimental Design Materials and Methodssupporting

confidence: 54%

“…Although ERA5 Land shows a better spatial resolution, an important limit of this dataset (especially for the Italian peninsula, with approximately 8,000 km of coastline) is that data are not provided for the grid points falling on the sea surface or in the proximity of the coastline [8] . Another point is related to data accuracy: a study carried out in Italy has shown similar or slightly improved performances of ERA5 in comparison to ERA5 Land [9] . Therefore, the disadvantage of managing a larger dataset is not always balanced by an improvement in terms of accuracy.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

MADIA - Meteorological variables for agriculture: A dataset for the Italian area

Parisse¹,

Alilla²,

Gerardo³

et al. 2023

Data in Brief

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“…Muñoz‐Sabater et al (2021) describe the added value of ERA5‐Land horizontal resolution and its ability to describe the energy cycle. Vanella et al (2022) concluded that this reanalysis performed well with regard to the modelling of air temperature in seven irrigation districts located in Italy, on both a daily and seasonal scale. Araújo et al (2022) demonstrated that ERA5‐Land reanalysis successfully described the average air temperature of a region located in the northeast of Brazil.…”

Section: Introductionmentioning

confidence: 73%

A first assessment of ERA5 and ERA5‐Land reanalysis air temperature in Portugal

Almeida,

Coelho

2023

Intl Journal of Climatology

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This study evaluates the reliability of ERA5 and ERA5‐Land reanalysis datasets in describing the mean daily air temperature of four climate domains in mainland Portugal. The reanalysis datasets were compared with ground observations from 94 meteorological stations (1980–2021). Overall, the results demonstrated a good degree of correlation between the observed and reanalysis data on both a daily and seasonal scale. Both the latitudinal distribution of the air temperature and the moderating effect of the Atlantic Ocean are well described. However, in the case of Portugal, the ERA5‐Land was shown to be considerably more effective at describing the mean daily air temperature than ERA5. The results also indicated that, in general, the reanalysis methodologies perform better when applied to air temperature simulation in flatter regions as opposed to regions with high‐altitude and complex terrain. The study further suggests that ERA5 and ERA5‐Land reanalysis should be used with caution in the case of short‐term environmental studies. In fact, relevant differences were shown to exist between the reanalyses and the observed daily mean air temperature datasets for certain specific years. Overall, considering the RMSE between the ERA5‐Land reanalysis datasets for mean daily air temperature and the observed datasets there is a 28% probability of locally having a mean RMSE <1.5°C, 52% probability of having a mean RMSE >1.5°C and <2.0°C, and 16% probability of having a RMSE >2.0°C and <3.0°C. These conclusions will hopefully contribute to improving our understanding of the uncertainty sources in relation to ERA5 and ERA5‐Land reanalysis data for different climate domains.

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Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy

Cited by 36 publications

References 32 publications

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

MADIA - Meteorological variables for agriculture: A dataset for the Italian area

A first assessment of ERA5 and ERA5‐Land reanalysis air temperature in Portugal

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