Climate reanalyses play a crucial role in understanding past climate variability, assessing the performance of atmospheric weather models, and serving various applications. In this context, we conducted an extensive validation of high-resolution regional reanalyses and their effectiveness in reproducing precipitation fields over Italy, a climate change hotspot characterized by challenging conditions such as coastal sea-land interaction and complex orography. In this study, we considered nine reanalyses, with the ECMWF state-of-the-art ERA5 serving as the global reanalysis reference. Some of these products cover the entire Europe (BOLAM, COSMO-REA6, CERRA), while others are specifically designed for Italy (MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT), employing different atmospheric models and parametrizations. On one hand, the effective spatial resolution of each reanalysis has been assessed using wavelet techniques. This approach allowed us to cluster reanalyses into global, regional, and convection-permitting categories by analysing their daily precipitation fields, decomposing them into energy components at different spatial scales. On the other hand, the capability of higher-resolution reanalyses to more frequently capture extreme precipitation than ERA5 has been demonstrated by obtaining the frequency distributions of daily rainfall amounts. This investigation also includes the validation of the accuracy of reanalyses against various types of ground-based observations, both gridded and weather stations, not directly assimilated by reanalyses and therefore fully independent from them. This involves evaluating both climatological averages and day-to-day variability through the Stable Equitable Error in Probability Space (SEEPS). The main results indicate wet biases over the Alps, especially during spring and summer, and dry biases over Liguria, Tuscany, the Apennines, and Southern Italy during autumn and winter. Significant differences were found among different products, with MOLOCH and MERIDA-HRES showing the best performances, despite their higher resolution exposing them more than ERA5 to misplacement issues when compared to gauge station data. Finally, this study has compared reanalyses with observational datasets designed for trend analysis. Results reveal a significant increase in the annual precipitation bias in most reanalyses, with the only exception being VHR-REA_IT. This emphasizes the need for caution when calculating precipitation trends in the Italian region.
Multi-scale assessment of regional high-resolution reanalyses precipitation fields over Italy / F. Cavalleri, C. Lussana, M. Brunetti, F. Viterbo, R. Bonanno, V. Manara, M. Lacavalla, M. Maugeri. ((Intervento presentato al 5. convegno Congresso Nazionale dell’Associazione Italiana di Scienze dell’Atmosfera e Meteorologia (AISAM) tenutosi a Lecce nel 2024.
Multi-scale assessment of regional high-resolution reanalyses precipitation fields over Italy
F. Cavalleri
Primo
;V. Manara;M. MaugeriUltimo
2023
Abstract
Climate reanalyses play a crucial role in understanding past climate variability, assessing the performance of atmospheric weather models, and serving various applications. In this context, we conducted an extensive validation of high-resolution regional reanalyses and their effectiveness in reproducing precipitation fields over Italy, a climate change hotspot characterized by challenging conditions such as coastal sea-land interaction and complex orography. In this study, we considered nine reanalyses, with the ECMWF state-of-the-art ERA5 serving as the global reanalysis reference. Some of these products cover the entire Europe (BOLAM, COSMO-REA6, CERRA), while others are specifically designed for Italy (MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT), employing different atmospheric models and parametrizations. On one hand, the effective spatial resolution of each reanalysis has been assessed using wavelet techniques. This approach allowed us to cluster reanalyses into global, regional, and convection-permitting categories by analysing their daily precipitation fields, decomposing them into energy components at different spatial scales. On the other hand, the capability of higher-resolution reanalyses to more frequently capture extreme precipitation than ERA5 has been demonstrated by obtaining the frequency distributions of daily rainfall amounts. This investigation also includes the validation of the accuracy of reanalyses against various types of ground-based observations, both gridded and weather stations, not directly assimilated by reanalyses and therefore fully independent from them. This involves evaluating both climatological averages and day-to-day variability through the Stable Equitable Error in Probability Space (SEEPS). The main results indicate wet biases over the Alps, especially during spring and summer, and dry biases over Liguria, Tuscany, the Apennines, and Southern Italy during autumn and winter. Significant differences were found among different products, with MOLOCH and MERIDA-HRES showing the best performances, despite their higher resolution exposing them more than ERA5 to misplacement issues when compared to gauge station data. Finally, this study has compared reanalyses with observational datasets designed for trend analysis. Results reveal a significant increase in the annual precipitation bias in most reanalyses, with the only exception being VHR-REA_IT. This emphasizes the need for caution when calculating precipitation trends in the Italian region.File | Dimensione | Formato | |
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