Building on the foundational activities of the initial phase mostly focused on the analysis of state-of-the-art literature and existing datasets along with interactions with climate services end-users, during the second 18 months of the project the CLINT partners have continued to harmonise their efforts, leveraging diverse professional backgrounds to maintain efficient coordination and integration. The development of AI and ML algorithms has progressed significantly, enabling the detection of relationships between extreme events and large-scale climatological fields. These advancements have furthered our ability to quantify causal interdependencies and isolate human fingerprints in relevant processes. As these analyses are consolidated, they will support the generation of sub-seasonal to seasonal forecasts for extreme events. Moreover, six CLINT Artificial Intelligence prototype(s) and two demonstrators of the enhanced Climate Services are under development.
As highlights, the following peer-reviewed articles on CLINT research advances have been already published in top-level journals:
Barriopedro, D., García‐Herrera, R., Ordóñez, C., Miralles, D. G., & Salcedo‐Sanz, S. (2023). Heat waves: Physical understanding and scientific challenges. Reviews of Geophysics, 61(2), e2022RG000780.
Du, Y., Clemenzi, I., & Pechlivanidis, I. G. (2023). Hydrological regimes explain the seasonal predictability of streamflow extremes. Environmental Research Letters, 18(9), 094060.
Torralba, V., Materia, S., Cavicchia, L., Álvarez-Castro, M. C., Prodhomme, C., McAdam, R., ... & Gualdi, S. (2024). Nighttime heat waves in the Euro-Mediterranean region: definition, characterisation, and seasonal prediction. Environmental Research Letters, 19(3), 034001.
Salcedo-Sanz, S., Pérez-Aracil, J., Ascenso, G., Del Ser, J., Casillas-Pérez, D., Kadow, C., ... & Castelletti, A. (2024). Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review. Theoretical and Applied Climatology, 155(1), 1-44.
Ascenso, G., Palcic, G., Scoccimarro, E., Giuliani, M., & Castelletti, A. (2024). A Systematic Framework for Data Augmentation for Tropical Cyclone Intensity Estimation Using Deep Learning (No. EGU24-8955). Copernicus Meetings.
Scoccimarro, E., Lanteri, P., & Cavicchia, L. (2024). Freddy: breaking record for tropical cyclone precipitation?. Environmental Research Letters, 19(6), 064013.
