May 20, 2025 · Despite these innovations, significant challenges persist, including data quality inconsistencies, model interpretability limitations, ethical concerns, and the scalability of AI models …

We discuss how ML has been used to tackle long-standing problems in the reconstruction of observational data, representation of sub-grid-scale phenomena and climate (and weather)...

Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields.

Apr 27, 2024 · In this study, 62 years of historical monitoring data from 105 weather stations in Xinjiang were used for missing sequence prediction, validating proposed data reconstruction tool.

Despite these advancements, challenges persist, such as the need for standardized data formats, model interpretability, and ethical considerations. Additionally, the integration of AI and ML findings into …

Dec 18, 2024 · By addressing the domains connected to ML and climate research, the findings underline the versatility of ML in tackling challenges ranging from hydrology and biodiversity to agriculture, …

redict climate variability at large temporal and spatial scales. In those cases, the information provided by those limited and incomplete data sets needs to be interpreted, interpolated and extrapolated by the …

Understanding past climate conditions is essential for addressing future climate challenges. However, observational climate datasets often contain missing values, especially in older records, leading to …

Nov 11, 2024 · The ability to accurately reconstruct climate data from the past century isn’t just a scientific breakthrough—it also holds significant implications for policy.

One of the primary issues is the inefficiency of conventional data processing systems in managing, analyzing, and extracting meaningful insights from large-scale climate data. The integration of …