Cavaiola Mattia, Tuju Peter Enos, Mazzino Andrea
CNR-National Research Council of Italy, Institute of Marine Sciences, Via S.Teresa S/N, 19032, Pozzuolo di Lerici, La Spezia, Italy.
Department of Civil, DICCA, Chemical and Environmental Engineering, Via Montallegro 1, 16145, Genova, Italy.
Sci Rep. 2024 Oct 30;14(1):26158. doi: 10.1038/s41598-024-77542-z.
Scientific inquiry has long relied on deterministic algorithms for systematic problem-solving and predictability. However, the rise of artificial intelligence (AI) has revolutionized data analysis, allowing us to uncover complex patterns in large datasets. In this study, we combine these two approaches by using AI to improve the reconstruction of past precipitation events, which is crucial for understanding climate change. Our objective is to leverage AI to map large-scale atmospheric proxies from the ERA5 climate reanalysis and multi-satellite historical precipitation data from the NASA-IMERG GPM constellation to observed precipitation, enhancing the accuracy and the resolution of climate reanalysis. Accurate climate reanalyses are essential, as they provide the most realistic representations of past atmospheric conditions, serving as benchmarks against which climate models are validated. Our AI-enhanced method offers a more accurate and computationally efficient solution compared to deterministic high-resolution precipitation downscaling methods. Additionally, it shows the capability to generalize predictions to new, previously unobserved locations, making it applicable across various regions. By integrating AI with traditional reanalysis techniques, we open up new opportunities for climate science and geosciences, with the potential to improve the accuracy and reliability of climate data, contributing to a better understanding of climate dynamics.
长期以来，科学探究一直依赖确定性算法来进行系统的问题解决和预测。然而，人工智能（AI）的兴起彻底改变了数据分析，使我们能够在大型数据集中发现复杂模式。在本研究中，我们通过使用人工智能来改进过去降水事件的重建，将这两种方法结合起来，这对于理解气候变化至关重要。我们的目标是利用人工智能将来自ERA5气候再分析的大规模大气代理数据和来自NASA-IMERG GPM星座的多卫星历史降水数据映射到观测到的降水上，提高气候再分析的准确性和分辨率。准确的气候再分析至关重要，因为它们提供了过去大气状况最真实的表述，作为验证气候模型的基准。与确定性高分辨率降水降尺度方法相比，我们的人工智能增强方法提供了一种更准确且计算效率更高的解决方案。此外，它还显示出能够将预测推广到新的、以前未观测到的地点的能力，使其适用于各个地区。通过将人工智能与传统再分析技术相结合，我们为气候科学和地球科学开辟了新的机会，有可能提高气候数据的准确性和可靠性，有助于更好地理解气候动态。
