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在上一期热点文献推荐中，我们为您推荐了通信领域的最新发展前沿，包括利用多分区异步空时编码数字超表面处理多频波和信号、基于参考频率多路复用实现带宽保证与低延迟通信、支持超表面的物理层智能无线攻击、面向6G无线通信的区块链与人工智能研究综述。本期我们为您选取了4篇文献，介绍AI在气候领域的最新发展前沿，包括Google GraphCast基于机器学习精准预测未来10天全球天气情况、华为盘古气象利用3D神经网络进行精确的中期全球天气预报、NowcastNet对极端降水的临近预报、通过深度学习揭示全球变暖对日降水量的影响。文献一 GraphCast精准预测全球中期天气情况 Learning skillful medium-range global weather forecasting Lam, Remi, etc. SCIENCE, 2023, 382(6672) Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but does not directly use historical weather data to improve the underlying model. Here, we introduce “GraphCast,” a machine learning-based method trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25° resolution globally, in under one minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting, and helps realize the promise of machine learning for modeling complex dynamical systems. 阅读原文：https://www.science.org/doi/10.1126/science.adi2336 文献二盘古气象利用3D神经网络进行精确的全球中期天气预报 Accurate medium-range global weather forecasting with 3D neural networks Bi, Kaifeng, etc. NATURE, 2023, 619(7970): 533–538 Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states. However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world's best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF). Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES. 阅读原文：https://www.nature.com/articles/s41586-023-06185-3 文献三 NowcastNet对极端降水的临近预报 Skilful nowcasting of extreme precipitation with NowcastNet Zhang, Yuchen, etc. NATURE, 2023, 619(7970): 526–532 Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details. Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation. We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable. 阅读原文：https://www.nature.com/articles/s41586-023-06184-4 文献四通过深度学习揭示全球变暖对日降水量的影响 Anthropogenic Fingerprints in Daily Precipitation Revealed by Deep Learning Ham, Yoo-Geun, etc. NATURE, 2023, 622(7982): 301–307 According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN) with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged. 阅读原文：https://www.nature.com/articles/s41586-023-06474-x “前沿文献专题推荐服务”是图书馆情报服务部学科服务的重要内容。欢迎有相关需求的科研人员与我们联系，我们将为您提供更有针对性的定题服务。联系人：杨老师闫老师邮箱：yandong80@bupt.edu.cn 电话：西土城校区图书馆125室 62281933 62283502 沙河校区图书馆东205室 66605330 66605331

热点文献带您关注AI在气候领域的最新进展——图书馆前沿文献专