We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.
translated by 谷歌翻译
In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by downloading $43$ years of hourly global weather data from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep neural networks with about $256$ million parameters in total. The spatial resolution of forecast is $0.25^\circ\times0.25^\circ$, comparable to the ECMWF Integrated Forecast Systems (IFS). More importantly, for the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors (e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in all time ranges (from one hour to one week). There are two key strategies to improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer (3DEST) architecture that formulates the height (pressure level) information into cubic data, and (ii) applying a hierarchical temporal aggregation algorithm to alleviate cumulative forecast errors. In deterministic forecast, Pangu-Weather shows great advantages for short to medium-range forecast (i.e., forecast time ranges from one hour to one week). Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast (e.g., tropical cyclone tracking) and large-member ensemble forecast in real-time. Pangu-Weather not only ends the debate on whether AI-based methods can surpass conventional NWP methods, but also reveals novel directions for improving deep learning weather forecast systems.
translated by 谷歌翻译
后处理整体预测系统可以改善天气预报,尤其是对于极端事件预测。近年来,已经开发出不同的机器学习模型来提高后处理步骤的质量。但是,这些模型在很大程度上依赖数据并生成此类合奏成员需要以高计算成本的数值天气预测模型进行多次运行。本文介绍了ENS-10数据集,由十个合奏成员组成,分布在20年中(1998-2017)。合奏成员是通过扰动数值天气模拟来捕获地球的混乱行为而产生的。为了代表大气的三维状态,ENS-10在11个不同的压力水平以及0.5度分辨率的表面中提供了最相关的大气变量。该数据集以48小时的交货时间针对预测校正任务,这实质上是通过消除合奏成员的偏见来改善预测质量。为此,ENS-10为预测交货时间t = 0、24和48小时(每周两个数据点)提供了天气变量。我们在ENS-10上为此任务提供了一组基线,并比较了它们在纠正不同天气变量预测时的性能。我们还评估了使用数据集预测极端事件的基准。 ENS-10数据集可在创意共享归因4.0国际(CC By 4.0)许可下获得。
translated by 谷歌翻译
我们基准了一个简单学习模型的亚季节预测工具包,该工具包优于操作实践和最先进的机器学习和深度学习方法。这些模型,由Mouatadid等人引入。 (2022),包括(a)气候++,这是气候学的一种适应性替代品,对于降水而言,准确性9%,比美国运营气候预测系统(CFSV2)高9%,熟练250%; (b)CFSV2 ++,一种学习的CFSV2校正,可将温度和降水精度提高7-8%,技能提高50-275%; (c)持久性++是一种增强的持久性模型,将CFSV2预测与滞后测量相结合,以将温度和降水精度提高6-9%,技能提高40-130%。在整个美国,气候++,CFSV2 ++和持久性++工具包始终优于标准气象基准,最先进的机器和深度学习方法,以及欧洲中等范围的天气预报集合中心。
translated by 谷歌翻译
了解极端事件及其可能性是研究气候变化影响,风险评估,适应和保护生物的关键。在这项工作中,我们开发了一种方法来构建极端热浪的预测模型。这些模型基于卷积神经网络,对极长的8,000年气候模型输出进行了培训。由于极端事件之间的关系本质上是概率的,因此我们强调概率预测和验证。我们证明,深度神经网络适用于法国持续持续14天的热浪,快速动态驱动器提前15天(500 hpa地球电位高度场),并且在慢速较长的交货时间内,慢速物理时间驱动器(土壤水分)。该方法很容易实现和通用。我们发现,深神经网络选择了与北半球波数字3模式相关的极端热浪。我们发现,当将2米温度场添加到500 HPA地球电位高度和土壤水分场中时,2米温度场不包含任何新的有用统计信息。主要的科学信息是,训练深层神经网络预测极端热浪的发生是在严重缺乏数据的情况下发生的。我们建议大多数其他应用在大规模的大气和气候现象中都是如此。我们讨论了处理缺乏数据制度的观点,例如罕见的事件模拟,以及转移学习如何在后一种任务中发挥作用。
translated by 谷歌翻译
由于其对人类生命,运输,粮食生产和能源管理的高度影响,因此在科学上研究了预测天气的问题。目前的运营预测模型基于物理学,并使用超级计算机来模拟大气预测,提前预测数小时和日期。更好的基于物理的预测需要改进模型本身,这可能是一个实质性的科学挑战,以及潜在的分辨率的改进,可以计算令人望而却步。基于神经网络的新出现的天气模型代表天气预报的范式转变:模型学习来自数据的所需变换,而不是依赖于手工编码的物理,并计算效率。然而,对于神经模型,每个额外的辐射时间都会构成大量挑战,因为它需要捕获更大的空间环境并增加预测的不确定性。在这项工作中,我们提出了一个神经网络,能够提前十二小时的大规模降水预测,并且从相同的大气状态开始,该模型能够比最先进的基于物理的模型更高的技能HRRR和HREF目前在美国大陆运营。可解释性分析加强了模型学会模拟先进物理原则的观察。这些结果代表了建立与神经网络有效预测的新范式的实质性步骤。
translated by 谷歌翻译
Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another. Our framework-which we term "Graph Network-based Simulators" (GNS)-represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time. Our model was robust to hyperparameter choices across various evaluation metrics: the main determinants of long-term performance were the number of message-passing steps, and mitigating the accumulation of error by corrupting the training data with noise. Our GNS framework advances the state-of-the-art in learned physical simulation, and holds promise for solving a wide range of complex forward and inverse problems.
translated by 谷歌翻译
尽管有持续的改进,但降水预测仍然没有其他气象变量的准确和可靠。造成这种情况的一个主要因素是,几个影响降水分布和强度的关键过程出现在全球天气模型的解决规模以下。计算机视觉社区已经证明了生成的对抗网络(GAN)在超分辨率问题上取得了成功,即学习为粗图像添加精细的结构。 Leinonen等。 (2020年)先前使用GAN来产生重建的高分辨率大气场的集合,并给定较粗糙的输入数据。在本文中,我们证明了这种方法可以扩展到更具挑战性的问题,即通过使用高分辨率雷达测量值作为“地面真相”来提高天气预报模型中相对低分辨率输入的准确性和分辨率。神经网络必须学会添加分辨率和结构,同时考虑不可忽略的预测错误。我们表明,甘斯和vae-gan可以在创建高分辨率的空间相干降水图的同时,可以匹配最新的后处理方法的统计特性。我们的模型比较比较与像素和合并的CRP分数,功率谱信息和等级直方图(用于评估校准)的最佳现有缩减方法。我们测试了我们的模型,并表明它们在各种场景中的表现,包括大雨。
translated by 谷歌翻译
地球天气和气候的数值模拟需要大量的计算。这导致替换替换具有在推理时间快速的近似机器学习(ml)方法的子程序来替换的子程序感兴趣。在天气和气候模型中,大气辐射转移(RT)计算特别昂贵。这使他们成为了基于神经网络的仿真器的流行目标。然而,由于缺乏缺乏全面的数据集和ML基准测试的标准化最佳实践,事先工作难以比较。为了填补这个差距,我们建立一个大型数据集,比加拿大地球系统模型为基础的大型数据集,高于\ emph {1000万个样本,未来的气候条件}。 Climart为ML社区带来了几种方法论挑战,例如多次分发试验集,底层域物理学和准确性和推广速度之间的权衡。我们还提出了几种新颖的基线,这些基线表示现有工作中使用的数据集和网络架构的缺点。下载说明,基准和代码可提供:https://github.com/rolnicklab/climart
translated by 谷歌翻译
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical dynamics, such as fluids, cloth and articulated bodies, they have been less effective and efficient on rigid-body physics, except with very simple shapes. Existing methods that model collisions through the meshes' nodes are often inaccurate because they struggle when collisions occur on faces far from nodes. Alternative approaches that represent the geometry densely with many particles are prohibitively expensive for complex shapes. Here we introduce the Face Interaction Graph Network (FIGNet) which extends beyond GNN-based methods, and computes interactions between mesh faces, rather than nodes. Compared to learned node- and particle-based methods, FIGNet is around 4x more accurate in simulating complex shape interactions, while also 8x more computationally efficient on sparse, rigid meshes. Moreover, FIGNet can learn frictional dynamics directly from real-world data, and can be more accurate than analytical solvers given modest amounts of training data. FIGNet represents a key step forward in one of the few remaining physical domains which have seen little competition from learned simulators, and offers allied fields such as robotics, graphics and mechanical design a new tool for simulation and model-based planning.
translated by 谷歌翻译
具有经典数字求解器的湍流模拟需要非常高分辨率的网格来准确地解决动态。在这里,我们以低空间和时间分辨率培训学习模拟器,以捕获高分辨率产生的湍流动态。我们表明我们所提出的模型可以比各种科学相关指标的相同低分辨率的经典数字求解器更准确地模拟湍流动态。我们的模型从数据训练结束到底,能够以低分辨率学习一系列挑战性的混乱和动态动态,包括最先进的雅典娜++发动机产生的轨迹。我们表明,我们的更简单,通用体系结构优于来自所学到的湍流模拟文献的各种专业的湍流特异性架构。一般来说,我们看到学习的模拟器产生不稳定的轨迹;但是,我们表明调整训练噪音和时间下采样解决了这个问题。我们还发现,虽然超出培训分配的泛化是学习模型,训练噪声,卷积架构以及增加损失约束的挑战。广泛地,我们得出的结论是,我们所知的模拟器优于传统的求解器在较粗糙的网格上运行,并强调简单的设计选择可以提供稳定性和鲁棒的泛化。
translated by 谷歌翻译
太阳能现在是历史上最便宜的电力形式。不幸的是,由于其变异性,显着提高栅格的太阳能的一部分仍然具有挑战性,这使得电力的供需平衡更加困难。虽然热发电机坡度 - 它们可以改变输出的最高速率 - 是有限的,太阳能的坡度基本上是无限的。因此,准确的近期太阳能预测或垂圈,对于提供预警来调整热发电机输出,以响应于太阳能变化来调整热发电机,以确保平衡供需。为了解决问题,本文开发了使用自我监督学习的丰富和易于使用的多光谱卫星数据的太阳能垂圈的一般模型。具体而言,我们使用卷积神经网络(CNN)和长短期内存网络(LSTM)开发深度自动回归模型,这些模型在多个位置训练全球培训,以预测最近推出的最近收集的时空数据的未来观察-R系列卫星。我们的模型估计了基于卫星观测的未来的太阳辐照度,我们向较小的场地特定的太阳能数据培训的回归模型提供,以提供近期太阳能光伏(PV)预测,其考虑了现场特征的特征。我们评估了我们在25个太阳能场所的不同覆盖区域和预测视野的方法,并表明我们的方法利用地面真理观察结果产生靠近模型的错误。
translated by 谷歌翻译
生产精确的天气预报和不确定的不确定性的可靠量化是一个开放的科学挑战。到目前为止,集团预测是最成功的方法,以产生相关预测的方法以及估计其不确定性。集合预测的主要局限性是高计算成本,难以捕获和量化不同的不确定性来源,特别是与模型误差相关的源。在这项工作中,进行概念证据模型实验,以检查培训的ANN的性能,以预测系统的校正状态和使用单个确定性预测作为输入的状态不确定性。我们比较不同的培训策略:一个基于使用集合预测的平均值和传播作为目标的直接培训,另一个依赖于使用确定性预测作为目标的决定性预测,其中来自数据隐含地学习不确定性。对于最后一种方法,提出和评估了两个替代损失函数,基于数据观察似然和基于误差的本地估计来评估另一个丢失功能。在不同的交货时间和方案中检查网络的性能,在没有模型错误的情况下。使用Lorenz'96模型的实验表明,ANNS能够模拟集合预测的一些属性,如最不可预测模式的过滤和预测不确定性的状态相关量化。此外,ANNS提供了在模型误差存在下的预测不确定性的可靠估计。
translated by 谷歌翻译
Producing high-quality forecasts of key climate variables such as temperature and precipitation on subseasonal time scales has long been a gap in operational forecasting. Recent studies have shown promising results using machine learning (ML) models to advance subseasonal forecasting (SSF), but several open questions remain. First, several past approaches use the average of an ensemble of physics-based forecasts as an input feature of these models. However, ensemble forecasts contain information that can aid prediction beyond only the ensemble mean. Second, past methods have focused on average performance, whereas forecasts of extreme events are far more important for planning and mitigation purposes. Third, climate forecasts correspond to a spatially-varying collection of forecasts, and different methods account for spatial variability in the response differently. Trade-offs between different approaches may be mitigated with model stacking. This paper describes the application of a variety of ML methods used to predict monthly average precipitation and two meter temperature using physics-based predictions (ensemble forecasts) and observational data such as relative humidity, pressure at sea level, or geopotential height, two weeks in advance for the whole continental United States. Regression, quantile regression, and tercile classification tasks using linear models, random forests, convolutional neural networks, and stacked models are considered. The proposed models outperform common baselines such as historical averages (or quantiles) and ensemble averages (or quantiles). This paper further includes an investigation of feature importance, trade-offs between using the full ensemble or only the ensemble average, and different modes of accounting for spatial variability.
translated by 谷歌翻译
我们基于技能评分,对确定性太阳预测进行了首次全面的荟萃分析,筛选了Google Scholar的1,447篇论文,并审查了320篇论文的全文以进行数据提取。用多元自适应回归样条模型,部分依赖图和线性回归构建和分析了4,758点的数据库。值得注意的是,分析说明了数据中最重要的非线性关系和交互项。我们量化了对重要变量的预测准确性的影响,例如预测范围,分辨率,气候条件,区域的年度太阳辐照度水平,电力系统大小和容量,预测模型,火车和测试集以及使用不同的技术和投入。通过控制预测之间的关键差异,包括位置变量,可以在全球应用分析的发现。还提供了该领域科学进步的概述。
translated by 谷歌翻译
在学识表的迅速推进的地区,几乎所有方法都训练了从输入状态直接预测未来状态的前进模型。然而,许多传统的仿真引擎使用基于约束的方法而不是直接预测。这里我们提出了一种基于约束的学习仿真的框架,其中标量约束函数被实现为神经网络,并且将来的预测被计算为在这些学习的约束下的优化问题的解决方案。我们使用图形神经网络作为约束函数和梯度下降作为约束求解器来实现我们的方法。架构可以通过标准的backprojagation培训。我们在各种具有挑战性的物理领域中测试模型,包括模拟绳索,弹跳球,碰撞不规则形状和飞溅液。我们的模型可实现更好或更具可比性的性能,以获得最佳学习的模拟器。我们模型的一个关键优势是能够在测试时间概括到更多求解器迭代,以提高模拟精度。我们还展示了如何在测试时间内添加手工制定的约束,以满足培训数据中不存在的目标,这是不可能的前进方法。我们的约束框架适用于使用前进学习模拟器的任何设置,并演示了学习的模拟器如何利用额外的归纳偏差以及来自数值方法领域的技术。
translated by 谷歌翻译
A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.
translated by 谷歌翻译
Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes equations. However, despite the fast-growing field of data-driven models for physical systems, reference datasets representing real-world phenomena are lacking. In this work, we develop AirfRANS, a dataset for studying the two-dimensional incompressible steady-state Reynolds-Averaged Navier-Stokes equations over airfoils at a subsonic regime and for different angles of attacks. We also introduce metrics on the stress forces at the surface of geometries and visualization of boundary layers to assess the capabilities of models to accurately predict the meaningful information of the problem. Finally, we propose deep learning baselines on four machine learning tasks to study AirfRANS under different constraints for generalization considerations: big and scarce data regime, Reynolds number, and angle of attack extrapolation.
translated by 谷歌翻译
有希望的方法来改善气候模型中的云参数化,因此气候预测是使用深度学习与来自Storm-解析模型(SRM)模拟的培训数据结合使用。 ICOSAHEDRAL非静水压(图标)建模框架允许模拟从数值天气预报到气候投影,使其成为开发基于神经网络(NN)的子网比例过程的参数化的理想目标。在图标框架内,我们通过基于逼真的区域和全局图标SRM模拟培训基于NN的云覆盖参数化。我们设置了三种不同类型的NNS,其垂直局部程度不同,它们假设从粗粒粒度大气状态变量诊断云盖。 NNS精确地从粗粒数据中估计子网格尺度云覆盖,该数据具有与其训练数据相似的地理特征。此外,全球培训的NNS可以再现区域SRM仿真的子网格级云覆盖。使用基于游戏理论的可解释性库福芙添加剂解释,我们识别特定湿度和云冰上的过分传播,以及我们基于列的NN不能从全局到区域粗粒度SRM数据完全概括的原因。该解释工具还有助于可视化区域和全球训练的基于列的NNS之间的特征重要性的相似性和差异,并在其云覆盖预测和热力学环境之间揭示了本地关系。我们的结果表明,深度学习的潜力从全球SRMS获得准确但可解释的云覆盖参数化,并表明基于邻域的模型可能是精度和概括性之间的良好折衷。
translated by 谷歌翻译
地震的预测和预测有很长的时间,在某些情况下有肮脏的历史,但是最近的工作重新点燃了基于预警的进步,诱发地震性的危害评估以及对实验室地震的成功预测。在实验室中,摩擦滑移事件为地震和地震周期提供了类似物。 Labquakes是机器学习(ML)的理想目标,因为它们可以在受控条件下以长序列生产。最近的作品表明,ML可以使用断层区的声学排放来预测实验室的几个方面。在这里,我们概括了这些结果,并探索了Labquake预测和自动回归(AR)预测的深度学习(DL)方法。 DL改善了现有的Labquake预测方法。 AR方法允许通过迭代预测在未来的视野中进行预测。我们证明,基于长期任期内存(LSTM)和卷积神经网络的DL模型可以预测在几种条件下实验室,并且可以以忠诚度预测断层区应力,证实声能是断层区应力的指纹。我们还预测了实验室的失败开始(TTSF)和失败结束(TTEF)的时间。有趣的是,在所有地震循环中都可以成功预测TTEF,而TTSF的预测随preseismisic断层蠕变的数量而变化。我们报告了使用三个序列建模框架:LSTM,时间卷积网络和变压器网络预测故障应力演变的AR方法。 AR预测与现有的预测模型不同,该模型仅在特定时间预测目标变量。超出单个地震周期的预测结果有限,但令人鼓舞。我们的ML/DL模型优于最先进的模型,我们的自回归模型代表了一个新颖的框架,可以增强当前的地震预测方法。
translated by 谷歌翻译