With increasing number of crowdsourced private automatic weather stations (called TPAWS) established to fill the gap of official network and obtain local weather information for various purposes, the data quality is a major concern in promoting their usage. Proper quality control and assessment are necessary to reach mutual agreement on the TPAWS observations. To derive near real-time assessment for operational system, we propose a simple, scalable and interpretable framework based on AI/Stats/ML models. The framework constructs separate models for individual data from official sources and then provides the final assessment by fusing the individual models. The performance of our proposed framework is evaluated by synthetic data and demonstrated by applying it to a re-al TPAWS network.
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准确地估算主要山区盆地中的积雪对于水资源经理来说至关重要,以便做出影响当地和全球经济,野生动植物和公共政策的决策。目前,此估计需要多个配备LIDAR的飞机飞行或原位测量值,两者均昂贵,稀疏和对可访问区域有偏见。在本文中,我们证明了来自多个,公开可用的卫星和天气数据源的空间和时间信息的融合,可以估算关键山区的积雪。我们的多源模型的表现优于单源估计值5.0英寸RMSE,并且优于稀疏的原位测量值的估计值1.2英寸RMSE。
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在过去的几十年中,风产能的增长表明,风能可以促进世界许多地区的能源过渡。对于模型的高度可变和复杂,对风能的时空变化和相关的不确定性的定量与能源计划者高度相关。机器学习已成为执行风速和功率预测的流行工具。但是,现有方法有几个局限性。其中包括(i)在风速数据中不足以考虑时空相关性,(ii)缺乏量化风速预测不确定性及其对风能估算的不确定性的现有方法,以及(iii)焦点在少于小时的频率上。为了克服这些局限性,我们引入了一个框架,以从不规则分布的风速测量值中的常规网格上重建时空场。将数据分解为时间引用的基础函数及其相应的空间分布系数后,后者是使用极端学习机对空间建模的。然后,对模型和预测不确定性的估计及其在风速转化为风能后的传播的估计值,然后将提供对数据分布模式的任何假设。该方法适用于研究瑞士100米轮毂高度的250 x 250平方米的小时风能潜力,为该国提供了其类型的第一个数据集。潜在的风力发电与风力涡轮机安装的可用区域相结合,以估算瑞士风力发电的技术潜力。此处介绍的风力估算代表了计划人员的重要意见,以支持风力发电增加的未来能源系统的设计。
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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.
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消耗的湖冰是气候变化指标,就像海平面上升或冰川静修一样。监测冰冰物候(LIP)是有用的,因为长期冻结和融化模式充当了哨兵,以了解区域和全球气候变化。我们报告了一项针对瑞士奥伯伦加丁地区的研究,那里有几个中小型山区湖泊。我们从光学卫星图像中观察到唇部事件,例如冻结,分手和冰盖持续时间(2000-2020)。我们通过对这些高山湖泊的湖泊冰层估算有监督的机器学习的空间分辨图来分析MODIS图像的时间序列。为了训练分类器,我们依靠基于网络摄像头图像手动注释的参考数据。从冰图中,我们得出了长期的唇部趋势。由于网络摄像头数据仅适用于两个冬季,因此我们与操作MODIS和VIIRS SNOW PRODUCTS进行了交叉检查结果。我们发现,对于湖泊和西瓦普拉纳(Lakes Sils)和Silvaplana,每年的完全冻结持续时间为-0.76和-0.89天。此外,我们观察到唇部趋势与在附近气象站测得的气候数据的合理相关性。我们注意到,平均冬季空气温度与冻结持续时间和分手事件以及与冻结事件的正相关性具有负相关性。此外,我们观察到在冬季,阳光与冻结持续时间和分手事件之间存在很强的负相关性。
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降水预测是一项重要的科学挑战,对社会产生广泛影响。从历史上看,这项挑战是使用数值天气预测(NWP)模型解决的,该模型基于基于物理的模拟。最近,许多作品提出了一种替代方法,使用端到端深度学习(DL)模型来替代基于物理的NWP。尽管这些DL方法显示出提高的性能和计算效率,但它们在长期预测中表现出局限性,并且缺乏NWP模型的解释性。在这项工作中,我们提出了一个混合NWP-DL工作流程,以填补独立NWP和DL方法之间的空白。在此工作流程下,NWP输出被馈入深层模型,该模型后处理数据以产生精致的降水预测。使用自动气象站(AWS)观测值作为地面真相标签,对深层模型进行了监督训练。这可以实现两全其美,甚至可以从NWP技术的未来改进中受益。为了促进朝这个方向进行研究,我们提出了一个专注于朝鲜半岛的新型数据集,该数据集称为KOMET(KOMEN(KOREA气象数据集),由NWP预测和AWS观察组成。对于NWP,我们使用全局数据同化和预测系统-KOREA集成模型(GDAPS-KIM)。
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机器学习(ML)近年来往往应用于太空天气(SW)问题。 SW起源于太阳能扰动,包括由此产生的复杂变化,它们导致太阳和地球之间的系统。这些系统紧密耦合并不太了解。这为熟练的模型创造了具有关于他们预测的信心的知识。这种动态系统的一个例子是热层,地球上层大气的中性区域。我们无法预测其在低地球轨道中对象的卫星拖拽和碰撞操作的背景下具有严重的影响。即使使用(假设)完美的驾驶员预测,我们对系统的不完全知识也会导致往往是不准确的中性质量密度预测。正在进行持续努力来提高模型准确性,但密度模型很少提供不确定性的估计。在这项工作中,我们提出了两种技术来开发非线性ML模型以预测热散,同时提供校准的不确定性估计:蒙特卡罗(MC)丢失和直接预测概率分布,既使用预测密度(NLPD)损耗函数的负对数。我们展示了在本地和全局数据集上培训的模型的性能。这表明NLPD为这两种技术提供了类似的结果,但是直接概率方法具有更低的计算成本。对于在集合HASDM密度数据库上回归的全局模型,我们在具有良好校准的不确定性估计的独立测试数据上实现11%的错误。使用原位校准密度数据集,这两种技术都提供了13%的测试误差。 CHAMP模型(独立数据)占测试所有预测间隔的完美校准的2%。该模型也可用于获得具有给定时期的不确定性的全局预测。
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陆地温度(LST)是监控土地面过程时的关键参数。然而,云污染和空间和时间分辨率之间的权衡大大妨碍了对高质量的热红外(TIR)遥感数据的访问。尽管采取了巨大的努力来解决这些困境,但仍然难以通过并发空间完整性和高时空分辨率产生LST估计。陆地表面模型(LSM)可用于模拟高度的时间分辨率的Genpless LST,但这通常具有低空间分辨率。在本文中,我们向卫星观察和LSM模拟LST数据提供了一个集成的温度融合框架,以通过60米的空间分辨率和半小时时间分辨率映射Gapless LST。全局线性模型(GLOLM)模型和昼夜陆地表面温度周期(DTC)模型分别作为预处理步骤进行传感器和不同LST数据之间的时间归一化。然后使用基于滤波器的时空集成融合模型融合Landsat LST,适度分辨率成像光谱仪(MODIS)LST和社区土地模型5.0(CLM 5.0)-SIMUTION LST。在一个城市主导地区(中国武汉市)和自然主导地区(中国海河流域)实施了评估,在准确性,空间可变性和日颞动力学方面。结果表明,熔融LST与实际LANDSAT LST数据(原位LST测量)高于Pearson相关系数,在0.94(0.97-0.99)方面,平均绝对误差为0.71-0.98k(0.82-3.17 k )和根平均误差为0.97-1.26 k(1.09-3.97 k)。
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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。
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降雨事件的遥感对于运营和科学需求至关重要,包括天气预报,极端洪水,水循环监测等。降水量的降水量。然而,这种雷达的观察范围仅限于几百公里,促使对其他遥感方法的探索,在开阔的海洋上,这代表了不被陆基雷达覆盖的大面积。几十年来,众所周知,诸如Sentinel-1图像之类的C波段SAR图像在海面上表现出降雨签名。但是,SAR来源的降雨产品的开发仍然是一个挑战。在这里,我们提出了一种深度学习方法,以从SAR图像中提取降雨信息。我们证明,在接触和预处理的Sentinel-1/Nexrad数据集中训练的卷积神经网络,例如U-NET,显然优于最先进的过滤方案。我们的结果表明,在分割降水状态下的性能高,由1、3和10 mm/h的阈值描绘。与当前依靠Koch过滤器绘制二进制降雨图的方法相比,这些基于多阈值的模型可以为更高的风速提供降雨估计,因此对于数据同化天气预测或提高SAR的资格可能引起了极大的兴趣 - 衍生的风场数据。
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In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. While AI-driven digital agriculture tools can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market and accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results reveal that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities.
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In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop a causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently estimate it using several methods on observational data. The results show that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%).
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Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication.
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野火预测对于减少灾害风险和环境可持续性至关重要。我们将每日火灾危险预测作为机器学习任务,使用过去十年来预测下一天的火灾危险。为此,我们收集,预先处理和协调开放式DataCube,其中包括一组协变量,共同影响火灾发生和传播,例如天气条件,卫星衍生的产品,与人类活动相关的地形特征和变量。我们实施各种深度学习(DL)模型,以捕获空间,时间或时空上下文,并将它们与随机林(RF)基线进行比较。我们发现空间或时间上下文足以超越RF,而利用时空上下文的Convlstm在接收器的操作特性为0.926的接收器下的测试区域最佳地执行。我们基于DL的概念证明提供了全国范围的日常火灾危险地图,其空间分辨率高于现有的运营解决方案。
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鉴于人工智能(AI)和机器学习(ML)方法越来越多,环境科学各方面的方法,我们必须讨论关于AI的道德和负责任使用。事实上,大大可以从其他领域学习,通常是最好的意图,但经常导致意外的社会后果,如刑事司法系统中的硬编码种族偏见或通过金融体系增加经济不平等。常见的误解是,当使用AI时,环境科学对这种非预期的后果免疫,因为大多数数据来自观察,并且AI算法基于数学公式,这些公式通常被视为物镜。在本文中,我们争论可能就是这样。使用具体示例,我们展示了许多方式,其中使用AI可以引入环境科学的类似后果。本文将刺激讨论和研究努力。作为一个社区,我们应该通过引入AI来避免重复在其他域中的任何可预见的错误。事实上,通过适当的预防措施,AI可以成为帮助{\它减少}气候和环境不公正的伟大工具。我们主要关注天气和气候示例,但结论普遍存在环境科学中。
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Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also performs implicit outlier detection during the clustering. AWT integrates ideas of several well-known K-Means clustering algorithms. It chooses the number of clusters automatically based on a user-defined threshold parameter, and it can be used for heterogeneous meteorological input data as well as for data sets that exceed the available memory size. We apply AWT to crowd sourced 2-m temperature data with an hourly resolution from the city of Vienna to detect outliers and to investigate if the final clusters show general similarities and similarities with urban land-use characteristics. It is shown that both the outlier detection and the implicit mapping to land-use characteristic is possible with AWT which opens new possible fields of application, specifically in the rapidly evolving field of urban climate and urban weather.
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提出了一个深度学习模型,以便在未来60分钟的五分钟时间分辨率下以闪电的形式出现。该模型基于反复横向的结构,该结构使其能够识别并预测对流的时空发展,包括雷暴细胞的运动,生长和衰变。预测是在固定网格上执行的,而无需使用风暴对象检测和跟踪。从瑞士和周围的区域收集的输入数据包括地面雷达数据,可见/红外卫星数据以及衍生的云产品,闪电检测,数值天气预测和数字高程模型数据。我们分析了不同的替代损失功能,班级加权策略和模型特征,为将来的研究提供了指南,以最佳地选择损失功能,并正确校准其模型的概率预测。基于这些分析,我们在这项研究中使用焦点损失,但得出结论,它仅在交叉熵方面提供了较小的好处,如果模型的重新校准不实用,这是一个可行的选择。该模型在60分钟的现有周期内实现了0.45的像素临界成功指数(CSI)为0.45,以预测8 km的闪电发生,范围从5分钟的CSI到5分钟的提前时间到CSI到CSI的0.32在A处。收货时间60分钟。
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该调查侧重于地球系统科学中的当前问题,其中可以应用机器学习算法。它概述了以前的工作,在地球科学部,印度政府的持续工作,以及ML算法的未来应用到一些重要的地球科学问题。我们提供了与本次调查的比较的比较,这是与机器学习相关的多维地区的思想地图,以及地球系统科学(ESS)中机器学习的Gartner的炒作周期。我们主要关注地球科学的关键组成部分,包括大气,海洋,地震学和生物圈,以及覆盖AI / ML应用程序统计侦查和预测问题。
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提出了一种使用天气数据实时太阳生成预测的新方法,同时提出了既有空间结构依赖性的依赖。随着时间的推移,观察到的网络被预测到较低维度的表示,在该表示的情况下,在推理阶段使用天气预报时,使用各种天气测量来训练结构化回归模型。从国家太阳辐射数据库获得的德克萨斯州圣安东尼奥地区的288个地点进行了实验。该模型预测具有良好精度的太阳辐照度(夏季R2 0.91,冬季为0.85,全球模型为0.89)。随机森林回归者获得了最佳准确性。进行了多个实验来表征缺失数据的影响和不同的时间范围的影响,这些范围提供了证据表明,新算法不仅在随机的情况下,而且在机制是空间和时间上都丢失的数据是可靠的。
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预测野火蔓延对于土地管理和灾害准备至关重要。为此,我们呈现“第二天野火蔓延,”一种策划,大规模的多变量数据集,历史野火的历史野火占据了美国近十年的遥感数据。与基于地球观测卫星的现有火灾数据集相比,我们的数据集合了2D解释性变量(例如,地形,植被,天气,干旱指数,人口密度)与2D区域对齐,提供了丰富的数据为机器学习设置。为了演示该数据集的有用性,我们实现了一个卷积的AutoEncoder,它利用了该数据的空间信息来预测野火扩散。我们将神经网络与其他机器学习模型的性能进行比较:Logistic回归和随机林。该数据集可以用作基于遥感数据开发野火传播模型的基准,以便有一天的提前期。
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