CAREER: Big Data Climate Causality Analytics

气候因果分析是气候科学的一个基本问题，它研究温度、湿度等气候变量之间的因果关系。通过从因果关系的角度研究气候系统如何工作，这些发现可以用于许多研究领域，包括气候变率、气候动力学、气候模拟和极端气候预测。当前，气候因果关系研究面临着处理超大高维数据集和现代计算资源复杂性等计算挑战。为了应对这些挑战，该项目的目标是新的因果关系发现算法和相关的可扩展计算技术。该项目有望极大地帮助地球系统科学家和气候科学家探索与气候因果关系相关的新假设和用例。该项目包括一个综合研究、教育和推广计划，以帮助更好地了解和评估气候模拟，促进“数据+计算+气候科学”多学科研究社区的劳动力发展，提高K-12学生和各种代表性不足群体对IT技术和气候研究的兴趣。因此，正如NSF的使命所述，该项目通过促进科学进步和促进国家繁荣和福利来服务于国家利益。这个CAREER项目的目标是研究大规模气候数据的有效和可重复的因果分析，以便气候科学家可以轻松地测试他们的因果假设，重现现有的研究，并比较不同的因果分析结果。为应对时空气候数据集维度和分辨率不断提高的问题，本项目将研究大规模气候数据集的增量因果关系发现算法和时空气候数据的并行因果关系发现算法。为了解决因果发现算法和气候模拟/观测数据集的多样性，该项目将研究如何有效地测量来自不同因果算法和不同气候数据集的气候因果结果，并通过集成技术整合因果结果。为解决在大规模气候数据集上进行和再现因果关系分析的困难，本项目将研究云计算用于大数据气候分析管道建设和执行优化。该项目将从两个角度进行评估。从计算角度出发，将从算法计算复杂度、算法精度和算法可扩展性三个方面对研究进行评价。从气候角度来看，研究的适用性将通过与气候科学家在其具体研究项目中的合作来评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A fundamental problem in climate science is climate causality analysis that studies the cause-effect relationship among climate variables, such as temperature and humidity. By studying how the climate system works from a causality perspective, the findings could be used for many research areas including climate variability, climate dynamics, climate simulation, and extreme climate prediction. Nowadays, climate causality study faces many computing challenges, such as processing very large and high-dimensional datasets, and the complexity of modern computing resources. To tackle these challenges, this project targets novel causality discovery algorithms and related scalable computing techniques. The project is expected to greatly aid Earth System scientists and climate scientists to explore new hypotheses and use cases related to climate causality. The project includes an integrated program of research, education and outreach to help better understand and evaluate climate simulation, fostering workforce development for a multidisciplinary research community on "Data + Computing + Climate Science", and raising interest in both IT technology and climate studies among K-12 students, and various underrepresented groups. The project thus serves the national interest, as stated in NSF's mission, by promoting the progress of science and advancing national prosperity and welfare.The goal of this CAREER project is to study efficient and reproducible causality analytics for large-scale climate data, so that climate scientists can easily test their causal hypotheses, reproduce existing studies and compare different causality analytics results. To handle the increasing dimensionality and resolution of spatiotemporal climate datasets, the project will study incremental causality discovery algorithms for large-scale climate datasets and parallel causality discovery for spatiotemporal climate data. To address the variety of both causal discovery algorithms and climate simulation/observation datasets, the project will study how to effectively measure climate causality results from different causality algorithms and different climate datasets, and integrate causality results through ensemble techniques. To cope with difficulties in conducting and reproducing causality analytics with large-scale climate datasets, the project will study cloud computing for big data climate analytics pipeline construction and execution optimization. The project will be evaluated from two perspectives. From the computing perspective, the research will be evaluated in terms of algorithm computation complexity, algorithm accuracy and algorithm scalability. From the climate perspective, the applicability of the research will be evaluated by collaborating with climate scientists in their specific research programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.