A Statistics Program at the National Center for Atmospheric Research

摘要：道格拉斯·尼奇卡提议：美国国家大气研究中心（NCAR）的地球物理统计项目（GSP）为解决地球科学与统计学之间的界面上的重要研究问题提供了一个独特的机会。统计研究包括非平稳空间和时空数据的分析，计算机实验的设计和分析，过滤，极端统计，数据挖掘和统计计算。这些方法适用于大型数据集，并且公开可用的软件可用于实现空间数据和极端的新方法。对地球科学应用的智力贡献同样广泛，包括引入子网格尺度过程的随机模拟，估计极端事件的气候学，空间相干天气发生器的构建，以及数据同化方法的统计改进。本世纪的一个重大科学挑战是理解大气，海洋，陆地过程，生物圈和人类活动，定义了地球系统。与此同时，还需要预测我们环境的变化，并将这些变化转化为更直接的经济和社会影响。 伊萨个相应的挑战，统计科学，以处理大型和复杂的数据集，现在是规范的许多领域的科学和工程，并利用丰富的结构和先验知识所提供的传统数值模型. 通过对地质学家的培训，一个积极的访问者计划和参与NCAR的几项战略举措，这项工作覆盖了地球科学领域的广泛的统计学家和研究人员。在这种背景下，一些具体的问题是设计和分析大型计算机实验，模拟地球的气候，推断历史气象数据集的位置，他们没有观察到，改善算法用于进行数值天气预报，并确定模型，其中包括随机过程来表示复杂的地球物理现象。一个共同的元素在整个这项研究是量化的不确定性在一个派生的方法或结果。不确定性估计通常很难在没有统计学处理的情况下获得，但当分析随后用于决策或评估风险时尤为重要。

ABSTRACTPI: Douglas Nychkaproposal: 0355474The Geophysical Statistics Project (GSP) at the National Center forAtmospheric Research (NCAR) provides a unique opportunity to addressimportant research problems at the interface between the geosciences andstatistics. The statistical research includes contributions to theanalysis of nonstationary spatial and spatio-temporal data, the design andanalysis of computer experiments, filtering, the statistics of extremes,data mining, and statistical computing. These methods are adapted to largedata sets and publicly available software is available to implement newmethods for spatial data and extremes. The intellectual contributions toapplications in the geosciences is equally broad and includes theintroduction of stochastic modeling for sub-grid scale processes,estimating the climatology for extreme events, the construction ofspatially coherent weather generators, and statistical improvements tomethods of data assimilation.A grand scientific challenge for this century is to understand the complexinterrelationships among the atmosphere, ocean, land processes, biosphereand human activities that define the Earth System. Coupled with thiseffort is the need to predict changes to our environment and to translatesuch changes into more immediate economic and societal impacts. There isa corresponding challenge for statistical science to tackle the large andcomplex data sets that are now the norm in many areas of science andengineering and to leverage the rich structure and prior knowledgeafforded by traditional numerical models. Through the training of youngresearchers, a vigorous visitor program and participation within severalof the NCAR strategic initiatives, this work reaches a wide communityof statisticians and researchers in the geosciences. In this context, somespecific problems are the design and analysis of large computerexperiments that simulate the Earth's climate, extrapolating historicalmeteorological data sets to locations where they are not observed,improving the algorithms used to make numerical weather predictions, andidentifying models that include random processes to represent complicatedgeophysical phenomenon. One common element throughout this research isquantifying the uncertainty in a derived method or result. Uncertaintyestimates are often difficult to obtain without a statistical treatmentbut are particularly important when the analysis is subsequently used fordecision making or to assess risks.