A Statistics Program at the National Center for Atmospheric Research

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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.