Geophysical Statistics Project
http://www.cgd.ucar.edu:80/stats/index.shtml

Doug Nychka, Richard Katz, Joe Tribbia, National Center for Atmospheric Research, Climate and Global Dynamics Division, Geophysical Statistics Project, National Center for Atmospheric Research, Environmental and Societal Impacts Group, National Center for Atmospheric Research, Climate and Global Dynamics Division


The mission of the Geophysical Statistics Project (GSP) is to encourage the application and further development of statistical analysis to problems faced in the Earth sciences. GSP is an institution-wide effort at the National Center for Atmospheric Research (NCAR) and is funded by the Division of Mathematical Sciences of the National Science Foundation. Some major research areas of GSP are: extension of statistical methodology for spatial processes and space/time processes; application of modern regression and model selection to the analysis of geophysical data; deriving a statistical basis for forecasting including the assimilation of observational data with numerical models; modeling complicated physical processes through Bayesian hierarchical models; and understanding physical processes through the use of dynamical systems and nonlinear time series. The GSP attempts to serve as a bridge between the atmospheric-oceanographic and the statistical-probabilistic research communities. The project sponsors visits by statisticians at various levels, as well as postdoctoral positions. Datasets, reports, and software may be downloaded, and links are provided to useful statistics and atmospheric sites.

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This resource originally cataloged at:

DLESE

Subject: Geoscience:Geology, Atmospheric Science, Oceanography, Hydrology, Environmental Science:Ecosystems:Biogeochemical cycling, Biology, Geoscience:Atmospheric Science:Climatology , Geoscience:Oceanography:Physical
Resource Type: Datasets and Tools:Tools, Audio/Visual:Images/Illustrations, Maps, Datasets and Tools:Datasets
Grade Level: Graduate/Professional
Data Derived: Data Derived
Data Source: Real-Time Data, Observational Data
Science Background Required: Basic scientific background required
Topics: Hydrosphere/Cryosphere, Ocean, Energy/Material cycles, Ocean:Physical Oceanography, Climate, Atmosphere, Biosphere, Chemistry/Physics/Mathematics, Earth surface