Geophysical Statistics Project
From our unique position within CISL and IMAGe, the Geophysical Statistics Project (GSP) has been a leader in training and research emphasizing the synergy between the geosciences and the statistical sciences. In addition to basic methodological and theoretical statistical research, GSP has a strong training component supporting graduate students and postdoctoral visiting scientists. These young researchers are immersed in research activities that not only focus their skills as applied statisticians but also expose them to important applications in the geosciences.
Northern Hemisphere mean temperature
reconstructions based on synthetic proxies embedded in noise and forcings.
The proxies are generated by the algorithms that approximate the
relationship between proxies and temperatures using the synthetic
temperatures from global climate model output. The top panel compares
the reconstruction using different types of proxies: PF uses 15 local
temperatures as proxies; R uses tree ring only; RP uses tree ring and
pollen; RB uses tree ring and borehole; and RPB uses tree ring, pollen,
and borehole. Among the four reconstructions from different combinations
of proxies, RBP has lowest bias and mean squared error. So in the bottom
panel, the reconstruction from RBP with its 95% uncertainty band (gray
area) is displayed. The reconstruction follows the trend of the target
fairly well, and the uncertainty band covers the target most of the
time.
In addition to these core activities, GSP also has an active visitor program providing research opportunities for visiting faculty members from across the nation and abroad. Our goal is to foster collaboration between graduate students, postdocs, the permanent and visiting statistical staff, and NCAR scientists. These programs, as well as the research and training aspects of GSP that emphasize the interaction between statistics and the geosciences, embody the tenets of integration, innovation, and community building within the NCAR strategic plan. Specifically, this program supports the NCAR strategic priorities of "Conducting computer science, computational science, applied mathematics, statistics, and numerical methods R&D," "Supporting and enhancing formal science education at all levels," and "Engaging a broader and more diverse community in the atmospheric and geosciences."
Results of a calibration experiment
matching the output of the Lyon-Fedder-Mobary (LFM) computer model of the
magnetosphere to observations from the Polar Ultraviolet Image (UVI)
during a geomagnetic storm. The blue dots indicate the initial values of
calibration parameters (alpha, beta, and R) based on space-filling
experimental design. These parameters transform the standard MHD
parameters into the average energy and flux of the precipitating
electrons. The contours represent approximate 50 (red), 75 (yellow),
and 95 (white) percent contour shells of the posterior distribution
of the optimal calibration values for alpha, beta, and R based on a
novel statistical approach to calibrating computer models with
high-dimensional outputs.
During FY2008, GSP researchers have been involved in numerous important
projects, including:
- Design and analysis of computer experiments, in particular focusing on regional climate models and models of the upper atmosphere and the magnetosphere
- Developing methodology for analyzing extremes of weather and climate
- Stochastic weather generators
- Modeling uncertainty in climate reconstruction
- Impacts of climate and climate change on public health
GSP continues to develop theory and methodology for analyzing spatial data, including nonstationary covariance models, models for spatial lattice data, multivariate spatial observations, spatial-temporal models, as well as general methodology for computational statistics and Bayesian hierarchical models.
In FY2009, the scientific focus on computer models will continue, in particular through GSP scientists being involved in such NCAR programs as the North American Regional Climate Model Assessment Program (NARCCAP) as well as in collaborations with other computer modeling groups across NCAR. Beyond computer models, GSP scientists will continue to assess the impacts of climate and climate change on public health, to develop methodology for analyzing extremes, to develop methodology for modeling daily weather scenarios, to develop methodology for quantifying the uncertainty in climate reconstructions, and to develop statistical methodology for the analysis of complex, spatial and spatial-temporal data.
This project is made possible through NSF Core funding, as well as grants through NSF's Division of Mathematical Sciences and NSF's Collaboration in Mathematical Geosciences.