IMAGe

IMAGe Brown Bag- Colette Smirniotis

Modeling Snow Water Equivalent in the Rocky Mountain Region

Colette Smirniotis
San Diego State University

IMAGe Brown Bag - Lee Richardson, Carnegie Mellon

The Quicksort Probability Distribution 
Lee Richardson
Carnegie Mellon University

IMAGe Brown Bag

Uncertainty in Pattern Scaling and Addressing Big Data

Doug Nychka

NCAR/IMAGe

IMAGe Brown Bag

Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical model outputs

Dr. Julie Bessac
Argonne National Laboratory

Bayes in Space! — NASA’s CO2 Measurements and Uncertainty Quantification

NASA's Orbiting Carbon Observatory-2 (OCO-2) mission is now actively collecting space-based measurements of atmospheric carbon dioxide (CO2). Data are collected with high spatial and temporal resolution and the data product includes both an estimate of column averaged CO2 dry air mole fraction (XCO2) and an estimate of uncertainty. In this talk we will take a look at how these estimates are obtained. As with any remote sensing method, the measurements are indirect. The OCO-2 instrument measures reflected sunlight in three spectral bands that make a single "sounding”.

Two studies of statistical modeling of environmental extremes

In this talk, we will present two separate studies which analyze environmental extremes.

NIMBLE Webinar

What: Programming with models: An introduction to NIMBLE, a BUGS-compatible system for hierarchical statistical modeling using MCMC and more

When: May 27, 2016, 10:00 am – 12:00 pm

Where: ML- VisLab 

Beyond P-values Course: Bayesian Statistics

CISL is excited to announce the second course coordinated by The Institute for Mathematics Applied to Geosciences (IMAGe) in the Beyond P-values series.

Global Spatial Statistics

Global-scale geophysical, environmental, and climate science data sets require statistical models that explain the curvature of their spatial domain. Over the last few decades, statisticians have developed covariance models to capture their spatial and/or temporal behavior. Mathematical limitations have prevented the use of the geodesic distance, the most natural metric for measuring distance on the surface of a sphere, and instead some previous approaches have applied the Euclidean or chordal distance to approximate the covariance.

Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies

This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data.

Pages

Subscribe to IMAGe