Title
Data Mining to Improve Climate Models
Speaker
Amy Braverman, Jet Propulsion Laboratory, California Institute of Technology
Abstract
Given very large volumes of remote sensing data and climate model output, one would like to be able to compare them in order to understand where, when and why model predictions do not agree with observations. Due to the large volumes, and to incongruities between instrument observation techniques and models, the traditional approach is to reduce both data sources by averaging important parameters up to coarse, common resolution. This destroys information about high-resolution dependencies among parameters, which are often important sources of model-data discrepancies. Instead, we replace the means with nonparametric multivariate distribution estimates of multiple quantities of interest. We then perform statistical hypothesis tests to determine whether distributions produced from model output agree with those for the same coarse grid cell obtained from observations. If differences exist, we can isolate them with another suite of hypothesis tests that identify the distributional characteristics causing the problems. In this talk, we report on work to assess and diagnose the Geophysical Fluid Dynamics Laboratory's AM2 atmospheric model.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
