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Stochastic Analysis of Probability Density Functions of Radiance Measurements from NASA's Atmospheric Infrared Sounder (AIRS)

Stochastic Analysis of Probability Density Functions of Radiance Measurements from NASA's Atmospheric Infrared Sounder (AIRS)

Series: Modeling and Computation Seminar
Location: Math 402
Presenter: Andrew Tangborn, NASA, Goddard Space Flight Center

We analyze climate variability by applying stochastic analysis techniques to infrared satellite observations over a 10 year period. The Atmospheric Infrared Sounder (AIRS) measures radiation from the Earth's atmosphere in more than 2000 wavelengths, which are sensitive to a variety of physical properties, including temperature, humidity, ozone and carbon dioxide. Many of the physical processes related to these properties can be analyzed stochastically by determining relationships between higher statistical moments and the structure of extremes in probability density functions (PDFs). Long duration satellite missions are becoming a valuable source of data for the study of climate variability and climate extremes. This data can also be compared directly with numerical weather prediction (NWP) model outputs (or reanalyses) by applying a radiative transfer model to NWP models to determine the resulting radiance at the top of the atmosphere. We use this approach to apply the stochastic analysis methods to reanalyses from  NASA and the European Center for Medium Range Forecasts (ECMWF), to show how they can also be used to evaluate the capacity of these models to reproduce climate variability and extremes.

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