HR: 09:30h
AN: A11E-06    [Abstracts]
TI: Comparing MISR and MODIS Data to Output from the IMPACT Aerosol Transport Model Using the Aerosol Measurement and Processing System
AU: * Braverman, A
EM: Amy.Braverman@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Penner, J
EM: penner@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48109, United States
AU: Xu, L
EM: lixum@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48109, United States
AU: Chuang, C
EM: chuang1@llnl.gov
AF: Lawrence-Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, United States
AU: Wilson, B
EM: Brian.Wilson@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Manipon, G
EM: Geraldjohn.Manipon@jpl.nasa.gov
AF: Raytheon Corporation, 299 N. Euclid Ave., Pasadena, CA 91101, United States
AU: Xing, Z
EM: Zhangfan.Xing@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Paradise, S
EM: Susan.Paradise@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AB: Large-scale comparisons of model output and observational data can be notoriously difficult. Typically, observational data like those from EOS instruments MISR (the Multi-angle Imaging SpectroRadiometer) and MODIS (the Moderate Resolution Imaging Spectrometer) are massive, distributed at different physical locations, and heterogenous: their observation grids are not coincident either with each other or with model grids. The usual strategy for comparing such disparate data sets is to aggregate the high-resolution observations to a coarse common spatial and temporal resolution that matches that of the model. For example, one may compare IMPACT (the Integrated Massively Parallel Atmospheric Chemical Transport model) output, which are daily predictions of aerosol optical depth (aod) by aerosol particle type, on a one-degree spatial grid, to averages of MISR and MODIS optical depths for the same grid cells on the same days. However, this makes no use of the distributions of optical depth provided by both MISR and MODIS in those grid cells. To compare IMPACT predictions to MISR and MODIS, we conduct hypothesis tests by grid cell and day, testing whether IMPACT predicted values could arise from statistical populations represented by the MISR and MODIS data distributions. The Aerosol Measurement and Processing System (AMAPS), a distributed scientific computing environment for aerosol science, makes it possible for us to carry out these tests on a large scale. In this talk, we report the results for a global, full-year (2001) analysis.
DE: 0305 Aerosols and particles (0345, 4801, 4906)
DE: 0520 Data analysis: algorithms and implementation
DE: 0550 Model verification and validation
DE: 3309 Climatology (1616, 1620, 3305, 4215, 8408)
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting