HR: 1340h
AN: A33C-0914    [Abstracts]
TI: Quantifying uncertainty in remotely sensed cloud microphysical properties through MODIS and MISR fusion
AU: * Di Griolamo, L
EM: larry@atmos.uiuc.edu
AF: Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, 105 South Gregory Street, Urbana, IL 61801
AU: Liang, L
EM: lliang4@atmos.uiuc.edu
AF: Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, 105 South Gregory Street, Urbana, IL 61801
AU: Platnick, S
EM: steven.platnick@nasa.gov
AF: NASA Goddard Space Flight Center, Code 613.2, Greenbelt, MD 20771
AB: Global, remotely sensed cloud microphysical properties are now commonplace using meteorological satellite instruments measuring scattered solar radiation. The remote sensing algorithms used to retrieve these properties make numerous assumptions (e.g., 1-D radiative transfer applies, vertical homogeneity, fixed drop size distribution, and more). Although we have a limited understanding of the uncertainties in the remotely sensed cloud microphysical properties through field campaigns and simulations using forward radiative transfer models (1-D and 3-D), we do not yet know the global distribution of these uncertainties. We examine the uncertainties in the Moderate Resolution Imaging Sepctroradiometer (MODIS) cloud microphysical products through fusion with the Multi-angle Imaging SpectroRadiometer (MISR), both of which are on the EOS-Terra satellite platform. Two approaches are taken: (1) a reversal in the look-up table, whereby MODIS retrieved optical depth and effective radius are used to find radiances at the nine MISR camera angles, and (2) a MISR look-up table using the MISR near-IR radiance and MODIS-derived droplet effective radius to produce an estimate of the optical depth for each of the nine MISR camera angles. In approach (1), the angular signatures from the look-up table are compared with the MISR observations to ascertain uncertainty. In approach (2), the spread in the nine estimates of optical depth are used to ascertain uncertainty. The two approaches are applied to a global dataset and uncertainties are stratified by cloud type and cloud texture. Our results show that the smallest uncertainties are for single layered low stratus clouds, while the largest uncertainties occur around cloud edges, large spatial gradients in cloud top height, and for small cumulus clouds.
DE: 0321 Cloud/radiation interaction
DE: 0360 Radiation: transmission and scattering
DE: 3310 Clouds and cloud feedbacks
DE: 3360 Remote sensing
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005