Retrieval of PMC Properties From CIPS: Algorithm Description, Error Analysis and Cloud Detection Sensitivity

Details Retrieval of PMC Properties From CIPS: Algorithm Description, Error Analysis and Cloud Detection Sensitivity Retrieval of PMC Properties From CIPS: Algorithm Description, Error Analysis and Cloud Detection Sensitivity

Dec 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agufmsa41a1587l&link_type=abstract

American Geophysical Union, Fall Meeting 2009, abstract #SA41A-1587

Physics

[0321] Atmospheric Composition And Structure / Cloud/Radiation Interaction, [0320] Atmospheric Composition And Structure / Cloud Physics And Chemistry, [0360] Atmospheric Composition And Structure / Radiation: Transmission And Scattering, [0394] Atmospheric Composition And Structure / Instruments And Techniques

Scientific paper

The Cloud Imaging and Particle Size (CIPS) instrument is a nadir-viewing UV imager aboard the Aeronomy of Ice in the Mesosphere (AIM) satellite. CIPS measures scattered solar radiation at 265 nm using a unique four-camera configuration with an instantaneous field of view of 120 deg by 80 deg. Full instrument resolution for nadir pixels is 1km x 2km. By combining data from multiple cameras, CIPS observes a given volume of air at multiple scattering angles ranging from 20 to 180 degrees. PMC detection and cloud parameter retrievals typically use binned data with a spatial resolution of 15km x 15km but can also be run at higher resolution. The detection algorithm discriminates a PMC signature from the Rayleigh-scattered background by exploiting the fact that the former is strongly forward scattered, whereas the background signal is symmetric about 90 deg scattering angle. We present an overview of the CIPS data processing and retrieval algorithms, concentrating on the separation of cloud and background Rayleigh signatures in the CIPS measurements. The primary CIPS PMC retrieval parameters - cloud albedo, particle mode radius, and ice water content - are discussed and retrieval error estimates are given. We also present an analysis of the CIPS cloud detection sensitivity, with the goal of deriving an effective cloud brightness threshold. This threshold varies with solar zenith angle (and hence latitude) due to both the CIPS measurement sampling characteristics and the geophysical variation in the Rayleigh background. Simulations show that it also depends on the cloud particle radius, as well as the spatial resolution of the cloud product (data binning). Quantifying these dependencies allows one to account for the effects of varying detection sensitivity in interpreting the cloud occurrence frequencies observed by CIPS, particularly the latitude dependence. This provides a quantitative foundation for comparing the CIPS observations with other data sets.

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