The Remote Sensing of Thin Cirrus Cloud Using Satellites, Lidar and Radiative Transfer Theory

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  • 1 Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado/NOAA, Boulder, Colorado
  • | 2 Department of atmospheric Science, Colorado State University, Fort Collins, Colorado
  • | 3 CSIRO, Division of atmospheric Research, Mordialloc, Victoria, Australia
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Abstract

The problem of retrieving cirrus cloud optical depth from radiance measurements made by instruments aboard operational meteorological satellites is addressed. A method is proposed that exploits the relationship between observed differences in the near infrared (NIR) and infrared (IR) window radiances (expressed in terms of brightness temperature differences ΔT) and the optical depth of the cloud. The approach designed to test this method relies on the simultaneous collection of ground-based lidar and infrared radiometric (LIRAD) data, radiosonde data and bispectral satellite images.

Two case studies are described for which independent estimates of satellite pixel and coincident time-averaged LIRAD optical depths are compared with radiative transfer calculations made for hypothetical clouds characterized by distributions of spherical ice particles. Such comparative analyses yield information about cloud microphysics and enable the selection of representative theoretical relationships between estimates of cloud optical depth and observed spectral differences. A third case demonstrates the potential use of this split window technique to estimate cirrus cloud optical depth when only operational data is available.

In the first two cases, it was found that the LIRAD-derived optical depths agree to within 70% of the satellite estimates for optical depths greater than about 0.3, and that the differences tend to be systematic. Larger discrepancies are noted for thinner clouds, however, indicating inaccuracies in one or the other, or possibly both of these methods when applied to very thin layers. Another possible cause for these large discrepancies is the potential ambiguity in comparing the spatially averaged satellite data with time-averaged LIRAD data if physical changes in cloud structure occur during the course of the experiment.

We also found that, in all cases, the observed spectral differences (NIR-IR) agree reasonably well with model simulations if the clouds are assumed to be composed of distributions of large spherical ice particles having effective radii in the 32–64 μm range.

Abstract

The problem of retrieving cirrus cloud optical depth from radiance measurements made by instruments aboard operational meteorological satellites is addressed. A method is proposed that exploits the relationship between observed differences in the near infrared (NIR) and infrared (IR) window radiances (expressed in terms of brightness temperature differences ΔT) and the optical depth of the cloud. The approach designed to test this method relies on the simultaneous collection of ground-based lidar and infrared radiometric (LIRAD) data, radiosonde data and bispectral satellite images.

Two case studies are described for which independent estimates of satellite pixel and coincident time-averaged LIRAD optical depths are compared with radiative transfer calculations made for hypothetical clouds characterized by distributions of spherical ice particles. Such comparative analyses yield information about cloud microphysics and enable the selection of representative theoretical relationships between estimates of cloud optical depth and observed spectral differences. A third case demonstrates the potential use of this split window technique to estimate cirrus cloud optical depth when only operational data is available.

In the first two cases, it was found that the LIRAD-derived optical depths agree to within 70% of the satellite estimates for optical depths greater than about 0.3, and that the differences tend to be systematic. Larger discrepancies are noted for thinner clouds, however, indicating inaccuracies in one or the other, or possibly both of these methods when applied to very thin layers. Another possible cause for these large discrepancies is the potential ambiguity in comparing the spatially averaged satellite data with time-averaged LIRAD data if physical changes in cloud structure occur during the course of the experiment.

We also found that, in all cases, the observed spectral differences (NIR-IR) agree reasonably well with model simulations if the clouds are assumed to be composed of distributions of large spherical ice particles having effective radii in the 32–64 μm range.

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