Estimation of Liquid Cloud Properties that Conserve Total-Scene Reflectance Using Satellite Measurements

Author:

Foster Michael J.1,Bennartz Ralf2,Heidinger Andrew3

Affiliation:

1. Space Science and Engineering Center, Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

2. Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

3. NOAA/NESDIS/Center for Satellite Applications and Research, and University of Wisconsin—Madison, Madison, Wisconsin

Abstract

Abstract A new method of deriving statistical moments related to the distribution of liquid water path over partially cloudy scenes is tested using a satellite cloud climatology. The method improves the ability to reconstruct total-scene visible reflectance when compared with an approach that relies on valid liquid water path retrievals, and thus it maintains physical consistency with the primary satellite observations when deriving cloud climatologies. A global application of the new method finds a mean bias of −0.008 ± 0.017 when reconstructing total-scene reflectance from liquid water path distributions, as compared with a bias of 0.05 ± 0.047 when using a conventional approach. Application of the method to a multidecadal cloud climatology suggests that this may provide a means of identifying data artifacts that could affect long-term cloud property trends. The conservation of reflectance plus the ease of applicability to various satellite datasets makes this method a valuable tool for model validation and comparison of satellite climatologies. Gaussian and gamma functions are used to approximate the distribution of horizontal subgrid-scale liquid water path for 1° × 1° scenes, and while both functions perform well for the majority of atmospheric conditions, it is found that the Gaussian distribution generates a negative bias for cases in which visible reflectance is very high and that neither function is able to represent liquid water path well in the few cases in which the observed distribution is bi- or multimodal.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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