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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-206
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-206
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  11 Jun 2020

11 Jun 2020

Review status
A revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

Benjamin Scarino1, Kristopher Bedka2, Rajendra Bhatt1, Konstantin Khlopenkov1, David R. Doelling2, and William L. Smith Jr.2 Benjamin Scarino et al.
  • 1Science Systems and Applications, Inc., One Enterprise Pkwy Ste 200, Hampton, VA 23666 USA
  • 2NASA Langley Research Center, 21 Langley Blvd MS 420, Hampton, VA 23681-2199 USA

Abstract. Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A one-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bi-directional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

Benjamin Scarino et al.

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Benjamin Scarino et al.

Benjamin Scarino et al.

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Latest update: 23 Sep 2020
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Short summary
This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles. A one-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angles.
This paper highlights a technique for facilitating anvil cloud detection based on visible...
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