Abstract
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 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional 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.
Highlights
Satellite imagery offers a valuable perspective for tracking deep convection that is advantageous in being both spatially broad and contiguous and persistent in time
Given that Meteosat Second Generation (MSG) data are incorporated into the Global Space-based Intercalibration System (GSICS) intercalibration analysis, we expect that methods developed from GOES and Himawari will perform consistently when applied to MSG data, which is a claim supported by analyses not shown in this paper
An initial VIS anvil rating is constructed based on three main considerations: (1) the width and height of the histogram peak, excluding a possible peak at low-reflectance bins that correspond to clear-sky areas, (2) the difference between the observed reflectance and some nominal reflectance predicted by the bidirectional reflectance distribution function (BRDF) model, and (3) the existence of saturated pixels corresponding to bright overshooting tops (OTs) edges or sun glint from clouds
Summary
Satellite imagery offers a valuable perspective for tracking deep convection that is advantageous in being both spatially broad and contiguous and persistent in time. Unlike the case of land surface BRDF retrieval, which requires atmospheric correction, DCC tops reside near the tropopause, above which absorption effects are relatively small and albedo distribution is assumed to be effectively constant month to month (Hu et al, 2004) Owing to these characteristics, a DCC-based VIS BRDF model was developed from CERES and Visible Infrared Scanner (VIRS) observations for the purpose of post-launch calibration of satellite sensors. The DCC technique allows for characterization of sensor gain stability early in an instrument’s lifetime – forgoing the 2-year time period necessary for traditional deseasonalization methods, and thereby enabling more timely calibration stability analyses for any imager with a similar sun-synchronous orbit (Bhatt et al, 2017a, b) These studies demonstrate that a DCC-sourced BRDF can accurately predict expected cloud reflectance for a given range of viewing zenith angle (VZA), solar zenith angle (SZA), and relative azimuth angle (RAA) conditions, thereby allowing for accurate monitoring of satellite imager stability. Consistency of the anvil detection scheme and related COD parameterization owed to the BRDF model are beneficial to operational forecasting and nowcasting efforts, e.g., convection avoidance or interception for flight routing purposes or airborne science campaigns, which rely on accurate, realtime information
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