A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Abstract

† The contribution of H. W. Barker to this article was prepared as part of his official duties as a Canadian government employee This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active-passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed.The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km) 2 domains constructed from MODIS, CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m −2 for ∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation ∼65% are within ±10 W m −2 .

HETEAC: The Aerosol Classification Model for EarthCARE

“…MSI observations will also be used to extend the twodimensional cross-sections from lidar and radar into the three-dimensional domain, which then will serve as input to broadband radiative-transfer models [2]. In this way, fluxes, heating rates, and radiances can be calculated and top-of-atmosphere (TOA) radiances and fluxes compared to those derived from BBR measurements.…”

Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data

“…Obviously this can influence both 1D and 3D radiative transfer solutions, as well as their differences which are of concern here. Nevertheless, when clouds produced this way are operated on by 1D shortwave and longwave radiative transfer algorithms, local TOA flux estimates agree well with CERES data, particularly longwave values (Barker et al 2011).…”

“…To get around the issue of satellite retrievals being essentially 2D cross-sections, 3D fields can be constructed using the algorithm of Barker et al (2011) that was developed for use by the EarthCARE satellite mission (ESA 2001). EarthCARE is slated for launch in 2015 and will carry a high-spectral resolution lidar, a 94-GHz cloud Doppler radar, a 7-channel imager and a broadband radiometer.…”

“…A-train data and retrieval products (Kato et al 2010) construction algorithm developed by Barker et al (2011) to construct 3D cloudy atmospheres. A broadband solar transport solver (capable of affecting 1D and 3D solutions) was then applied to them.…”

Scientific aspects of the Earth clouds, Aerosols, and Radiation Explorer (EarthCARE) mission

“…Radiative transfer codes are then applied to the 3-D scene and their estimated radiometric quantities are compared to corresponding BBR measurements. Given that BBR data will not be used for any retrieval, this procedure constitutes a form of radiative closure assessment of the L2 retrievals [6].…”