A New NASA Initiative in Three-Dimensional Tomography of the Aerosol-Cloud Environment (3D-TRACE): Outcome of a Pilot Study

Remote sensing is a key tool for sorting cloud ensembles by dynamical state, aerosol environments by source region, and establishing causal relationships between aerosol amounts, type, and cloud microphysics---the so-called indirect aerosol climate impacts, which is identified as one of the main sources of error in current climate models. Current satellite imagers use data processing approaches that invariably start with cloud detection/masking to isolate aerosol air-masses from clouds, and then rely on one-dimensional (1D) radiative transfer (RT) to interpret the aerosol and cloud measurements in isolation. Not only does this lead to well-documented biases for the estimates of aerosol radiative forcing and cloud optical depths in current missions, but it is fundamentally inadequate for future missions such as ESA's Earth Cloud, Aerosol and Radiation Experiment (EarthCARE), NASA's Pre-ACE (PACE) and Aerosol-Cloud-Ecosystem mission (ACE, a NASA Tier-2 Decadal Survey mission). All of these missions are intent on capturing the complex, three-dimensional (3D) interactions between clouds and aerosols.

In order to advance the state-of-the-art, the next generation of satellite information processing systems must incorporate technologies that will enable the treatment of the atmosphere as a fully 3D environment, represented more realistically as a continuum. At one end, there is an optically thin background dominated by aerosols and molecular scattering that is strongly stratified and relatively homogeneous in the horizontal. At the other end, there are optically thick embedded elements, clouds and aerosol plumes, which can be more or less uniform and quasi-planar or else highly 3D with boundaries in all directions; in both cases, strong internal variability may be present.

To make this paradigm shift possible, we propose to combine the standard models for satellite signal prediction physically grounded in 1D and 3D RT, both scalar and vector, with technologies adapted from biomedical imaging, digital image processing, and computer vision. This will enable us to demonstrate how the 3D distribution of atmospheric constituents, and their associated microphysical properties, can be reconstructed from multi-angle/multi-spectral imaging radiometry and, more and more, polarimetry. Specific technologies of interest are computed tomography (reconstruction from projections), optical tomography (using cross-pixel radiation transport in the diffusion limit), stereoscopy (depth/height retrievals), blind source and scale separation (signal unmixing), and disocclusion (information recovery in the presence of obstructions).

In time, these potentially powerful inverse problem solutions will be fully integrated in a versatile satellite data analysis toolbox. At present, we will report on substantial progress at the component level achieved in the course of a one-year pilot study sponsored by NASA's Earth Science and Technology office (ESTO). We focused specifically on the most elementary problems in atmospheric tomography:

* One basic problem is to infer the outer shape and mean extinction of optically thick cumulus-type 3D clouds, along with a bulk measure of cloud particle size. Two independent approaches were tested at JPL and Columbia University.

* Another is to reconstruct the 3D spatial distribution of aerosol particle density in a plume, or crystal density in a cirrus layer, using only passive imaging. Here again, two independent approaches were tested at JPL and Technion - IIT. See Aides et al. [2013] for a description and demonstration of the latter tomographic reconstruction method.

* Yet another is to separate high (cirrus) and low (broken cumulus) cloud layers based on their characteristically different spatial textures. See Yanovsky et al. [2014] for a description and demonstration of the image processing methodology that was used to solve this basic problem.

Across all of these efforts, the wide-open frontier of multi-angle/multi-pixel algorithms was explored. The suite of five independent feasibility studies will amount to a compelling proof-of-concept for the ambitious 3D-Tomographic Reconstruction of the Aerosol-Cloud Environment (3D-TRACE) project as a whole.

Finally, a notable spin-off of the 3D-TRACE project is the development of a high-performance computing framework for generating high-fidelity synthetic multi-angle imagery to test new algorithms in a setting where the truth is known at every level of detail. First, the JPL Large-Eddy Simulation (LES) code [Matheou and Chung, 2014] is used to obtain very realistic clouds or aerosol plumes (5 to 20 m grid-cells over 5 to 20 km domains). Then Lorentz-Mie code is used to convert the LES's bulk or bin microphysical quantities into optical ones. Finally, the state-of-the-art MYSTIC 3D vector radiative transfer code [Emde et al., 2010] is applied to this large gridded scene using highly optimized backward Monte Carlo methods [Buras and Mayer, 2011] to deliver the imagery just as a remote sensing instrument would record it. This stand-alone capability at JPL will also be used to test operational algorithms in new ways.

References:

Aides, A., Y. Y. Schechner, V. Holodovsky, M. J. Garay, and A. B. Davis (2013). Multi Sky-View 3D Aerosol Distribution Recovery, Opt. Express, 21, 25820-25833.

Emde, C., R. Buras, B. Mayer, and M. Blumthaler (2010). The impact of aerosols on polarized sky radiance: Model development, validation, and applications. Atmos. Chem. Phys., 10, 383-396.

Buras, R., and B. Mayer (2011). Efficient unbiased variance reduction techniques for Monte Carlo simulations of radiative transfer in cloudy atmospheres: The solution. J. Quant. Spectrosc. Radiat. Transfer, 112, 434-447.

Matheou, G., and D. Chung (2014). Large-eddy simulation of stratified turbulence. Part II: Application of the stretched-vortex model to the atmospheric boundary layer. J. Atmos. Sci. (under revision).

Yanovsky, I., A. B. Davis, and V. M. Jovanovic (2014). Separation of radiances from a cirrus layer and broken cumulus clouds in multispectral images. IEEE Trans. Geosc. and Remote Sens. (submitted).