Spaceborne observations serve as input to models and retrieval algorithms of various atmospheric properties. Specifically, we seek 3-D volumetric scattering tomography of clouds. Toward this, cloud fields are to be imaged simultaneously from multiple directions. The CloudCT project aims to demonstrate this by the formation of ten nanosatellites. Based on this data, scattering tomography will seek the 3-D volumetric distribution of cloud microphysical properties. We present constraints and fundamental considerations for spaceborne formation-based cloud tomography. We quantitatively compare visible (VIS) polarized imagers, VIS unpolarized imagers, and short-wave infrared unpolarized imagers. Each possibility is considered using a large eddy simulation of clouds. We study tomographic quality in the presence of sensor and photon noise, calibration errors, and stray light. We find that a polarized imager of a red waveband is preferable.

Methods based on statistical learning have become prevalent in various signal processing disciplines and have recently gained traction in atmospheric lidar studies. Nonetheless, such methods often require large quantities of annotated or resolved data. Such data are rare and require effort, especially when exploring evolving phenomena. Existing simulators and databases primarily focus on atmospheric vertical profiles. We propose the Atmospheric Lidar Data Augmentation (ALiDAn) framework to fill this gap. ALiDAn serves as an end-to-end generation and augmentation framework of spatiotemporal and multiwavelength resolved lidar simulated data. ALiDAn employs a hybrid approach of physical models, data statistics, and sampling processes. In addition, it takes into account geographical and seasonal characteristics of aerosols and meteorological conditions along with short- and long-term phenomena that affect lidar measurements. This approach can provide diversified data and robust benchmarks to assist in developing and validating new lidar processing algorithms. We demonstrate simulations compatible with a pulsed time-of-flight lidar. Our approach leverages a broader use of existing databases and can inspire similar data augmentation to other types of lidars and active sensors.

We introduce a comprehensive method for space-borne 3-D volumetric scattering-tomography of cloud microphysics, developed for the CloudCT mission. The retrieved microphysical properties are the liquid-water-content (LWC) and effective droplet radius within a cloud. We include a model for a perspective polarization imager and an assumption of 3-D variation of the effective radius. Elements of our work include computed tomography initialization by a parametric horizontally uniform microphysical model. This results in smaller errors than the prior art. The mean absolute errors of the retrieved LWC and effective radius are reduced from 62% and 28% to 40% and 9%, respectively. The parameters of this initialization are determined by a grid search of a cost function. Furthermore, we add viewpoints in the cloudbow region, to better sample the polarized scattering phase function. The suggested advances are evaluated by retrieval of a set of clouds generated by large-eddy simulations.

Calibration of an atmospheric lidar is often required due to variations in the electro-optical system. Rayleigh fitting commonly performed may fail under various conditions. Temporal and spatial variations both affect lidar signals. We hence opt for spatiotemporal analysis. We present a novel deep-learning (DL) lidar calibration model based on convolutional neural networks (CNN). We demonstrate our method on simulated data that mimics natural ground-based pulsed time-of-flight lidar signals. Such an approach can better address measurements with a poor signal-to-noise ratio (SNR) and provide a more frequent calibration.

We introduce new adjustments and advances in space-borne 3D volumetric scattering-tomography of cloud micro-physics. The micro-physical properties retrieved are the liquid water content and effective radius within a cloud. New adjustments include an advanced perspective polarization imager model, and the assumption of 3D variation of the effective radius. Under these assumptions, we advanced the retrieval to yield results that (compared to the simulated ground-truth) have smaller errors than the prior art. Elements of our advancement include initialization by a parametric horizontally-uniform micro-physical model. The parameters of this initialization are determined by a grid search of the cost function. Furthermore, we added viewpoints corresponding to single-scattering angles, where polarization yields enhanced sensitivity to the droplet micro-physics (i.e., the cloudbow region). In addition, we introduce an optional adjustment, in which optimization of the liquid water content and effective radius are separated to alternating periods. The suggested initialization model and additional advances have been evaluated by retrieval of a set of large-eddy simulation clouds.

Significant climate uncertainties are associated with insufficient understanding of small warm clouds, due to the nature of their 3D structure and radiative transfer. It is desirable to improve understanding of such clouds and their sensitivity to environmental changes. This requires sensing platforms that are suitable for 3D sensing, and signal analysis tuned to 3D radiative transfer. We approach these challenges in the CloudCT project, funded by the ERC. It is a mission that develops and aims to demonstrate 3D volumetric scattering tomography of clouds. This will be facilitated by an unprecedented large formation of ten cooperating nanosatellites. The formation will simultaneously image cloud fields from multiple directions, at approximately 20m nadir ground resolution. Based on this data, scattering tomography will seek the 3D volumetric distribution of droplet effective radius, liquid water content and optical extinction. In addition to advancement of the technology, CloudCT will yield a global database of 3D macro and microphysical properties of warm cloud fields.In this talk, we present advances made on several fronts of the project: modeling, payload, algorithm, and operation. Regarding cloud modeling, we performed LES simulations (using the SAM model with bin microphysics) of warm convective cloud fields (at different environments), at high spatial resolution. Using the simulated clouds properties, several imager and waveband possibilities have been quantitatively considered for the mission. Major consideration criteria are tomographic quality in the face of sensor and photon noise, calibration errors and stray light. Additional criteria are technological availability, platform constraints, calibration requirements and cost.We investigated specifically possibilities of visible light (VIS, 463nm, 545nm, 645nm, and 705nm) short wave infra-red (SWIR, 1641 nm), and polarized imagers (POL, 463nm, 545nm, 645nm, and 705nm). These examinations relied on physical modeling of 3D radiative transfer and the sensing processes. Due to platform constraints in CloudCT, each platform will carry a single camera exclusively (either VIS/NIR or SWIR). Hence, we describe the tradeoff of introducing SWIR cameras and various POL architectures.While CloudCT is mainly designed for simultaneous imaging of each cloud field, it is possible to tolerate a lag of several seconds, as small warm clouds hardly evolve in this time scale (at the 20 meter spatial scale). We exploit this, to add more view-points, using the same number of platforms (10). The added viewpoints correspond to single-scattering angles, where polarization yields enhanced sensitivity to the droplet microphysics. These angles require sampling of <1° in the fogbow region. This dictates requirements for the platform attitude control.On the algorithmic front, we advanced the retrieval to yield results that (compared to the simulated ground truth) have smaller errors than the prior art. Elements of our advancement include initialization by a parametric horizontally-uniform microphysical model. The parameters of this initialization are determined by a fast optimization process. The optimized initialization is particularly strong, when relying on the detected degree of linear polarization, instead of radiance.

The CloudCT project is a mission that aims to demonstrate 3D volumetric scattering tomography of clouds. A formation of ten nanosatellites will simultaneously image cloud fields from multiple directions, at ≈20m nadir ground resolution. Based on this data, scattering tomography will seek the 3D volumetric distribution of cloud properties. We quantitatively compare visible polarized imagers to other imagers considered for the mission. We investigated specifically visible light and short-wave infra-red imagers. Each possibility was considered using Large Eddy Simulation clouds. Major consideration criteria are tomographic quality in the face of sensor and photon noise, calibration errors and stray light. We check the sensitivity to unknown stray light and uncertainty in gain calibration.