Special Issue "Remote Sensing of Cloud and Aerosol Properties in a Three-Dimensional Atmosphere"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Zhibo Zhang
Website
Guest Editor
University of Maryland, Baltimore County, USA
Interests: Remote sensing of cloud and aerosol; radiative transfer; aerosol-cloud-radiation interaction; climate modeling and evaluation
Dr. Tamas Várnai
Website
Guest Editor
University of Maryland, Baltimore County, USA
Interests: 3-D radiative processes in the atmosphere; effects of scene heterogeneity in atmospheric remote sensing; aerosols in partly cloudy regions; multiview lidar measurements of thick clouds
Dr. Hironobu Iwabuchi

Guest Editor
Tohoku University, Japan
Interests: Atmospheric radiation; radiative transfer; remote sensing; cloud; 3D radiative effects
Prof. Bernhard Mayer
Website
Guest Editor
Meteorological Institute, Ludwig-Maximilians-University Munich, Theresienstr. 37, 80333 Munich, Germany

Special Issue Information

Dear Colleagues,

It is our pleasure to organize a Special Issue of “Remote Sensing of Cloud and Aerosol Properties in a Three-Dimensional Atmosphere” in the journal Remote Sensing.
Clouds and aerosols play a vital role in modulating the radiative energy budget of the Earth-atmosphere system. They can interact with each other, as well as many other components of the Earth system, in various ways. Remote sensing is an invaluable source of observation for cloud and aerosol studies. In the traditional paradigm, “all sky” is divided into the “cloudy-sky” and “clear-sky” first. Then, cloud remote sensing is done only for cloudy sky and aerosol remote sensing for clear-sky. Moreover, almost all current cloud and aerosol remote sensing algorithms are based on one-dimensional (1-D) radiative transfer theory, which assumes cloud and aerosol fields to be plane-parallel. Similarly, clouds and aerosols are treated as plane-parallel mediums in most numerical weather and climate models. More and more studies have indicated that this traditional paradigm may not be appropriate in many circumstances, because clouds and aerosols often co-exist with each other and can have significant three-dimensional (3-D) structures and variations at various scales. 
This Special Issue invites recent theoretical, observational and technological studies that attempt to advance the 3-D remote sensing of clouds and aerosols. Potential topics for this Special Issue include, but are not limited to the following:

  • The identification and reduction of the uncertainties and errors caused by 3-D radiative effects and unresolved small-scale horizontal variations in cloud and aerosol remote sensing, and in atmospheric correction for ocean color or other surface remote sensing.
  • Theoretical and/or numerical studies of how 3-D radiative effects of clouds and aerosols influence cloud dynamics, surface energy budget, and land-air interactions.
  • Remote sensing of aerosol properties in the vicinity of clouds.
  • Advanced theories and novel techniques (e.g., machine learning) to retrieve the 3-D structure of clouds and aerosols.
  • Sub-grid parameterization schemes to account for the impacts of small-scale cloud and aerosol variability on radiation simulations in global climate models.
  • Advances in 3-D radiative transfer theory and models.
Dr. Zhibo Zhang
Dr. Tamas Várnai
Dr. Hironobu Iwabuchi
Prof. Bernhard Mayer
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Dear Colleagues,

It is our pleasure to organize a Special Issue of “Remote Sensing of Cloud and Aerosol Properties in a Three-Dimensional Atmosphere” in the journal Remote Sensing.
Clouds and aerosols play a vital role in modulating the radiative energy budget of the Earth-atmosphere system. They can interact with each other, as well as many other components of the Earth system, in various ways. Remote sensing is an invaluable source of observation for cloud and aerosol studies. In the traditional paradigm, “all sky” is divided into the “cloudy-sky” and “clear-sky” first. Then, cloud remote sensing is done only for cloudy sky and aerosol remote sensing for clear-sky. Moreover, almost all current cloud and aerosol remote sensing algorithms are based on one-dimensional (1-D) radiative transfer theory, which assumes cloud and aerosol fields to be plane-parallel. Similarly, clouds and aerosols are treated as plane-parallel mediums in most numerical weather and climate models. More and more studies have indicated that this traditional paradigm may not be appropriate in many circumstances, because clouds and aerosols often co-exist with each other and can have significant three-dimensional (3-D) structures and variations at various scales. 
This Special Issue invites recent theoretical, observational and technological studies that attempt to advance the 3-D remote sensing of clouds and aerosols. Potential topics for this Special Issue include, but are not limited to the following:

  • The identification and reduction of the uncertainties and errors caused by 3-D radiative effects and unresolved small-scale horizontal variations in cloud and aerosol remote sensing, and in atmospheric correction for ocean color or other surface remote sensing.
  • Theoretical and/or numerical studies of how 3-D radiative effects of clouds and aerosols influence cloud dynamics, surface energy budget, and land-air interactions.
  • Remote sensing of aerosol properties in the vicinity of clouds.
  • Advanced theories and novel techniques (e.g., machine learning) to retrieve the 3-D structure of clouds and aerosols.
  • Sub-grid parameterization schemes to account for the impacts of small-scale cloud and aerosol variability on radiation simulations in global climate models.
  • Advances in 3-D radiative transfer theory and models.

Dr. Zhibo Zhang
Dr. Tamas Várnai
Dr. Hironobu Iwabuchi
Guest Editors

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Published Papers (3 papers)

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Research

Open AccessArticle
Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size
Remote Sens. 2020, 12(17), 2831; https://doi.org/10.3390/rs12172831 - 01 Sep 2020
Abstract
Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomography of cloud droplet distributions via passive remote sensing. [...] Read more.
Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomography of cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3D polarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developed convectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Current techniques are based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometry defaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud-droplets, changes its polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and glory angular regions can be used to infer the droplet size distribution. This work defines and derives a framework for a full 3D tomography of cloud droplets for both their mass concentration in space and their distribution across a range of sizes. This 3D retrieval of key microphysical properties is made tractable by our novel approach that involves a restructuring and differentiation of an open-source polarized 3D RT code to accommodate a special two-step optimization technique. Physically-realistic synthetic clouds are used to demonstrate the methodology with rigorous uncertainty quantification. Full article
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Open AccessArticle
Three Dimensional Radiative Effects in Passive Millimeter/Sub-Millimeter All-sky Observations
Remote Sens. 2020, 12(3), 531; https://doi.org/10.3390/rs12030531 - 06 Feb 2020
Cited by 1
Abstract
This study was conducted to quantify the errors prompted by neglecting three-dimensional (3D) effects, i.e., beam-filling and horizontal photon transport effects, at millimeter/sub-millimeter wavelengths. This paper gives an overview of the 3D effects that impact ice cloud retrievals of both current and proposed [...] Read more.
This study was conducted to quantify the errors prompted by neglecting three-dimensional (3D) effects, i.e., beam-filling and horizontal photon transport effects, at millimeter/sub-millimeter wavelengths. This paper gives an overview of the 3D effects that impact ice cloud retrievals of both current and proposed (Ice Cloud Imager) satellite instruments operating at frequencies of ≈186.3 and ≈668 GHz. The 3D synthetic scenes were generated from two-dimensional (2D) CloudSat (Cloud Satellite) observations over the tropics and mid-latitudes using a stochastic approach. By means of the Atmospheric Radiative Transfer Simulator (ARTS), three radiative transfer simulations were carried out: one 3D, one independent beam approximation (IBA), and one-dimensional (1D). The comparison between the 3D and IBA simulations revealed a small horizontal photon transport effect, with IBA simulations introducing mostly random errors and a slight overestimation (below 1 K). However, performing 1D radiative transfer simulations results in a significant beam-filling effect that increases primarily with frequency, and secondly, with footprint size. For a sensor footprint size of 15 km, the errors induced by neglecting domain heterogeneities yield root mean square errors of up to ≈4 K and ≈13 K at 186.3 GHz and 668 GHz, respectively. However, an instrument operating at the same frequencies, but with a much smaller footprint size, i.e., 6 km, is subject to smaller uncertainties, with a root mean square error of ≈2 K at 186.3 GHz and ≈7.1 K at 668 GHz. When designing future satellite instruments, this effect of footprint size on modeling uncertainties should be considered in the overall error budget. The smallest possible footprint size should be a priority for future sub-millimeter observations in light of these results. Full article
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Open AccessArticle
Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer
Remote Sens. 2019, 11(17), 1962; https://doi.org/10.3390/rs11171962 - 21 Aug 2019
Cited by 4
Abstract
Observation of the spatial distribution of cloud optical thickness (COT) is useful for the prediction and diagnosis of photovoltaic power generation. However, there is not a one-to-one relationship between transmitted radiance and COT (so-called COT ambiguity), and it is difficult to estimate COT [...] Read more.
Observation of the spatial distribution of cloud optical thickness (COT) is useful for the prediction and diagnosis of photovoltaic power generation. However, there is not a one-to-one relationship between transmitted radiance and COT (so-called COT ambiguity), and it is difficult to estimate COT because of three-dimensional (3D) radiative transfer effects. We propose a method to train a convolutional neural network (CNN) based on a 3D radiative transfer model, which enables the quick estimation of the slant-column COT (SCOT) distribution from the image of a ground-mounted radiometrically calibrated digital camera. The CNN retrieves the SCOT spatial distribution using spectral features and spatial contexts. An evaluation of the method using synthetic data shows a high accuracy with a mean absolute percentage error of 18% in the SCOT range of 1–100, greatly reducing the influence of the 3D radiative effect. As an initial analysis result, COT is estimated from a sky image taken by a digital camera, and a high correlation is shown with the effective COT estimated using a pyranometer. The discrepancy between the two is reasonable, considering the difference in the size of the field of view, the space–time averaging method, and the 3D radiative effect. Full article
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