Remote Sensing of Crystal Shapes in Ice Clouds

Diedenhoven, Bastiaan van

doi:10.1007/978-3-319-70808-9_5

Cited by 14 publications

(13 citation statements)

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“…Shortwave reflectance measurements provide a robust means of inferring r e at cloud tops (Platnick et al, 2017) but rely on assumptions of ice scattering properties, most importantly assumed g (Holz et al, 2016). In situ and satellite measurements have suggested that rough or distorted compact crystals with smooth phase functions and visible asymmetry parameters near 0.75 dominate globally (Holz et al, 2016; Järvinen et al, 2018) (see van Diedenhoven, 2018, for an overview on ice shape remote sensing). However, the presented interquartile and full ranges of such measurements are large, indicating substantial variation of crystal shape, distortion, and scattering properties regionally and globally.…”

Section: Introductionmentioning

confidence: 99%

Global Statistics of Ice Microphysical and Optical Properties at Tops of Optically Thick Ice Clouds

et al. 2020

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The sizes and shapes of ice crystals in clouds affect fundamental microphysical processes, such as sedimentation and aggregation, as well as their optical properties. The evolution of ice crystal size and shape depends on temperature and supersaturation, as well as on other processes that may lead to various coexisting complex shapes. Here we present a global assessment of collocated size and shape characteristics and shortwave scattering properties of ice crystals at the tops of optically thick clouds inferred from space‐borne multiwavelength reflectance measurements and multiangle polarimetry. The results indicate systematic covariations of ice size, shape, and distortion, as well as variations with temperature that can be plausibly related to simplified ice crystal growth theory and in situ and laboratory data. This simplicity may be attributable to the temperature dependence of cloud top ice size and shapes commonly being dominated by vapor growth at conditions similar to those at cloud top. Such a conclusion may be viewed as somewhat surprising given the expectation that ice properties at cloud top will be an integral manifestation of processes occurring at lower levels within updrafts. We also find that, contrary to commonly used models, ice scattering asymmetry parameters decrease with increasing effective radius, reducing sensitivity of cloud reflectance to particle size.

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Section: Introductionmentioning

confidence: 99%

Global Statistics of Ice Microphysical and Optical Properties at Tops of Optically Thick Ice Clouds

et al. 2020

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“…In addition, different habits of ice crystals have varying single‐scattering characteristics and correspond to different microphysical properties and radiative properties (Letu et al, , ). Moreover, from the perspective of radiative transfer simulations and remote sensing involved in the general circulation models, ice clouds previously could not be reliably represented because the current knowledge on the natural variation of ice crystal shapes is still incomplete (Diedenhoven, ). Thus, the classification of ice crystal habits can promote the development of ice cloud remote sensing and radiative transfer simulations.…”

Section: Introductionmentioning

confidence: 99%

Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning

Xiao

Zhang

et al. 2019

Earth and Space Science

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Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single‐scattering properties of ice crystal particles. The single‐scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked‐eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7,000 images. We propose an automatic classification model of ice crystal habits, called TL‐ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL‐ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high‐precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds.

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“…Water cloud retrievals include cloud optical depth and standard bi-spectral droplet size estimates (Platnick et al, 2016), as well as both parametric (Alexandrov et al, 2012b) and nonparametric (Alexandrov et al, 2012a) droplet size distribution estimates that use polarization (Alexandrov et al, 2018). Most clouds observed during ACEPOL were low-level water clouds, but when ice clouds were detected, cloud optical depth, particle size, and particle shape/roughness retrievals (Van Diedenhoven et al, 2012, 2013Van Diedenhoven, 2018) were retrieved. Level 2 processing for aerosol retrievals uses the Microphysical Aerosol Properties from Polarimetry (MAPP) algorithm .…”

Section: The Research Scanning Polarimeter (Rsp)mentioning

confidence: 99%

The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) airborne field campaign

Knobelspiesse¹,

Barbosa²,

Bradley³

et al. 2020

Earth Syst. Sci. Data

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Abstract. In the fall of 2017, an airborne field campaign was conducted from the NASA Armstrong Flight Research Center in Palmdale, California, to advance the remote sensing of aerosols and clouds with multi-angle polarimeters (MAP) and lidars. The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign was jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON). Six instruments were deployed on the ER-2 high-altitude aircraft. Four were MAPs: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary EXploration (SPEX airborne), and the Research Scanning Polarimeter (RSP). The remainder were lidars, including the Cloud Physics Lidar (CPL) and the High Spectral Resolution Lidar 2 (HSRL-2). The southern California base of ACEPOL enabled observation of a wide variety of scene types, including urban, desert, forest, coastal ocean, and agricultural areas, with clear, cloudy, polluted, and pristine atmospheric conditions. Flights were performed in coordination with satellite overpasses and ground-based observations, including the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI), sun photometers, and a surface reflectance spectrometer. ACEPOL is a resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. Data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion. They are freely available to the public. The DOI for the primary database is https://doi.org/10.5067/SUBORBITAL/ACEPOL2017/DATA001 (ACEPOL Science Team, 2017), while for AirMSPI it is https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/ELLIPSOID_V006 and https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/TERRAIN_V006 (ACEPOL AirMSPI 75 Science Team, 2017a, b). GroundMSPI data are at https://doi.org/10.5067/GROUND/GROUNDMSPI/ACEPOL/RADIANCE_v009 (GroundMSPI Science Team, 2017). Table 3 lists further details of these archives. This paper describes ACEPOL for potential data users and also provides an outline of requirements for future field missions with similar objectives.

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Remote Sensing of Crystal Shapes in Ice Clouds

Cited by 14 publications

References 200 publications

Global Statistics of Ice Microphysical and Optical Properties at Tops of Optically Thick Ice Clouds

Global Statistics of Ice Microphysical and Optical Properties at Tops of Optically Thick Ice Clouds

Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning

The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) airborne field campaign

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