Cloud thermodynamical phase classification from the POLDER spaceborne instrument

Goloub, Philippe; Herman, M.; Chepfer, Hélène; Riédi, J.; Brogniez, Gérard; Couvert, P.; Sèze, Geneviève

doi:10.1029/1999jd901183

Cited by 107 publications

(101 citation statements)

References 47 publications

(31 reference statements)

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“…The multiple scattering of light within clouds dilutes the primary rainbow feature both in flux and in polarization. In Earth-observation, with a spatial resolution of a few kilometers, the detection of the polarized rainbow feature is used to discriminate between liquid water clouds and ice clouds (Goloub et al 2000). Light that is scattered by ice cloud particles, such as hexagonal crystals, does not show the rainbow feature.…”

Section: Discussionmentioning

confidence: 99%

“…Knowledge of the cloud properties (thermodynamical phase, optical thickness, microphysical properties etc.) are crucial for studies of global climate change on Earth (Goloub et al 2000).…”

Section: Discussionmentioning

confidence: 99%

“…As far as we know, the only polarization observations of the Earth have been done by the POLDER instrument (POLarization and Directionality of the Earth's Reflectances) a version of which is currently flying onboard the PARASOL satellite (for a description of the instrument, see Deschamps et al 1994). Locally, POLDER has indeed observed the primary rainbow above liquid water clouds (see Goloub et al 2000). However, since PARASOL is in a low-Earth-orbit, the POLDER measurements are not representative for polarization observations of the whole (disk-integrated) Earth observed from afar.…”

Section: The Influence Of the Ice Cloud Coveragementioning

confidence: 99%

“…Its existence, most pronounced in polarization observations, can be masked by the existence of ice clouds (Goloub et al 2000). The latter are often located above thick liquid water clouds and can dominate the appearance of the reflected polarization signal for ice cloud optical thicknesses larger than 2 (Goloub et al 2000).…”

Section: Introductionmentioning

confidence: 99%

“…The latter are often located above thick liquid water clouds and can dominate the appearance of the reflected polarization signal for ice cloud optical thicknesses larger than 2 (Goloub et al 2000).…”

Section: Introductionmentioning

confidence: 99%

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Looking for the rainbow on exoplanets covered by liquid and icy water clouds

Karalidi

Stam

Hovenier

2012

A&A

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Aims. Looking for the primary rainbow in starlight that is reflected by exoplanets appears to be a promising method to search for liquid water clouds in exoplanetary atmospheres. Ice water clouds, that consist of water crystals instead of water droplets, could potentially mask the rainbow feature in the planetary signal by covering liquid water clouds. Here, we investigate the strength of the rainbow feature for exoplanets that have liquid and icy water clouds in their atmosphere, and calculate the rainbow feature for a realistic cloud coverage of Earth. Methods. We calculate flux and polarization signals of starlight that is reflected by horizontally and vertically inhomogeneous Earth-like exoplanets, covered by patchy clouds consisting of liquid water droplets or water ice crystals. The planetary surfaces are black. Results. On a planet with a significant coverage of liquid water clouds only, the total flux signal shows a weak rainbow feature. Any coverage of the liquid water clouds by ice clouds, however, dampens the rainbow feature in the total flux, and thus the discovery of liquid water in the atmosphere. On the other hand, detecting the primary rainbow in the polarization signal of exoplanets appears to be a powerful tool for detecting liquid water in exoplanetary atmospheres, even when these clouds are partially covered by ice clouds. In particular, liquid water clouds covering as little as 10-20% of the planetary surface, with more than half of these covered by ice clouds, still create a polarized rainbow feature in the planetary signal. Indeed, calculations of flux and polarization signals of an exoplanet with a realistic Earth-like cloud coverage, show a strong polarized rainbow feature.

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Section: Discussionmentioning

confidence: 99%

“…Knowledge of the cloud properties (thermodynamical phase, optical thickness, microphysical properties etc.) are crucial for studies of global climate change on Earth (Goloub et al 2000).…”

Section: Discussionmentioning

confidence: 99%

Section: The Influence Of the Ice Cloud Coveragementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Looking for the rainbow on exoplanets covered by liquid and icy water clouds

Karalidi

Stam

Hovenier

2012

A&A

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Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO

et al. 2016

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We compare the cloud detection and cloud phase determination of three independent climatologies based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) to airborne in situ measurements. Our analysis of the cloud detection shows that the differences between the satellite and in situ measurements mainly arise from three factors. First, averaging CALIPSO Level l data along track before cloud detection increases the estimate of high-and low-level cloud fractions. Second, the vertical averaging of Level 1 data before cloud detection tends to artificially increase the cloud vertical extent. Third, the differences in classification of fully attenuated pixels among the CALIPSO climatologies lead to differences in the low-level Arctic cloud fractions. In another section, we compare the cloudy pixels detected by colocated in situ and satellite observations to study the cloud phase determination. At midlatitudes, retrievals of homogeneous high ice clouds by CALIPSO data sets are very robust (more than 94.6% of agreement with in situ). In the Arctic, where the cloud phase vertical variability is larger within a 480 m pixel, all climatologies show disagreements with the in situ measurements and CALIPSO-General Circulation Models-Oriented Cloud Product (GOCCP) report significant undefined-phase clouds, which likely correspond to mixed-phase clouds. In all CALIPSO products, the phase determination is dominated by the cloud top phase. Finally, we use global statistics to demonstrate that main differences between the CALIPSO cloud phase products stem from the cloud detection (horizontal averaging, fully attenuated pixels) rather than the cloud phase determination procedures. 100 km) and have been used recently to emphasize models' flaws [Cesana et al., 2015; Komurcu et al., 2014]. CESANA ET AL. CALIPSO CLOUD PHASE VALIDATION 5788

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Development of Algorithm for Discriminating Hydrometeor Particle Types With a Synergistic Use of CloudSat and CALIPSO

et al. 2017

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We developed a method for classifying hydrometeor particle types, including cloud and precipitation phase and ice crystal habit, by a synergistic use of CloudSat/Cloud Profiling Radar (CPR) and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud‐Aerosol LIdar with Orthogonal Polarization (CALIOP). We investigated how the cloud phase and ice crystal habit characterized by CALIOP globally relate with radar reflectivity and temperature. The global relationship thus identified was employed to develop an algorithm for hydrometeor type classification with CPR alone. The CPR‐based type classification was then combined with CALIPSO‐based type characterization to give CPR‐CALIOP synergy classification. A unique aspect of this algorithm is to exploit and combine the lidar's sensitivity to thin ice clouds and the radar's ability to penetrate light precipitation to offer more complete picture of vertically resolved hydrometeor type classification than has been provided by previous studies. Given the complementary nature of radar and lidar detections of hydrometeors, our algorithm delivers 13 hydrometeor types: warm water, supercooled water, randomly oriented ice crystal (3D‐ice), horizontally oriented plate (2D‐plate), 3D‐ice + 2D‐plate, liquid drizzle, mixed‐phase drizzle, rain, snow, mixed‐phase cloud, water + liquid drizzle, water + rain, and unknown. The global statistics of three‐dimensional occurrence frequency of each hydrometeor type revealed that 3D‐ice contributes the most to the total cloud occurrence frequency (53.8%), followed by supercooled water (14.3%), 2D‐plate (9.2%), rain (5.9%), warm water (5.7%), snow (4.8%), mixed‐phase drizzle (2.3%), and the remaining types (4.0%). This hydrometeor type classification provides observation‐based insight for climate model diagnostics in representation of cloud phase and their microphysical characteristics.

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Cloud thermodynamical phase classification from the POLDER spaceborne instrument

Cited by 107 publications

References 47 publications

Looking for the rainbow on exoplanets covered by liquid and icy water clouds

Looking for the rainbow on exoplanets covered by liquid and icy water clouds

Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO

Development of Algorithm for Discriminating Hydrometeor Particle Types With a Synergistic Use of CloudSat and CALIPSO

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