Ice cloud single-scattering property models with the full phase matrix at wavelengths from 0.2 to 100µm

Baum, Bryan A.; Yang, Ping; Heymsfield, Andrew J.; Bansemer, Aaron; Cole, B. H.; Merrelli, Aronne; Schmitt, Carl; Wang, Chenxi

doi:10.1016/j.jqsrt.2014.02.029

Cited by 166 publications

(178 citation statements)

References 44 publications

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“…where A and V are respectively the total projected area and volume of all ice particles per unit surface area in a given atmospheric column (Baum et al, 2014). COD is given by…”

Section: Classification and Parameterization Of Thin Ice Cloudsmentioning

confidence: 99%

“…The triangular symbols represent the integration of the absorption efficiency across the six bands of the CIMEL CE-312. These efficiencies were derived for the "severely roughened solid column"-type crystals of Yang et al (2013) and Baum et al (2014), available at http://www.ssec.wisc.edu/ice_models/polarization.html. et al (2014).…”

Section: Sensitivity Of Thermal Infrared Radiometry To Thin Ice Cloudsmentioning

confidence: 99%

“…These were obtained for 1000 appropriately normalized samples of a random number generator with a normal probability distribution, the mean and standard deviation of which was controlled by the six parameter values of Table 2 (COD, D eff , cloud base height, cloud thickness, column-integrated water vapor of the atmosphere, WVC, and particle shape). The particle shape parameter is based on three particle characterizations as defined by Baum et al (2014): severely roughened solid columns, a general habit mixture involving a set of nine habits and severely roughened aggregate of solid columns. The standard deviations in T b that result from the variation of the six parameters are computed relative to the reference case defined above (Table 2).…”

Section: Sensitivity Of Thermal Infrared Radiometry To Thin Ice Cloudsmentioning

confidence: 99%

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Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

et al. 2017

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Abstract. Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80 • N, 86 • W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookup table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation (R 2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21 µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.

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“…where A and V are respectively the total projected area and volume of all ice particles per unit surface area in a given atmospheric column (Baum et al, 2014). COD is given by…”

Section: Classification and Parameterization Of Thin Ice Cloudsmentioning

confidence: 99%

Section: Sensitivity Of Thermal Infrared Radiometry To Thin Ice Cloudsmentioning

confidence: 99%

Section: Sensitivity Of Thermal Infrared Radiometry To Thin Ice Cloudsmentioning

confidence: 99%

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Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

et al. 2017

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“…Ice crystals are found to vary within a virtually countless set of shapes, but, limited by the lack of constraints on the correct optical properties, very few studies have departed from the common practice of assuming spherical shapes for the snow grains, an approximation which can negatively impact the quality of the albedo products (Tedesco and Kokhanovsky, 2007;Aoki et al, 2000). Among the available optical models for non-spherical grains are for example those of Baum et al (2011Baum et al ( , 2014 and Yang et al (2013). Incidentally, Räisänen et al (2015) recently attempted to fit measured phase functions of blowing snow by using phase functions of several complex habits provided by Yang et al (2013) in addition to phase functions of other habits.…”

Section: Methodsmentioning

confidence: 99%

Photopolarimetric retrievals of snow properties

2015

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Abstract. Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

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“…The evolution of varying versions for different purposes and sensors made it desirable to harmonize the sensor dependent versions again. Necessary requirements like the introduction of a cloud droplet effective radius retrieval along with the optical depth estimation (Nakajima and King, 1990), the use of modern representations of ice cloud optical properties (Baum et al, 2014) and the requirement for more flexible cloud detection (Merchant et al, 2005;Holzer-Popp et al, 2013) consequently motivated the development of a new cloud detection and cloud retrieval scheme based on the physical principles of APOLLO.…”

Section: Introductionmentioning

confidence: 99%

APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

Klüser¹,

Killius²,

Gesell³

2015

Atmos. Meas. Tech.

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Abstract. The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensorspecific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

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Ice cloud single-scattering property models with the full phase matrix at wavelengths from 0.2 to 100µm

Cited by 166 publications

References 44 publications

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

Photopolarimetric retrievals of snow properties

APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

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