A Novel Parameterization of Snow Albedo Based on a Two-Layer Snow Model with a Mixture of Grain Habits

Saito, Masanori; Yang, Ping; Loeb, Norman G.; Kato, Seiji

doi:10.1175/jas-d-18-0308.1

Cited by 33 publications

(51 citation statements)

References 62 publications

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“…where r col VA and r agg VA are the snow grain radii for the Voronoi column and aggregate model, respectively. The mixing ratio f agg varies linearly as a function of logarithm of r VA between a and b, similar in concept to those given by Ishimoto et al (2018) and Saito et al (2019): Note.…”

Section: 1029/2019jd031858supporting

confidence: 57%

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

et al. 2020

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We proposed new snow grain model and snow impurity mixture models for the purpose of accurate retrievals of snow grain size and concentration of light‐absorbing particles (LAP) in snow from the optical remote sensing data. Two kinds of ice crystal models, irregularly shaped Voronoi columns, and Voronoi aggregates were employed. LAP can be captured by the snow through two processes: dry and wet deposition. Two different snow impurity mixture models were proposed. One is an external mixture model. We employed a coated sphere model in which soot were hydrophilic particles to represent a hygroscopic property of aerosol and assumed the hydrophilic soot particles to be externally mixed with snow particles. The other is an internal mixture model. We employed a dynamic effective medium approximation method in which soot particles were randomly located within snow particle with any size distribution and number concentration. Validation of these models is conducted using a ground‐based spectral radiometer system with in situ measurement data. For snow grain size retrievals, a Voronoi mixture model seamlessly representing a geometrical shape and an optical properties of various snow types can provide accurate retrievals. For the retrieval of LAP concentration in snow, employing different mixing state models depending on season and measurement site gives accurate results. We also discussed the uncertainty of retrieved snow parameters on the surface slope involved in the retrieval accuracy. These models are expected to be useful for advanced airborne/satellite remote sensing and climate studies via radiative transfer modeling in the atmosphere‐snow system.

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Section: 1029/2019jd031858supporting

confidence: 57%

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

et al. 2020

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“…The algorithm is not based on the look-up table approach and can be used with small modifications for other optical instruments orbiting our planet (MODIS, S-GLI, etc.). The algorithm may be biased at specific observation conditions (forward/backward scattering, high contamination of snow by pollutants, vertically inhomogeneous snow and sloping terrain) as discussed by [54,[62][63][64].…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

Kokhanovsky¹,

et al. 2019

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The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

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“…The algorithm is not based on the look-up table approach and can be used with small modifications for other optical instruments orbiting our planet (MODIS, S-GLI, etc.). The algorithm may be biased at special observation conditions (forward/backward scattering, high contamination of snow by pollutants, vertically inhomogeneous snow, and slopping terrain) as discussed by [54,[62][63][64].…”

Section: Discussionmentioning

confidence: 99%

Retrieval of snow properties from the Sentinel-3 Ocean and Land Colour Instrument

Kokhanovsky¹,

Lamare²,

Danne³

et al. 2019

Preprint

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The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing (http://step.esa.int/main/toolboxes/snap/). In this work we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020nm, we derive important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on the spatial grid of 300m. The algorithm also incorporates cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long – term snow property records essential for climate monitoring and data assimilation studies - especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

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A Novel Parameterization of Snow Albedo Based on a Two-Layer Snow Model with a Mixture of Grain Habits

Cited by 33 publications

References 62 publications

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

Retrieval of snow properties from the Sentinel-3 Ocean and Land Colour Instrument

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