Spectral albedo of snow-covered first-year and multi-year sea ice during spring melt

Abreu, R.A. De; Barber, David G.; Misurak, Kevin; LeDrew, E.

doi:10.1017/s0260305500016037

Cited by 17 publications

(6 citation statements)

References 8 publications

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“…For seasonal young sea ice, its albedo is typically less than the multiyear sea ice during the melting season and pond evolution [54]. Considering the lack of representativeness of Greenland AWS measured albedo to seasonal sea ice, we also cited some sea-ice albedo measurements as complementary data, which cover the surface conditions such as young sea ice and thin melt ponds.…”

Section: Limited Validation Data On the Sea-ice Surfacementioning

confidence: 99%

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The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

Peng

et al. 2018

Remote Sensing

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Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration's (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and the limited sample size. The VSIA data will serve for weather prediction applications and climate model calibrations. Combined with the historical observations from MODIS, current S-NPP VIIRS, and NOAA-20 VIIRS observations, VSIA will dramatically contribute to providing high-accuracy routine sea-ice albedo products and irreplaceable records for monitoring the long-term sea-ice albedo for climate research. TOA: top-of-atmosphere; BSAs: black-sky albedos; WSA: white-sky albedo; 6S: Second Simulation of 205 the Satellite Signal in the Solar Spectrum.206 2.3. The operational VSIA algorithm 207The NDE albedo process consists of two components, as shown in Figure 2. The granule albedo, 208 i.e., the published VIIRS albedo product, is computed online from a combination of the directly 209 estimated albedo and a historical temporally filtered gap-free albedo; the historical albedo is 210 computed offline from the granule albedo of previous days. 211The instant retrieval of sea-ice albedo is calculated online from ...

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Section: Limited Validation Data On the Sea-ice Surfacementioning

confidence: 99%

“…For seasonal young sea ice, its albedo is typically less than the multiyear sea ice during the 550 melting season and pond evolution [54]. Considering the lack of representativeness of Greenland…”

mentioning

confidence: 99%

The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

Peng

et al. 2018

Remote Sensing

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“…Although many albedo studies have been carried out in the Arctic (Hanson, 1961; Chernigovski, 1966; Langleben, 1969,1971; Grenfell and Maykut, 1977; Grenfell and Perovich, 1984; De Abreu and others, 1995) and Antarctic (Liljequist, 1956; Hanson, 1960; Hoinkes, 1960; Weller, 1968; Kuhn and others, 1977; Carroll and Fitch, 1981; Yamanouchi, 1983; Allison and others, 1993; Grenfell and others, 1994), the data obtained are not sufficient for a global study. The processes affecting the snow surface albedo are still only qualitatively known, and the available albedo data on regional and temporal differences are too sparse to validate albedomodeling efforts (Fetterer and Untersteiner, 1998).…”

Section: Introductionmentioning

confidence: 99%

Measurement of all-wave and spectral albedos of snow-covered summer sea ice in the Ross Sea, Antarctica

Zhou

Morris

2001

Ann. Glaciol.

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ABSTRACT. All-wave albedo and spectral albedo data were collected over snowcovered pack-ice floes during summer 1999 (January and February) in the Ross Sea, Antarctica. Temporal variation of the all-wave albedo and spectral albedo was measured from the northern edge to the southern edge of the pack ice along three lines of longitude: 165³ W, 150³ W and 135³ W. Snow depth, snow-cover stratification, snow-temperature profiles, and grain-size and morphology were also documented. It was observed that daily-averaged albedos were 0.70^0.86 for cloudy conditions over the pack ice. Only two sets of daily-averaged albedo were collected for clear-sky conditions (0.788 and 0.825). Albedo was lower at the marginal edges of the pack ice than in the central pack ice. Albedo was higher over the southern part of the central pack ice than over the northern part. Clear-and cloudy-sky albedos measured on the same site indicate that the average increase in albedo due to clouds is 1.4% (maximum 4.0%). Spectral albedos of the packice floes were similar under clear-sky and cloudy conditions. Both are mainly controlled by the snow grain-size, especially in the top snow layer. All-wave albedos derived from measured visible and near-infrared spectral albedos, with extrapolation to ultraviolet and shortwave infrared regions for both clear-and cloudy-sky conditions, agreed well with all-wave albedos from direct measurements.

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“…De Abreu and others (1995), Allison and others (1993), and Perovich and others (1986) measured spectral albedo for various sea-ice-surface types, and found that individual features, such as melt ponds, serve to lower substantially the albedo from typical values relative to pure ice. To assess the surface albedo properly, determination of the areal aggregate of surface features on the ice pack is therefore required.…”

Section: Introductionmentioning

confidence: 99%

Determination of areal surface-feature coverage in the Beaufort Sea using aircraft video data

Tschudi

Maslanik

1997

Ann. Glaciol.

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The surface-energy budget of the Arctic Ocean depends on the distribution of various sea-ice features that form by both mechanical and thermodynamic processes. Melt ponds, new ice and open water greatly affect the determination of surface albedo. However, even basic measurements of some surface-feature characteristics, such as areal extent of melt ponds, remain rare.A method has been developed to assess the areal coverage of melt ponds, new ice and open water using video data from the Beaufort and Arctic Storms Experiment (BASE). A downward-looking video camera mounted on the underside of a Hercules C-130 aircraft provided clear images of the surface. Images acquired over multi-year ice on 21 September 1994 were analyzed using a spectral technique to determine the areal coverage of melt ponds, new ice and open water. Statistics from this analysis were then compared to previous field studies and to the Schramm and others (in press) sea-ice model.

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Spectral albedo of snow-covered first-year and multi-year sea ice during spring melt

Cited by 17 publications

References 8 publications

The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

Measurement of all-wave and spectral albedos of snow-covered summer sea ice in the Ross Sea, Antarctica

Determination of areal surface-feature coverage in the Beaufort Sea using aircraft video data

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