ADEOS-II/GLI snow/ice products — Part II: Validation results using GLI and MODIS data

Aoki, Teruo; Hori, Masahiro; Motoyoshi, Hiroki; Tanikawa, Tomonori; Hachikubo, Akihiro; Sugiura, Komei; Yasunari, Teppei J.; Storvold, Rune; Eide, Hans; Knut, Stamnes; Li, Wei; Nieke, Jens; Nakajima, Yumiko; Takahashi, Fumihiro

doi:10.1016/j.rse.2007.02.035

Cited by 69 publications

(57 citation statements)

References 39 publications

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“…In addition, the snow grain size of the top 10 cm was measured manually after 1 July. In this snow grain size measurement process, we followed the definition of "r 2 " presented by Aoki et al (2000Aoki et al ( , 2003Aoki et al ( , 2007: Figure 4. Temporal evolution of observed main snow grain shape profile within the NSL, which was defined by a thick bottom ice layer, at SIGMA-A during the IOP.…”

Section: Snow Pit Measurementsmentioning

confidence: 99%

Numerical simulation of extreme snowmelt observed at the SIGMA-A site, northwest Greenland, during summer 2012

et al. 2015

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Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78 • 03 N, 67 • 38 W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements as well as numerical modeling with a one-dimensional multi-layered physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. Above all, the fact that the SMAP model successfully reproduced frequently observed rapid increases in snow albedo under cloudy conditions highlights the advantage of the physically based snow albedo model (PBSAM) incorporated in the SMAP model. Using such data and model, we estimated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

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Section: Snow Pit Measurementsmentioning

confidence: 99%

Numerical simulation of extreme snowmelt observed at the SIGMA-A site, northwest Greenland, during summer 2012

et al. 2015

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“…Therefore, it is of importance to understand in what way various snow pollutants (e.g., soot, dust, algae) influence snow albedo and, therefore, duration of snow cover. Corresponding studies have been performed by Warren and Wiscombe (1980), Painter et al (2001), Dozier and Painter (2004), Takeuchi et al (2006), Aoki et al (2007), Hadley and Kirchstetter (2012), and Warren (2013) among others. It was found that pollutants considerably modify snow spectral reflectance in the visible and near infrared (up to approximately the wavelength λ = 1 µm).…”

Section: Introductionmentioning

confidence: 91%

Spectral reflectance of solar light from dirty snow: a simple theoretical model and its validation

Kokhanovsky

2013

The Cryosphere

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Abstract.A simple analytical equation for the snow albedo as the function of snow grain size, soot concentration, and soot mass absorption coefficient is presented. This simple equation can be used in climate models to assess the influence of snow pollution on snow albedo. It is shown that the squared logarithm of the albedo (in the visible) is directly proportional to the soot concentration. A new method of the determination of the soot mass absorption coefficient in snow is proposed. The equations derived are applied to a dusty snow layer as well.

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“…Detailed comparison with field data obtained during campaigns carried out by Aoki et al (2007), where the micro-physical snow grain size was measured using a lens and a ruler, was performed as well (Wiebe at al., 2012). The SGSP-retrieved snow grain size complies well with the in-situ measured snow grain size.…”

Section: Retrieval Of Snow Grain Sizementioning

confidence: 99%

Remote sensing of sea ice: advances during the DAMOCLES project

et al. 2012

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Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMO-CLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer AS-CAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

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ADEOS-II/GLI snow/ice products — Part II: Validation results using GLI and MODIS data

Cited by 69 publications

References 39 publications

Numerical simulation of extreme snowmelt observed at the SIGMA-A site, northwest Greenland, during summer 2012

Numerical simulation of extreme snowmelt observed at the SIGMA-A site, northwest Greenland, during summer 2012

Spectral reflectance of solar light from dirty snow: a simple theoretical model and its validation

Remote sensing of sea ice: advances during the DAMOCLES project

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