Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

Grippa, Manuela; Mognard, N. M.; Toan, Thuy Le; Josberger, Edward G.

doi:10.1016/j.rse.2004.06.012

Cited by 57 publications

(47 citation statements)

References 21 publications

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“…global basis. As a consequence, snow depth retrieval will be very difficult to retrieve with any accuracy from passive microwave observations on a global basis, confirming previous studies (e.g., Kelly et al 2003;Grippa et al 2004). To partly alleviate these difficulties, a scheme has been developed to estimate the snow depth: it combines microwave emissivities, in situ measurements, and land surface models (Cordisco et al 2006).…”

Section: Sensitivity Of the Microwave Emissivities To The Pres-supporting

confidence: 53%

Land Surface Microwave Emissivities over the Globe for a Decade

Prigent¹,

Aires²,

Rossow³

2006

Bulletin of the American Meteorological Society

136

127

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Microwave land surface emissivities have been calculated over the globe for ~10 yr between 19 and 85 GHz at 53° incidence angle for both orthogonal polarizations, using satellite observations from the Special Sensor Microwave Imager (SSM/I). Ancillary data (IR satellite observations and meteorological reanalysis) help remove the contribution from the atmosphere, clouds, and rain from the measured satellite signal and separate surface temperature from emissivity variations. The method to calculate the emissivity is general and can be applied to other sensors. The monthly mean emissivities are available for the community, with a 0.25° × 0.25° spatial resolution. The emissivities are sensitive to variations of the vegetation density, the soil moisture, the presence of standing water at the surface, or the snow behavior, and can help characterize the land surface properties. These emissivities (not illustrated in this paper) also allow for improved atmospheric retrieval over land and can help evaluate land surface emissivity models at global scales.

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Section: Sensitivity Of the Microwave Emissivities To The Pres-supporting

confidence: 53%

Land Surface Microwave Emissivities over the Globe for a Decade

Prigent¹,

Aires²,

Rossow³

2006

Bulletin of the American Meteorological Society

136

127

View full text Add to dashboard Cite

show abstract

“…Many methods have been developed to deal directly or indirectly with metamorphism including empirical methods Grippa et al, 2004), statistical methods (Davis et al 1993;Tedesco et al 2003;Chang and Rango, 2000) and physical model-based methods (Pulliainen et al, 1999;Pulliainen, 2006;Guo et al, 2003). Figure 14 shows the snow depth retrieved in Siberia with metamorphism accounted for using an empirical approach (Grippa et al, 2004). Even under favorable conditions, such as over grassland, these methods are still insufficient for most applications but improvements can be expected as more prior information and physical constraints will be added in the retrieval process (see Sect.…”

Section: Application 1: Continental Snow Cover Extentmentioning

confidence: 99%

“…In passive mode, the V polarized waves emitted by the lowest layers are more transmitted than the H polarized ones and escape more easily from the medium. As a consequence, the difference between H Mean February Snow Depth (1989 in Siberia retrieved using passive microwave data (Grippa et al, 2004). The algorithm uses the difference between the brightness temperatures at 19 and 37 GHz.…”

Section: Application 3: Net Accumulationmentioning

confidence: 99%

Snow physics as relevant to snow photochemistry

et al. 2008

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Abstract. Snow on the ground is a complex multiphase photochemical reactor that dramatically modifies the chemical composition of the overlying atmosphere. A quantitative description of the emissions of reactive gases by snow requires knowledge of snow physical properties. This overview details our current understanding of how those physical properties relevant to snow photochemistry vary during snow metamorphism. Properties discussed are density, specific surface area, thermal conductivity, permeability, gas diffusivity and optical properties. Inasmuch as possible, equations to parameterize these properties as functions of climatic variables are proposed, based on field measurements, laboratory experiments and theory. The potential of remote sensing methods to obtain information on some snow physical variables such as grain size, liquid water content and snow depth are discussed. The possibilities for and difficulties of building a snow photochemistry model by adapting current snow physics models are explored. Elaborate snow physics models already exist, and including variables of particular interest to snow photochemistry such as light fluxes and specific surface area appears possible. On the other hand, understanding the nature and location of reactive molecules in snow seems to be the greatest difficulty modelers will have to face for lack of experimental data, and progress on this aspect will require the detailed study of natural snow samples.

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“…To increase the spatial coverage of snow depth, researchers have used different instruments (e.g., lidar, airborne laser scanning, and unmanned aerial systems) (Hopkinson et al, 2004;Grünewald and Lehning, 2013;Bühler et al, 2016) or developed and/or improved passive microwave snow algorithms (Foster et al, 1997;Derksen et al, 2003;Grippaa et al, 2004;Che et al, 2016). Although snow depth and SWE obtained from passive microwave satellite remote sensing could mitigate regional deficiency of in situ snow depth measurements, they have low spatial resolution (25 km × 25 km), and the accuracy is always affected by underlying surface conditions and algorithms.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

et al. 2018

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Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term ground-based measurements from 1814 stations. Spatially, long-term (1971Spatially, long-term ( -2000 mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade −1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50 • N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

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Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

Cited by 57 publications

References 21 publications

Land Surface Microwave Emissivities over the Globe for a Decade

Land Surface Microwave Emissivities over the Globe for a Decade

Snow physics as relevant to snow photochemistry

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

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