Global identification of snowcover using SSM/I measurements

Grody, Norman C.; Basist, Alan

doi:10.1109/36.481908

Cited by 257 publications

(168 citation statements)

References 21 publications

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“…In contrast to visible and infrared, passive microwave does not depend on the presence of sunlight and thus provides an alternative at high latitudes; and, passive microwave is largely (but not completely) transmitted through non-precipitating clouds, offering the potential to estimate snow cover under many cloudy conditions that preclude visible and infrared observations. In practice, research using passive microwave exploits the fact that microwave scattering by ice crystals is frequency-dependent: higher frequencies within the microwave portion of the spectrum are scattered more efficiently than lower frequencies, enabling the use of two or more frequency bands to estimate SWE (Chang et al, 1987;Derksen et al, 2005b;Grody and Basist, 1996). Other methods have also been evaluated such as one based on the inversion of a snow emission model (e.g., Pulliainen and Hallikainen, 2001).…”

Section: Passive Microwavementioning

confidence: 99%

A review of global satellite-derived snow products

Frei

Tedesco

Lee

et al. 2012

Advances in Space Research

264

184

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Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation.Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow.

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Section: Passive Microwavementioning

confidence: 99%

A review of global satellite-derived snow products

Frei

Tedesco

Lee

et al. 2012

Advances in Space Research

264

184

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“…The visible and passive microwave data sets provide comparable estimates of hemispheric interannual variability and long-term trends, but the microwave product tends to underestimate SCA [Armstrong and Brodzik, 2001;Basist et al, 1996]. Passive microwave has been used to track SCA fluctuations [Chang et al, 1990;Grody and Basist, 1996;Sun et al, 1997], often in regional applications [Tait and Armstrong, 1996;Tait, 1998;Walker et al, 1995]. Passive microwave offers the additional possibility of obtaining spatially complete information on snow mass, in addition to SCA, but current algorithms tend to underestimate snow mass, and are not always transferable between different geographic regions [Armstrong and Brodzik, 2002].…”

Section: Sca and Swe Observationsmentioning

confidence: 99%

Improved simulations of snow extent in the second phase of the Atmospheric Model Intercomparison Project (AMIP‐2)

2003

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[1] Simulations of snow-covered area (SCA) over Northern Hemisphere lands by a suite of general circulation models (GCMs) are evaluated. Results from GCM experiments submitted by an international array of research groups participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2) are compared to a data set derived primarily from visible band satellite imagery provided by the United States National Oceanic and Atmospheric Administration. At continental to hemispheric scales we find improvements over AMIP-1 models, including the elimination of temporal and spatial biases in simulations of the seasonal cycle of SCA, as well as improved simulations of the magnitude of interannual variability. At regional spatial scales, while no consistent model biases are identified over North America, regions over Eurasia are identified where models consistently either underestimate or overestimate SCA at the southern boundary of the seasonal snowpack. The region of greatest model bias is eastern Asia. While SCA biases are associated with temperature and precipitation biases, over only one region do we find a relationship between the magnitudes of SCA biases and the magnitudes of temperature and/or precipitation biases.

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“…During times of snowmelt this can lead to apparent disappearance and reappearance of snow cover on successive satellite passes due to changing wetness conditions (Figure 6) NESDIS produces daily snow charts for the Northern Hemisphere from SSM/I, which are averaged into weekly snow products. A description of the algorithm is given by Grody and Basist [1996]. Snow cover is identified when the brightness temperature in the 19-GHz channel is higher than in the 37-GHz channel (or higher in the 22-GHz channel compared with the 85-GHz channel).…”

Section: Surface Temperaturementioning

confidence: 99%

Measuring snow and glacier ice properties from satellite

König¹,

Winther²,

Isaksson³

2001

Reviews of Geophysics

242

159

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Abstract. Satellite remote sensing is a convenient tool for studying snow and glacier ice, allowing us to conduct research over large and otherwise inaccessible areas. This paper reviews various methods for measuring snow and glacier ice properties with satellite remote sensing. These methods have been improving with the use of new satellite sensors, like the synthetic aperture radar (SAR) during the last decade, leading to the development of new and powerful methods, such as SAR interferometry for glacier velocity, digital elevation model generation of ice sheets, or snow cover mapping. Some methods still try to overcome the limitations of present sensors, but future satellites will have much increased capability, for example, the ability to measure the whole optical spectrum or SAR sensors with multiple polarization or frequencies. Among the methods presented are the satellite-derived determination of surface albedo, snow extent, snow volume, snow grain size, surface temperature, glacier facies, glacier velocities, glacier extent, and ice sheet topography. In this review, emphasis is put on the principles and theory of each satellite remote sensing method. An extensive list of references, with an emphasis on studies from the 1990s, allows the reader to delve into specific topics. INTRODUCTIONThere have been tremendous technological achievements in the twentieth century that enable scientists to undertake research at virtually every spot on Earth. In parallel, advances in space technology during the last decades have provided us with a rapidly increasing number of satellite platforms that can be used to study complex physical processes of the Earth-atmosphere system. The development within this field concerns not only the growing number of satellites but also the rapid progression of sensor capabilities. In the future a major challenge will be connected with combining various sources of information gathered from space, i.e., data assimilation, and to make use of this information in a systematic, repetitive manner to monitor temporal and spatial variability, for example, in climate-change research.In the field of glaciology, satellite remote sensing has proven to be a particularly useful tool because areas of interest are often inaccessible. Further, in many regions at high latitudes, like the Greenland and Antarctic ice sheets, it is only during parts of the year that effective ground-based research can be carried out due to the harsh climate environment and the lack of daylight. Satellite remote sensing often permits real-time, yearround, and long-term studies. Also, the large spatial coverage of satellite remotely sensed data enables monitoring and process studies over large areas. In this way, satellite data help in understanding processes and teleconnections on the regional, continental, or even global scale, for example, global satellite-derived maps of snow cover. Such products are particularly important because they assist interpretation and analysis concerning global change.Also, on smaller scales, sate...

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Global identification of snowcover using SSM/I measurements

Cited by 257 publications

References 21 publications

A review of global satellite-derived snow products

A review of global satellite-derived snow products

Improved simulations of snow extent in the second phase of the Atmospheric Model Intercomparison Project (AMIP‐2)

Measuring snow and glacier ice properties from satellite

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