Large-scale statistical study of Scanning Multichannel Microwave Radiometer (SMMR) data over Antarctica

Fily, Michel; Benoist, Jean-Pierre

doi:10.3189/s002214300004288x

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Microwave emissivity differences driven by the influence of snow accumulation rate may contribute significantly to spatial patterns in long term mean T b , making separation of accumulation and air temperature effects on T b difficult. However, these patterns are largely fixed in time [ Fily and Benoist , 1991], and can be removed by using anomalies. On an interannual timescale, accumulation is poorly correlated with T b , yet the high correlation of T b with air temperature is significant as shown by Bingham and Drinkwater [2000].…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal variability of Antarctic ice sheet microwave brightness temperatures

Schneider

Steig

2002

Geophysical Research Letters

View full text Add to dashboard Cite

Annual and interannual variability of 21 years of passive microwave brightness temperatures on the Antarctic ice sheet is documented through principal component analysis. The leading modes show that brightness temperatures are dominantly forced by the annual temperature cycle, with surface melting signals explaining about 5% of the total variance of the data. Analysis of the data with the annual cycle and melting signals removed results in two significant interannual modes. The first is consistent with the Antarctic Oscillation and its influence on Antarctic surface temperatures, while the second reflects regional scale variability that appears to be linked to sea‐ice anomalies and the El Niño‐Southern Oscillation. The expression of these characteristic patterns of Southern Hemisphere climate variability on the Antarctic continent provides a framework for interpreting modern climate records, as well as a basis for reconstructing past climate variability with ice cores.

show abstract

Section: Methodsmentioning

confidence: 99%

Spatial and temporal variability of Antarctic ice sheet microwave brightness temperatures

Schneider

Steig

2002

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…These sets of data are useful in obtaining the variation of the near surface characteristics over long distances. In other respects, recent studies have shown that the large-scale spatial distribution of microwave brightness temperatures did not change appreciably during the eight years of SMMR data (Fily and Benoist, 1991). Therefore it is likely worthwhile to compare recent satellite data with the earlier traverse data: this is the objective of the research work presented here.…”

Section: Introductionmentioning

confidence: 97%

Comparison of the passive microwave spectral signature of the Antarctic ice sheet with ground traverse data

Surdyk

Fily

1993

A. Glaciology.

Self Cite

View full text Add to dashboard Cite

ABSTRACT. Scanning Multichannel Microwave Radiometer (SMMR) data are compared with the snow characteristics measured during several traverses over Antarctica including average density over 0-2 m, 10 m temperature, grain-size distributions and number of strata. First a local study of specified areas and then a global study show some strong dependence between satellite data and ground data. The polarization ratios are affected by the stratification of snow (number and nature oflayers); large polarization ratios correspond to strong stratification, mainly for the lower frequencies. Bec'ause low density induces strong stratification there is also a dependence between polarization ratios and density even if the density is not the critical factor. The gradient ratios are linked to the grain-sizes as determined from detailed stratigraphies; the larger the grain-size, the smaller is the gradient ratio between 6.6 and 18 GHz.

show abstract

Numerical simulations of Greenland snowpack and comparison with passive microwave spectral signatures

2001

View full text Add to dashboard Cite

In this paper, we show that a detailed snow model (here, the Crocus model) may help to validate large-scale inferred meteorological datasets (e.g. from climate models or analyses) over the data-sparse ice sheets. Two series of snow simulations are carried out with two different meteorological datasets in input to the snow model. Both datasets are extracted from the European Center for Medium-range Weather Forecasts meteorological analyses and forecast archives. First, the microwave signatures of the surface of central Greenland from the Scanning Multichannel Microwave Radiometer (SMMR) are compared with the simulated density, grain-size and stratigraphy. The annual mean gradient ratio and polarization ratio, which are related to the emissivity of snow, are found to correlate spatially with these snow structural parameters. The sensitivity of the snow structure to differences in the two meteorological sets is then examined. It is found to be high for temperature and infrared radiation, precipitation and surface wind. The quantitative value of this result is limited by a still limited snow model validation over Greenland. Also, an optimal use of satellite data and a snow model for meteorological validation would require physically based translation of the simulated snow parameters into radiative properties, i. e radiation transfer modeling.

show abstract

Large-scale statistical study of Scanning Multichannel Microwave Radiometer (SMMR) data over Antarctica

Cited by 4 publications

References 32 publications

Spatial and temporal variability of Antarctic ice sheet microwave brightness temperatures

Spatial and temporal variability of Antarctic ice sheet microwave brightness temperatures

Comparison of the passive microwave spectral signature of the Antarctic ice sheet with ground traverse data

Numerical simulations of Greenland snowpack and comparison with passive microwave spectral signatures

Contact Info

Product

Resources

About