scholarly journals Changes in North American snowpacks for 1979-2007 detected from the snow water equivalent data of SMMR and SSM/I passive microwave and related climatic factors

2013 ◽  
Vol 118 (14) ◽  
pp. 7682-7697 ◽  
Author(s):  
Thian Yew Gan ◽  
Roger G. Barry ◽  
Mesgana Gizaw ◽  
Adam Gobena ◽  
Rajagopalan Balaji
Keyword(s):  
North American ◽  
Snow Water Equivalent ◽  
Climatic Factors ◽  
Passive Microwave ◽  
Snow Water

scholarly journals Time-series analysis of passive-microwave-derived central North American snow water equivalent imagery

2002 ◽  
Vol 34 ◽  
pp. 1-7 ◽  
Author(s):  
C. Derksen ◽  
A. Walker ◽  
E. LeDrew ◽  
B. Goodison
Keyword(s):  
Time Series ◽  
Land Cover ◽  
North American ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Weighted Average ◽  
Passive Microwave ◽  
Central Canada ◽  
Snow Water ◽  
Total Study Area

AbstractThe Meteorological Service of Canada has developed a series of operational snow water equivalent (SWE) retrieval algorithms for central Canada, based on the vertically polarized difference index for the 19 and 37 GHz channels of the Special Sensor Microwave/Imager (SSM/I). Separate algorithms derive SWE for open environments, deciduous, coniferous and sparse forest cover. A final SWE value represents the area-weighted average based on the proportional land cover within each pixel. In this study, 5 day averaged (pentad) passive-microwave-derived SWE imagery for the winter season (December–February) of 1994/95 is compared to in situ data from central Canada in order to assess algorithm performance. Investigation of regions with varying proportional land cover within the four algorithm classes shows that retrieved SWE remains within ±10–20mm of surface observations, independent of fractional within-pixel land cover. Following algorithm evaluation, ten winter seasons (1988/89 through 1997/98) of pentad central North American SWE imagery are subjected to a rotated principal-component analysis (PCA). Although there are no trends in total study-area SWE, the PCA results identify the interseasonal variability in the SWE accumulation and ablation centers of action through the SSM/I time series.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

2021 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Pinja Venalainen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Snow Density ◽  
Snow Season ◽  
Snow Water ◽  
Snow Mass

<p>The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.</p><p>Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m<sup>-3</sup> early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.</p>

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