Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach. Issue 3 (11th February 2013)

• Record Type: Journal Article
• Authors: Liu, Guosheng; Seo, Eun‐Kyoung
• Is Part Of: Journal of geophysical research. Volume 118:Issue 3(2013:Mar.)
• Page Start: 1376
• Page End: 1387
• Publication Date: 2013-02-11
• Subjects: snowfall detection -- high‐frequency microwave -- CloudSat; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/jgrd.50172
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: [1] It has been long believed that the dominant microwave signature of snowfall over land is the brightness temperature decrease caused by ice scattering. However, our analysis of multiyear satellite data revealed that on most of occasions, brightness temperatures are rather higher under snowfall than nonsnowfall conditions, likely due to the emission by cloud liquid water. This brightness temperature increase masks the scattering signature and complicates the snowfall detection problem. In this study, we propose a statistical method for snowfall detection, which is developed by using CloudSat radar to train high‐frequency passive microwave observations. To capture the major variations of the brightness temperatures and reduce the dimensionality of independent variables, the detection algorithm is designed to use the information contained in the first three principal components resulted from Empirical Orthogonal Function (EOF) analysis, which capture ~99% of the total variances of brightness temperatures. Given a multichannel microwave observation, the algorithm first transforms the brightness temperature vector into EOF space and then retrieves a probability of snowfall by using the CloudSat radar‐trained look‐up table. Validation has been carried out by case studies and averaged horizontal snowfall fraction maps. The result indicated that the algorithm has clear skills in identifying snowfall areas even over mountainous regions. Key Points: Ice Scattering is not the dominant microwave signature in snow clouds A statistical algorithm is developed for snowfall detection Validation shows the algorithm has reasonable skill