Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach

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 capturẽ 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 lookup 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.Citation: Liu, G., and E.-K. Seo (2013), Detecting snowfall over land by satellite high-frequency microwave observations: The lack of scattering signature and a statistical approach,

CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities

“…This again confirms previous studies [19,20] showing TB and ∆TB enhancement at 150 GHz in presence of snowfall, while damping of the polarization signal was observed in the presence of supercooled water. This also confirms the finding by Liu and Seo [44] that describes an emission signal at high frequencies in the winter associated with snowfall in the presence of a frozen surface background (low moisture and cold conditions). This effect was not visible at 166 GHz for the 30 April 2014 case study previously described because of the high TPW.…”

“…The presence of supercooled water near cloud tops in these shallow clouds has been verified using the CloudSat CPR/CALIPSO DARDAR product as shown in Figure 2a (magenta color layers in the top panel). In the work by Liu and Seo [44], similar TB increases was noted due to cloud liquid water that often mask frozen hydrometeor scattering effects and further complicate high microwave frequency TB signatures associated with snowfall events.…”

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

“…These sensors have high frequency channels that are highly sensitive to snowfall due to the scattering by snowflakes of upwelling terrestrial and radiation, originating in the lower levels of the atmosphere [15][16][17][18]. In addition, passive microwave radiometers have a large swath and have been installed on many platforms over the last decades, ensuring a good global coverage with a fair spatial resolution and lengthy data records.…”

“…Therefore, several approaches have been proposed in recent years for detecting and retrieving snowfall using passive microwave radiometers, such as the Special Sensor Microwave-Humidity (SSM/T2) [19], Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounding (MHS) [5,16,[20][21][22], Advanced Technology Microwave Sounder (ATMS) [23,24], Special Sensor Microwave-Imager/Sounder (SSMIS) [25] and GMI [26] (Figure 1). In addition, to these passive microwave products, specifically dedicated to snowfall, some operational products provide estimations of precipitation, including snowfall at the global scale, combining observations of several radiometers.…”

Snowfall Detectability of Nasa's Cloudsat: The First Cross-Investigation of Its 2c-Snow-Profile Product and National Multi-Sensor Mosaic Qpe (Nmq) Snowfall Data

“…Many previous efforts have been put into the use of passive microwave sensors (e.g., AMSR-E, AMSU, and TMI) for detecting snowfall or snow cover and retrieving snow depth, snow water equivalent (SWE), or snowfall rate [8][9][10][11][12][13]. Active microwave sensors also have a great potential in measuring snow.…”