TR32-Database: Database of Transregio 32

[616] - MRR noise processing

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Maahn, M., 2012. MRR noise processing. PhD Report, Institute for Geophysics and Meteorology, University of Cologne, Germany, Cologne, Germany. Accessed from at 2017-06-26.
Title(s):Main Title: MRR noise processing
Description(s):Abstract: Measuring solid precipitation is complex, because traditional gauges are highly biased, e.g by wind under-catch or blowing snow (Rasmussen et al., 2011). In addition, only few surface measurements are available in high latitudes where snow is the predominant type of precipitation (Ellis et al., 2009). Thus, observations of snow by remote sensing are needed to fill gaps in the observation of precipitation. Moreover, remote sensing instruments can also provide insights into the microphysical processes related to the formation of snowfall. Cloud and precipitation radars are increasingly used to study snow (Turk et al., 2011; Leinonen et al., 2012), especially from space using the satellite Cloudsat (Matrosov et al., 2008; Kulie and Bennartz, 2009) The Micro Rain Radar 2 (MRR) is a small vertically pointing precipitation K-band radar (Figure 1). The frequency modulated continuous wave (FM-CW) principle allows a very compact and power efficient design. Even though MRRs have been widely used to study liquid precipitation (Löffler-Mang et al., 1999; Peters et al., 2002, 2005) and the bright band (Kunhikrishnan et al., 2006; Cha et al., 2009), its potential for studying snow had not been analysed before the study of Kneifel et al. (2011). Although they found sufficient agreement between a MRR and a MIRA35 cloud radar for reflectivities exceeding 3 dBz, Kulie and Bennartz (2009) showed that approximately half of the snow events are occurring at reflectivities below 3 dBz, thus MRR has only limited use for studying snow climatologies. Kneifel et al. (2011) supposed that the decreasing performance of MRR below 3 dBz is most likely related to problems in the noise processing. This assumption, however, could not be proofed, because only noise-corrected data was avail able. In addition, MRR can be affected by aliasing effects due to turbulence as shown by Tridon et al. (2011). They, however, did not correct aliasing effects. This study presents a new data processing routine for MRR featuring an improved noise removal using non noise-corrected raw data. The routine includes also a dynamic method to dealiase the spectrum. The new routine provides reflectivity, Doppler velocity and spectral width, which are tested by comparison with a MIRA35 cloud radar at the Umweltforschungsstation Schneefernerhaus (UFS) close to the Zugspitze in the German Alps at an altitude of 1650m above sea level. From this comparison the suitability of MRR for observing snow is discussed and the question whether a MRR can be used to study snow climatologies is reevaluated.
Responsible Party
Creator(s):Author: Maximilian Maahn
Publisher:CRC/TR32 Database (TR32DB)
TR32 Topic:Remote Sensing
Subject(s):CRC/TR32 Keywords: PhD Report
File Details
File Name:Report2_Maahn_2012.pdf
Data Type:Text
File Size:3520 kB (3.437 MB)
Date(s):Available: 2012-04-01
Mime Type:application/pdf
Data Format:PDF
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:TR32DB Data policy agreement
North:-no map data
Measurement Region:Other
Measurement Location:--Other--
Specific Informations - Report
Report Date:1st of April, 2012
Report Type:PhD Report
Report City:Cologne, Germany
Report Institution:Institute for Geophysics and Meteorology, University of Cologne, Germany
Number Of Pages:22
Period of Pages:1 - 22
Further Informations:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Maximilian Maahn
Metadata Created:2013-12-16
Metadata Last Updated:2013-12-16
Funding Phase:2
Metadata Language:English
Metadata Version:V40
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