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|Title:||Removal of nonprecipitation echoes in weather radar using multifractals and intensity||Authors:||Charalampidis, Dimitrios
Jones, Wilma L.
|Keywords:||Algorithms;Multifractals;Rain and rainfall;Remote sensing;Radar||Category:||Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Engineering and Technology||Issue Date:||7-Aug-2002||Publisher:||IEEE||Source:||IEEE Transactions on Geoscience and Remote Sensing, 2002, vol. 40, no. 5, pp. 1121-1131||Journal:||IEEE Transactions on Geoscience and Remote Sensing||Abstract:||In this paper, we present an algorithm for the automated removal of nonprecipitation related echoes such as atmospheric anomalous propagation (AP) in the lower elevations of meteorological-radar volume scans. The motivation for the development of this technique is the need for an objective quality control algorithm that minimizes human interaction. The algorithm uses both textural and intensity information obtained from the two lower-elevation reflectivity maps. The texture of the reflectivity maps is analyzed with the help of multifractals. Four multifractal exponents are computed for each pixel of the reflectivity maps and are compared to a "strict" and a "soft" threshold. Pixels with multifractal exponents larger than the strict threshold are marked as "nonrain" and pixels with exponents smaller than the soft threshold are marked as "rain." Pixels with all other exponent values are further examined using intensity information. We evaluate our QC procedure by comparison with the Tropical Rainfall Measurement Mission (TRMM) Ground Validation Project quality control algorithm that was developed by TRMM scientists. Comparisons are based on a number of selected cases where nonprecipitation and a variety of rain events are present, and results show that both algorithms are effective in eliminating nonprecipitation related echoes while maintaining the rain pixels. The principal advantage of our algorithm is that it is automated; therefore, it eases the requirements for the training for the QC analysis and it speeds the data reduction process by eliminating the need for labor-intensive human-interactive software.||URI:||http://ktisis.cut.ac.cy/handle/10488/7117||ISSN:||0196-2892||DOI:||10.1109/TGRS.2002.1010899||Rights:||© IEEE||Type:||Article|
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