32nd Conference on Broadcast Meteorology/31st Conference on Radar Meteorology/Fifth Conference on Coastal Atmospheric and Oceanic Prediction and Processes

Wednesday, 6 August 2003: 12:15 PM
Sequential intensity filtering technique (SIFT): filtering out noise to highlight the physical variability of drop size distribution
GyuWon Lee, J.S. Marshall Radar Observatory, McGill University, Montreal, QC, Canada; and I. Zawadzki
Poster PDF (872.0 kB)
Disdrometric measurements are affected by the spurious variability due to drop sorting, small sampling volume and instrumental noise. As a result, analysis methods such as least square regression to derive R-Z relationships or studies of drop size distributions can lead to erroneous conclusions. This paper explores the importance of this variability and develops a new filter, called here Sequential Intensity Filter (SIF) that minimizes the effect of the spurious variability on disdrometric data.

A simple correction for drop sorting in stratiform rain illustrates that it generates a significant amount of the spurious variability and is prominent in small drops. SIF filters out this spurious variability while maintaining the physical variability as evidenced by stable R-Z relationships independent of averaging size and by a drastic decrease of the scatter in R-Z plots. The presence of scatter causes various regression methods to yield different best-fitted R-Z equations depending on whether the errors on R or Z are minimized. The weighted total least square (WTLS) solves this problem by taking into account errors in both R and Z and provides the appropriate coefficient and exponent of Z=aR^b. For example, with a simple R vs. Z least-square regression, there is an average fractional difference in a and b of Z=aR^b of 17% and 14% respectively when compared to those derived using WTLS. With Z vs. R regression, an average fractional difference in a and b is 19% and 12%, respectively. This uncertainty in the R-Z parameters explains 40% of the "natural variability" claimed in the literature and becomes negligible after applying SIF, regardless of the regression methods used.

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