WMOD00563

• Title: Snow Accumulation Algorithm for the WSR-80D Radar: Final Report
• Date: 7/1/1998
• Weather Modification - Doc Type: Report

CONTENTS

FIGURES

Figure Page

Shielded gages in small clearings in a large conifer forest offer nearly ideal
conditions for measuring snowfall. This site is on the Grand Mesa in western
Colorado with a snowpack of perhaps eight feet .................................. 14

2 There are numerous concerns for measuring snowfall for comparison with
radar measurements summarized in these sketches ................................ 14

3 Sample vertical profiles of radar estimated SWE (snow water equivalent) for
four radars using J3 = 2.0 and the a values in parentheses for the Ze-S relation.
Linear regression lines are fit to the 5 data pairs for each case. Both a = 180
(dashed line) and a = 260 (solid line) were used with the same KCLE data. . . . . . . . . . . . . 18

4a The minimum normalized (by sample size) CTF indicates that J3 is
near 2.0 for two of three radars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4b Similar to figure 4a but for two additional radars ................................. 26

5 Values of a for individual gages, all for J3 = 2.0, show a linear decrease with
range for five different radars. Linear regression lines are shown for each

radar's data set ............................................................ 28

6a Snow water equivalents estimated with the SAA vs surface values estimated
from SD for the major Cleveland area lake effect storm of November 1996.
The value of a = 180 with J3 = 2.0 produces good agreement with 12 surface
snow depth measurements within 75 km range, using snow density = 0.06 g cm-3
to estimate snow water equivalent. A dashed linear regression line is fit to
the a = 180 data pairs. A similar line fit to the 12 data pairs (not plotted) for
a = 260 is well above the solid 1: 1 "perfect fit" line ............................... 31

6b Similar to figure 6a but using a density of 0.1 0 g cm-3 to estimate snow water
equivalent. Both dashed regression lines are well above the solid 1: 1 line. . . . . . . . . . . . . . 31

7 The ratio of radar to surface estimates of snow water equivalent decreased rapidly
with range during a major November 1996 lake effect snowstorm near Cleveland.
A linear regression line is fit to the 28 data points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

8 An example of the scatter in the relationship between radar estimates and surface
measurements of snow water equivalent for hourly data values. All 458 detectable
hourly accumulations from Denver surface observing sites 1, 2, 3, and 7 are plotted
for the entire 1995-96 winter. The dashed least squares regression line is associated
with an R value (correlation coefficient) of 0.70. Considerable over plotting of data
points occurs for the common small hourly surface accumulations . . . . . . . . . . . . . . . . . . . . 34

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