East Asia and Western Pacific METEOROLOGY AND CLIMATE

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of 29 %). The Ra/Rg and the relative dispersion associated with cold
fronts are close to those of the average for the 18 cases. The radar
also tends to underestimate more seriously surface rainfall during the
months of June to August (average Ra/Rg of .39) than during April and
May (average Ra/Rg of .60).
In all cases except 4 (marked with * in Table 1), there is no
significant difference between Ra/Rg derived from 3 km and 1km radar
estimates, confirming that the use of 3 krn CAPPI in general is as good
as 1 km CAPPI in estimating rainfall amount. In the 4 exceptional
cases, the 3 km Ra/Rg are 120%, 71%, 34% and 60% of the 1 km Ra/Rg for
the trough of 870406, cold front of 870412, Tropical Storm Ruth and
Typhoon Betty respectively. During the passage of a cold front and
during light rain conditions under the peripheral of a tropical
cyclone, 3 km radar data can substantially underestimate surface
rainfall. It may be worthwhile to use lower CAPPIs in such conditions.
Ra/Rg can vary considerably within a relatively short period of
time. Examples are the trough situations of 870405-07 and 870727-30.
The synoptic conditions in the respective periods remain more or less
unchanged. However, Ra/Rg changes significantly within the periods and
it is considered more appropriate to stratify the rain episodes into
three and two rain cases respectively.
CORRELAION OF RADAR AND RAINGAUGE ESTIMATES
The mean correlation coefficient (c) over available gauges for
each rain case is presented in Table 2. The average of c over all rain
cases is .46 with a mean relative dispersion of 55% for 3 km while
those for 1 km are .58 and 3.7% respectively. This shows that 1 km
CAPPI data are better correlated with surface rainfall than 3 km in
general.
The correlation between gauge and radar rainfall improves with
decreasing time resolution, when the time interval for data analysis
increases from 5 to 60 minutes, the spatial variability of c decreases
and the c increases for all rain cases which cover a sufficiently long
period of time (Figure 2). This is expected because coarser time
resolution tends to reduce gauge and radar sampling differences. For