200
Chapter 11: Stochastic Modeling oE Precipitation
Figure 11.2: Typical variations in the spatial correlation with time period,
and by season, which appropriate to much oE North America and northern
Eurasia.
1.0
p(x)
1.0
p(x)
... ...
\ ...
\
......
\
...
...
... ...
...
..
tJ. ••
...
.. .. "VII}
....... 81 ....... !el'
'.!I-!Q
...
.... 1!~1' . . . . ..
x
. .
x
.......
.. ..
......... ......... ... ....
increases with the accumulation interval, and is longer in winter, when frontal
storms of large spatial extent dominate, than in summer. The zero-intercept
for the spatial correlation is less than one due to measurement error. An
extensive literature exists that deals with precipitation of spatial correlation,
the random component of the measurement error tends to average out at long
time scales, resulting in a zero intercept doser to one. However, it should
be emphasized that any bias present in the measurements is passed through
from the short to the long time scales.
Chapter 11: Stochastic Modeling oE Precipitation
Figure 11.2: Typical variations in the spatial correlation with time period,
and by season, which appropriate to much oE North America and northern
Eurasia.
1.0
p(x)
1.0
p(x)
... ...
\ ...
\
......
\
...
...
... ...
...
..
tJ. ••
...
.. .. "VII}
....... 81 ....... !el'
'.!I-!Q
...
.... 1!~1' . . . . ..
x
. .
x
.......
.. ..
......... ......... ... ....
increases with the accumulation interval, and is longer in winter, when frontal
storms of large spatial extent dominate, than in summer. The zero-intercept
for the spatial correlation is less than one due to measurement error. An
extensive literature exists that deals with precipitation of spatial correlation,
the random component of the measurement error tends to average out at long
time scales, resulting in a zero intercept doser to one. However, it should
be emphasized that any bias present in the measurements is passed through
from the short to the long time scales.
