F. Chen et al., Sensitivity of orographic moist convection to landscape variability A study of the Buffalo Creek, Colorado, flash flood case of 1996, J ATMOS SCI, 58(21), 2001, pp. 3204-3223
A number of numerical experiments with a high-resolution mesoscale model we
re conducted to study the convective rainfall event that caused the 1996 Bu
ffalo Creek, Colorado, flash flood. Different surface conditions and treatm
ents of land surface physics were utilized to assess the sensitivity of thi
s orographic moist convection to local and regional landscape forcing.
Given accurate large-scale synoptic conditions at the lateral boundaries, t
he mesoscale model with a convection-resolving grid shows reasonably good s
kill in simulating this convective event with a lead time of up to 12 h. Se
nsitivity experiments show that a primary reason for this success is the us
e of an advanced land surface model that provides time-varying soil-moistur
e fields. This land surface model plays an important role in capturing the
complex interactions among the land surface, the PBL, cloud-modulated radia
tion, and precipitation. For the case simulated, such interactions contribu
te to the temporal and spatial distribution of surface heating at small sca
les, and the convective triggering and development.
Tests show that the landscape variability at small and large scales signifi
cantly affects the location and intensity of the moist convection. For exam
ple, on timescales of 6 to 12 h, differences in initial soil moisture assoc
iated with irrigation in the plains affect the evolution of the convection
near the Continental Divide. Also, the surface modification by a wildfire b
urn influences the path of the major convective event that caused the flash
flood.
A watershed-based quantitative-precipitation-forecast skill score is propos
ed and employed. The relative success with which this severe thunderstorm i
s simulated over complex terrain provides some hope that the careful treatm
ent of land surface physics in convection-resolving models can perhaps prov
ide some useful level of predictability.