Exploratory Rainfall Simulation of 2004 Halloween Eve Flash Flood
2006-2007 Engagement Grant Report
Duane Stevens and Jax (Guangxia) Cao
Introduction
In the evening of 30 October 2004, a heavy thundershower on Oahu Island,
Hawai‘i induced a severe flash flood in the Manoa valley, causing a severe property loss.
The flood damaged and pile up many cars, ripped through houses and buildings, and
inundated the UH Hamilton Library ground floor, a loss of $50 millions occurred for the
UH alone. This commonly called Halloween Eve Flash flood, among many others, again
raises a key forecast question of how to correctly predict the abnormal rainfall for
property protection for both civilian and military. The best tool to handle the problem is
through numerical modeling. This project uses 2004 Halloween flash flood as the case,
to explore the issue of improving the simulation of heavy rainfall event. We use the
Weather Research and Forecast (WRF) model for the exploration.
The project has been conducted in a period of January – August 2007, with a
support from the 2006-2007 MHPCC/HPC engagement program, and has the following
objectives:
1) to test different physical options to identify the best approximation for the
Halloween even case,
2) to change the initial and run-time files through a pseudo 3DVAR and 4DVAR
approach to identify key initialization strategy for a better forecast.
Methods
WRF is a state-of-the-science model, jointly developed by the National Center for
Atmospheric Research (NCAR) with several federal agencies. This project uses three
nested domains, with the resolution of 25:5:1 km, to configure the WRF model. The
overall simulation strategy is shown in Table 1. The simulation is performed in MHPCC
JAWS, and runs over 32,000 hours.
Table 1. WRF model simulation strategy, 1. dry run, i.e. model run without soil moisture;
2. dry and nudging runs; 3. dry and the runs with the changed initial condition; 4. wet
runs, i.e. model run with soil moisture; 5 runs with changed initial condition, and the soil
moisture replaced with an one-month run results, and modified run-time model
conditions. MP = microphysics, and Cu = cumulus parameterization.
MP1 Kessler MP2 Lin et
MP5 Ferrier MP6 WSM6 MP8
al..
Thompson
Cu Kain1
1, 2, 3, 4, 5
1, 2, 3, 4, 5
1, 2, 3, 4, 5
1, 2, 3, 4, 5
Fritsch
Cu Bettsn/a
1, 2, 3, 4, 5
1, 2, 3, 4, 5
1, 2, 3, 4, 5
1, 2, 3, 4, 5
Miller-Janji
Cu Grelln/a
1, 2, 3, 4, 5
1, 2, 3, 4, 5
1, 2, 3, 4, 5
1, 2, 3, 4, 5
Devenyi
Results
Our simulation indicates that Lin et al. and Thompson microphysics and BettsMiller-Janjic cumulus paramterization performs better (Figure 1 and 2), in that more
rainfall is simulated over the Oahu Island.
The surface soil moisture affects the rainfall output in the model, and runs with
the soil moisture produce more precipitation than the runs without soil moisture (Figure 3
and 4). However, the nudging technique does not change the model simulation result.
As expected either changing the initial condition or modifying the soil moisture leads to
an improved rainfall simulation (Figure 5 and 6). Combining above techniques with the
modified run-time model environment enables us to have total rainfall simulation around
280 mm per day matches that of the observation 270 mm per day (Figure 7). However
there is still the problem of correctly simulating the location of the simulation. Our
results do suggest that in the future a combined idealized simulation is necessary to
generate guidelines for a further pseudo-4DVAR strategy.
Acknowledgement: This study is supported by the 2006-2007 MHPCC/HPC
Engagement grant.
Figure 1. Comparison of the simulation among four microphysics options, upper left: Lin
et al. scheme, upper right: Ferrier scheme, low left: WSM6 scheme, and low right:
Thompson scheme.
Figure 2. Comparison among three cumulus parameterization under Lin et al.
microphysics; upper left: Kain-Fritsch, upper right: Betts-Miller-Janjic, low left: GrellDevenyi ensemble, low right: Betts-Miller-Janjic but with Thompson microphysics.
Figure 3. Model simulation without soil moisture.
Figure 4. Model simulation with the soil moisture. Contour unit: mm.
Figure 5. The rainfall simulation with the soil moisture modified by a month-long
simulation output.
Figure 6. Model simulation with modified initial condition on wind and air moisture.
Contour unit: mm.
Figure 7. The total rainfall simulation after a period of 24 hours till 18 ZZ 31 October
2004. Unit: mm.