Auxiliary material for
Assessing WRF Model Parameter Sensitivity: A Case Study with 5-day Summer
Precipitation Forecasting in the Greater Beijing Area
Zhenhua Di1,2, Qingyun Duan1, Wei Gong1, Chen Wang1,3, Yanjun Gan1, Jiping Quan1,
Jianduo Li1, Aizhong Ye1, Chiyuan Miao1, Charles Tong3
(1. College of Global Change and Earth System Science, Beijing Normal University,
Beijing, 100875, China
2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and
Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing, 100029 China.
3. Lawrence Livermore National Laboratory, Livermore, CA 94550)
Geophysical Research Letters, 2014
Introduction
Twenty-three parameters from seven physical components of the WRF model, including
land surface, planetary boundary layer, surface layer, microphysics, cumulus clouds and
long-wave/short-wave radiation schemes, were chosen for sensitivity analysis to determine
their influence on summer precipitation forecasting in the Greater Beijing Area. Their
inclusion and their variability ranges are obtained based on the reading of literature and
computer codes, and consultation with the experts. The list of the twenty-three parameters is
given in the table “ts01.txt”.
The comparison of parameters sensitivities for storm event (e) using two different sets of
initial and boundary conditions is shown in Fig. S1. These two datasets are the NCEP
Reanalysis and CFSR datasets, respectively. The result shows the list of top ranked sensitive
parameters is consistent between the two datasets, even though the actual ranks do differ
somewhat.
Fig. S2 displays the ranks of sensitivity indices calculated using the MOAT method and
the traditional SA method. The two lists are generally consistent, even though there are some
differences in actual ranks. The differences are due to sampling errors. The MOAT is more
reliable because the traditional method is more susceptible to sampling error. Fig. S3 shows
the difference in mean daily rainfall simulation over the entire nine 5-day forecasting period
when selected parameters are perturbed from their lower bounds to upper bounds. Fig. S3a-h
shows the rainfall difference due to parameter perturbation for the top eight most sensitive
parameters based on the MOAT method. Fig. S3i-l shows the rainfall difference due to
parameter perturbation for other parameters that are regarded as less sensitive or non-sensitive
based on the MOAT method. The figure clearly shows that the more sensitive the parameters
are, the larger the difference in rainfall simulation due to parameter perturbation.
Explanation of Ts01.docx: Adjustable parameters and their variability ranges for the WRF
model.
Column 1 “index”: the indices of the twenty parameters
Column 2 “scheme”: the scheme associated with the parameters
Column 3 “parameter”: the original or redefined parameter name
Column 4 “default”: the default parameter value from the original WRF codes
Column 5 “range”: the variability range of the parameter
Column 6 “description”: the physical meaning of the parameter, including unit.
Fig. S1 Parameter sensitivities for the mixed storm event (i.e., Event (d)) obtained by
using the NCEP Reanalysis and the CFSR datasets to initialize the WRF model.
Fig.S2 The sensitivity ranks by the MOAT method and the traditional SA method: (a)
the sensitivity ranks of the MOAT analysis; (b) the sensitivity ranks of the traditional
analysis.
Fig. S3 The difference in mean daily rainfall simulation when a specific parameter is
perturbed from its lower bound to its upper bound with other parameters unchanged.