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Sensitivity Studies of Convective Schemes and Model Resolutions in Simulations
of Wintertime Circulation and Precipitation over the Western Himalayas
P. SINHA,1 P. R. TIWARI,1 S. C. KAR,2 U. C. MOHANTY,1 P. V. S. RAJU,3 S. DEY,1 and M. S. SHEKHAR4
Abstract—The present study examines the performance of
convective parameterization schemes at two different horizontal
resolutions (90 and 30 km) in simulating winter (December–Feb-
ruary; DJF) circulation and associated precipitation over the
Western Himalayas using the regional climate model RegCM4.
The model integrations are carried out in a one-way nested mode
for three distinct precipitation years (excess, normal and deficit)
using four combinations of cumulus schemes. The National Center
for Environment Prediction—Department of Energy Reanalysis-2
project utilized gridded data, observed precipitation data from the
India Meteorological Department and station data from the Snow
and Avalanche Study Establishment were used to evaluate model
performance. The seasonal mean circulation patterns and precipi-
tation distribution are well demonstrated by all of the cumulus
convection schemes. However, model performance varies using
different schemes. Statistical analysis confirms that the root mean
square error is reduced by about 2–3 times and the correlation
coefficient (CC) increases in the fine resolution (30 km) simula-
tions compared to coarse resolution (90 km) simulations. A
statistically significant CC (at a 10 % significance level) is found
only in the fine resolution simulations. The Grell cumulus model
with a Fritsch–Chappell closure (Grell-FC) is better at simulating
seasonal mean patterns and inter-annual variability of two con-
trasting winter seasons than the other scheme combinations.
Key words: Western Himalayas, winter season, cumulus
schemes, resolution, RegCM4.
Abbreviations
DJF December, January and February
WD Western Disturbance
NCEP National Centre for Environment
Prediction
DOE Department of Energy
NCMRWF National Center for Medium Range
Weather Forecasting
IMD India Meteorological Department
SASE Snow and Avalanche Study
Establishment
NNRP2 NCEP-DOE reanalysis 2
FC Fritsch and Chappell closure
AS Arakawa and Schubert closure
WH Western Himalaya
IWH Indian parts of WH
GCM General circulation model
RCM Regional climate model
WJS Westerly jet stream
CC Anomaly correlation coefficient
RMSE Root mean square error
ETS Equitable threat score
PSE Phase synchronizing event
SL Significant level
1. Introduction
The maximum annual precipitation in the Western
Himalaya (hereafter referred to as WH) region occurs
during the winter season (December–February; DJF).
This is attributed to eastward-moving mid-latitude
synoptic weather systems called ‘‘western distur-
bance’’ passing over the region (WD; PISAROTY and
DESAI 1956; CHITLANGIA 1976; MOHANTY et al. 1998).
The frequency and amplitude of these westerly sys-
tems in a given month or season determines whether
the winter season will experience higher or lower
Electronic supplementary material The online version of this
article (doi:10.1007/s00024-014-0935-3) contains supplementary
material, which is available to authorized users.
1 Centre for Atmospheric Sciences, Indian Institute of
Technology, Delhi, Hauz Khas, New Delhi, India. E-mail:
ucmohanty@gmail.com2 National Centre for Medium Range Weather Forecasting,
Noida, India.3 Department of Meteorology, King Abdulaziz University,
Jeddah, Saudi Arabia.4 Snow and Avalanche Study Establishment, Chandigarh,
India.
Pure Appl. Geophys. 172 (2015), 503–530
� 2014 Springer Basel
DOI 10.1007/s00024-014-0935-3 Pure and Applied Geophysics
than normal precipitation (KAR and RANA 2013).
Winter precipitation over the WH region is important
for several reasons, such as agriculture (especially for
winter crops), glacial water supply to rivers
throughout the year, hydropower production, trans-
port, logistics, etc. Therefore, seasonal scale
prediction of winter precipitation levels is important
for policy planning and economic growth.
Coupled general circulation models (GCMs) or
atmosphere-only GCMs (AGCMs) are the most
important tools for generating monthly to seasonal
scale predictions. However, in general, the horizontal
resolutions of these GCMs are coarse
(*150–300 km) (WANG et al. 2009; BARNSTON et al.
2010; KAR et al. 2011) and insufficient for repre-
senting finer-scale physical processes and regional
land surface characteristics WANG et al. (2009)
assessed seasonal predictions of 14 climate models
for the period of 1981–2004 and showed that the
models poorly predict winter precipitation over the
Indian sub-continent. BARNSTON et al. (2010) exam-
ined the performance of several recent AGCMs for
11 years (1997–2008) and suggested that these
GCMs are not satisfactory in seasonal-scale simula-
tions. The National Centre for Medium Range
Weather Forecasting (NCMRWF) in India is one of
the leading organizations in generating real time
forecasting of the Indian region. KAR et al. (2011)
investigated the NCMRWF global circulation model
and found that the accuracy of the model is satis-
factory for short and medium-range forecasts during
the Indian monsoon season. However, the model
performance is poor for seasonal-scale simulation.
Earlier, GUPTA et al. (1999) conducted several
experiments using the NCMRWF global model to
simulate winter precipitation over the WH and sug-
gested that the heavy to very heavy precipitation
events are under-predicted by the model. TIWARY
et al. (2014) observed the varying capabilites of five
state-of-the-art GCMs in simulating the inter-annual
variability of wintertime precipitation over the WH.
Therefore, it is important to study the small-scale
physical processes that play important roles in mod-
ulating short-term climate over the WH region using
regional climate models (RCMs). The regional cli-
mate models (RCMs) are run at realtively higher
resolutions to represent sub-grid scale physical
processes at the regional scale. Therefore, these high-
resolution regional models are capabile of reproduc-
ing finer scale information better than the GCMs
(GIORGI et al. 2001; RUMMUKAINEN 2010).
The cumulus parameterization scheme is one the
important components in numerical models and plays
a major role in representing sub-grid scale convective
processes. Furthermore, the sensitivity of cumulus
parameterizations on the precipitation process varies
with the model horizontal resolution (GIORGI et al.
1996). A number of modeling studies (GIORGI and
SHIELDS 1999; LIANG et al. 2004; SINHA et al. 2013a)
suggested that cumulus schemes play a key role in
simulating the precipitation in an RCM. Several
studies (DASH et al. 2006; MUKHOPADHAYA et al. 2010;
SINHA et al. 2013a) confirmed that seasonal mean
patterns of upper air circulations and associated pre-
cipitation during the summer monsoon over the
Indian region are well represented in RCMs. How-
ever, RCM performance varies with different
schemes. So far the sensitivity of different RCM
cumulus parameterization schemes with various
horizontal resolutions at simulating winter precipita-
tion has not been reported for the WH region.
In the present study, several experiments were
conducted using the regional climate model RegCM4
to examine the sensitivity of different cumulus
parameterization schemes and model horizontal res-
olutions at simulating seasonal-scale winter
circulation and associated precipitation over the WH
region. Three distinct precipitation years, viz. excess,
normal and deficit, were considered for carrying out
the model integrations. Two domains of the model,
i.e., the outer domain at 90 km and the inner domain
at 30 km resolutions, are considered. For each year,
the sensitivity of the RegCM4 is examined with three
different cumulus and two different closure schemes
for both model domains. Section 2 describes the data
and methodology used in the present study, results
are discussed in Sect. 3 and main conclusions are
provided in Sect. 4.
2. Data and Methodology
The regional climate model (RegCM, version 4),
developed at the International Centre for Theoretical
504 P. Sinha et al. Pure Appl. Geophys.
Physics, Italy consists of a hydrostatic dynamic core
similar to the Mesoscale Model MM5 (GRELL et al.
1994). The model has 18 vertical sigma levels, in
which five levels are in the lower troposphere (GIORGI
et al. 1989; PAL et al. 2007) in its standard configu-
ration. Several previous experiments (DIMRI 2009,
SINHA et al. 2013a, b; DIMRI and NIYOGI 2013) suggest
that the RegCM model, with standard 18 vertical
levels, has able to simulate the mean features of the
Indian Summer Monsoon, as well as winter circula-
tions and associated precipitation over India. The
RegCM4 model has three convective parameteriza-
tion schemes: the modified Kuo scheme (ANTHES
1977); the Grell scheme (GRELL 1993); and the MIT-
Emanuel scheme (EMANUEL 1991; EMANUEL and
ZIVKOVIC-ROTHMAN 1999), to account for convective
precipitation. A brief description on the cumulus
schemes used in this study is presented below.
The modified Kuo scheme is one of the simple
cumulus schemes. It is assumed that the convective
activity originates in a column when the moisture
convergence (T) exceeds a given threshold and the
vertical sounding is simultaneously convectively
unstable. Moistening of the column is done by a
fraction of the moisture convergence a, and the rest is
converted into rainfall PCU according to the relation
given below:
PCU ¼ T 1� að Þ ð1Þ
a is a function of the average relative humidity RH of
the sounding as follows:
a ¼ 2 1� RH� �
RH� 0:51:0 otherwise
�ð2Þ
Advective tendencies for water vapor are included
in the moisture convergence term only. The vertical
distribution of latent heat is assumed to be conden-
sation between the cloud top and bottom. In this
scheme, the available buoyant energy is removed in
each time step to keep the vertical sounding stable.
In the Grell scheme, clouds are considered two
steady-state circulations, a downdraft and an updraft.
The cloudy air and the environment mix only at the
top and bottom of the cloud. No entrainment or
detrainment is allowed along the edges of the cloud
and the mass flux of the clouds does not vary with
height. Two closure assumptions, the ARAKAWA and
SCHUBERT (1974) closure assumption (hereafter
referred to as AS) and the FRITSCH and CHAPPELL
(1980) closure assumption (hereafter referred to as
FC), are adopted due to the simplistic nature of the
Grell scheme. The AS assumes the convective pro-
cesses stabilize the environment equal to the rate at
which large-scale (non-convective) processes desta-
bilize it. On the other hand, FC assumes that clouds
remove the available buoyant energy for convection
in a given timescale.
The MIT scheme is an idealized model of sub
cloud-scale downdrafts and updrafts with a buoyancy
sorting method (EMANUEL 1991; EMANUEL and ZIVKO-
VIC-ROTHMAN 1999). The main assumption in this
scheme is that the mixing in clouds is inhomogeneous
and highly episodic, rather than continuous as in the
Grell scheme. It is assumed that the air that is
entrained into the cloud from the environment forms a
spectrum of different mixing fractions, which then
reaches its level of neutral buoyancy either by
descending or ascending. Details of the other config-
urations including physical parameterization schemes
Table 1
Configuration of RegCM4 used in the present study
Dynamics Hydrostatics
Main prognostic
variables
u, v, t, q and p
Model domain 30�S–56�N; 28�E–128�E; Res. = 90 km
18�N–45�N; 60�E–95�E; Res. = 30 km
Map projection Lambert conformal mapping
Vertical co-ordinate Terrain-following sigma co-ordinate
Total of 18 sigma levels
Cumulus
parameterization
Kuo—Arakawa and Schubert (AS)
Grell—Arakawa and Schubert (Grell-
AS)
Grell—Fritch and Chappell clouser
(Grell-FC)
MIT—Fritch and Chappell clouser
(MIT-FC)
Radiation
parameterization
NCAR/CCM3 radiation scheme
PBL parameterization Holtslag
Lateral boundary
treatment
Exponential relaxation
Horizontal grid system Arakawa B
Large scale
precipitation scheme
Subgrid explicit moisture scheme
(SUBEX)
Time scheme Leapfrog scheme
Time step 225 s for 90 km and 100 s for 30 km
model resolution
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 505
can be found in PAL et al. 2007). The model config-
uration used in the present study is given in Table 1.
Each winter season is considered by taking into
account three-month periods starting December 1st of
that year to the end of February of the next year. In
order to identify distinct precipitation years, 14 years
(1995–2008) of gridded (1� 9 1�) precipitation data
for the Indian parts of the WH region was obtained
from the India Meteorological Department (IMD)
(RAJEEVAN et al. 2006) and analyzed. The Indian part
of the WH region (hereafter referred to as IWH)
contains the Jammu and Kashmir (J&K), the Hima-
chal Pradesh (HP) and the Uttarakhand (UK) regions.
Seasonal precipitation anomalies for the study period
are shown in Fig. 1. Excess (deficit) precipitation
years are those years during which seasonal precipi-
tation standard deviation from the mean of 14 years is
equal to ?1 (-1) or more. Analysis reveals that sea-
sonal average precipitation is higher than the mean by
about 60 % during the winter season of December
1997 to February 1998 (hereafter referred to as the
excess year), and less than the mean by about 48 %
during the winter season of December 2000 to Feb-
ruary 2001 (hereafter referred to as the deficit year). It
may be noted that if larger number of years are used to
identify distinct precipitation years, 1997–1998 and
2000–2001 still fall under the category of excess and
deficit years with more than a 40 % departure from the
mean precipitation. The observed station data
obtained from the Snow and Avalanche Study
Establishment (SASE) were used to validate the
model results at a station level for each of the 3 years.
In addition to two extreme (excess and deficit) pre-
cipitation years, one normal winter precipitation year
(December 2003–February 2004, hereafter referred to
as the normal year) is also considered for the study.
The model is integrated from November 1st to
February 28th (29th for a leap year) for each year and
for the two domains separately. The topography
(m) and the two domains at 90 and 30 km horizontal
resolutions are shown in Fig. 2. The model horizontal
resolutions are 90 km for the outer domain (28�E–
128�E/30�S–56�N) and 30 km for the inner domain
(60�E–95�E/18�N–45�N). The initial and lateral
boundary conditions for the outer domain are pro-
vided by the National Centre for Environment
Prediction (NCEP)—Department of Energy (DOE)
Reanalysis-2 data (hereafter referred to as NNRP2)
available at a 2.5� 9 2.5� resolution (KANAMITSU
et al. 2002). The output of RegCM4 simulation of the
outer domain is used as initial and lateral boundary
conditions for the inner domain. For both model
domains, the sea surface temperature (SST, at
1� 9 1� resolution) is provided from the National
Oceanic and Atmospheric Administration Optimal
Interpolation SST (version 2, NOAA_OISST_V2).
Other geophysical parameters obtained from the
United States of Geophysical Survey (USGS) at
10 min resolution are used as surface conditions to
the model. The simulations are carried out using four
combinations of the convective and closure schemes:
• Kuo cumulus scheme with Arakawa Schubert
closure (Kuo-AS).
• Grell cumulus scheme with Arakawa Schubert
closure (Grell-AS).
• Grell cumulus scheme with Fritch Chappell closure
(Grell-FC).
• MIT Emanuel cumulus scheme with Fritch Chap-
pell closure (MIT-FC).
Hereafter, RegCM4 simulations using the config-
urations stated above (from top to bottom) will be
referred as CSC1, CSC2, CSC3 and CSC4, respec-
tively, for the outer domain and FSC1, FSC2, FSC3 and
FSC4, respectively, for the inner domain. Experimen-
tal design details of the 90 and 30 km resolutions are
provided in Table 2. Model-simulated results are
Figure 1Interannual variability of area-averaged IMD gridded rainfall
anomalies for winter seasons (December–February; DJF) over
northwest India (1995–2008)
506 P. Sinha et al. Pure Appl. Geophys.
Figure 2Topography (in m) and size of the model domain with grid spacing of 90 and 30 km
Table 2
Experimental design of RegCM4 simulations with 90 and 30 km resolutions
Reanalysis RegCM4 model (90 km res.)
(Initial and Boundary Conditions)
RegCM4 model (30 km res.)
(Initial and Boundary Conditions)
NCEP-Department of Energy reanalysis 2
i) Kuo cumulus scheme with Arakawa Schubert closure (Kuo-AS)
(referred to as CSC1)
i) Kuo cumulus scheme with Arakawa Schubert closure
(referred to as FSC1)
ii) Grell cumulus with Arakawa Schubert closure (Grell-AS)
(referred to as CSC2)
ii) Grell cumulus with Arakawa Schubert closure
(referred to as FSC2)
iii) Grell cumulus scheme with Fritsch Chappell closure (Grell-FC)
(referred to as CSC3)
iii) Grell cumulus scheme with Fritsch Chappell closure
(referred to as FSC3)
iv) MIT-Emanuel cumulus scheme with Fritsch Chappell closure (MIT-FC)
(referred to as CSC4)
iv) MIT-Emanuel cumulus scheme with Fritsch Chappell closure
(referred to as FSC4)
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 507
validated with the NNRP2 reanalysis, and IMD grid-
ded and SASE station-level precipitation data sets.
3. Results and Discussion
The results obtained from the RegCM4 simula-
tions are analyzed in two broad sub-sections. In the
first sub-section, upper air circulation features are
described, while in the following sub-section, pre-
cipitation distribution and intensity are presented.
3.1. Circulation Features
The RegCM4 model (at 90 km resolution; here-
after referred to as coarse resolution) results were
Figure 3Seasonal (DJF) mean wind magnitude (in m/s) and vector at 500 hPa pressure level of a NNRP2, RegCM4 simulations at 90 km resolution
using b CSC1, c CSC2, d CSC3 and e CSC4 cumulus schemes for a normal precipitation year
508 P. Sinha et al. Pure Appl. Geophys.
analyzed to examine the upper air circulation pattern
for three different years.
The verification analysis (NNRP2) and model-
simulated wind fields at 500 hPa for the normal year
are shown in Fig. 3. The results show that strong
westerly winds that spread from Iran to the Himala-
yan region exist in NNRP2 and in all the model
simulations. It is seen that stronger westerly wind
([25 m/s) centered near 26�N/38�E persists in the
reanalysis during the normal year. All the cumulus
experiments are able to simulate westerly winds with
strengths more than 25 m/s that are centered almost
at the same location as in NNRP2. In the Indian
region, the westerly wind is strong over central India
in NNRP2. The location of strong westerly winds
over the central Indian region is represented better in
CSC3 than in other experiments. There is a shift of
about 108 towards the north in CSC1, CSC2 and
CSC4 experiments compared to the verification
analysis. A region of weak westerly wind (\10 m/s)
over the Himalayan region, as seen in NNRP2, is
simulated well by the model (but with a southward
shift) using all combinations of cumulus schemes.
Model-simulated wind fields at 500 hPa were also
examined for excess and deficit years with different
cumulus schemes (shown in Fig. 4). Results reveal
that the strength of westerly winds over Iraq, Iran and
some parts of Saudi Arabia are stronger during excess
year compared to normal and deficit years in both
NNRP2 and RegCM4 simulations. During both the
excess and deficit years, the patterns and magnitude
of the westerly winds are better represented in CSC3
than in other experiments of RegCM4 when com-
pared to NNRP2. During excess year, stronger
westerlies at upper-pressure levels could be caused
by frequent passages of intense WDs over this region
(PISAROTY and DESAI 1956).
Figure 5 represents the NNRP2 reanalysis and
model-simulated winds at 200 hPa for a normal year.
The verification analysis depicts a region of strong
westerly wind ([50 m/s) from Iran to the Himalayas
between 20�N and 32�N (Fig. 5a). The RegCM4
model is able to represent the strong westerly winds
with magnitudes of more than 50 m/s (except in
CSC1) in the same latitudinal belt as seen in NNRP2.
The strength of the westerlies is weaker over the
Himalayas in both CSC2 and CSC4 as compared to
other experiments. It is seen that the area with a
stronger westerly is represented better in the CSC3
experiment (Fig. 5d) when compared with the veri-
fication analysis. The core of the subtropical westerly
jet stream (WJS) is well demonstrated by the CSC2,
CSC3 and CSC4 experiments and is in agreement
with NNRP2. Furthermore, analysis of excess and
deficit years with respect to the normal year (shown
in Figure S1: provided in the supplementary material)
indicates that the strength of the westerly jet stream
from Iran to Himalaya is stronger during an excess
year as compared to normal and deficit years in both
NNRP2 and RegCM4 model simulations. The
strength and location of WJS in CSC3 are closer to
NNRP2 during excess and deficit years. Model-
simulated stronger westerlies from Iran to Himalaya
during an excess year are in good agreement with
previous observational studies by (SINGH et al. 1981).
The vertical structures of the seasonal mean zonal
and meridional wind have been examined for all
precipitation years. For this purpose, zonal and
meridional components of wind were averaged over
the longitudinal belt from 288E to 1288E. The
latitudinal cross section of the sectorial (28�E–
128�E) zonal wind for the verification analysis,
CSC1, CSC2, CSC3 and CSC4 RegCM4 experiments
are shown in Fig. 6 for the normal precipitation year.
The upper air WJS is well represented in all RegCM4
experiments; however, the area with a core WJS in
the CSC3 experiment is closer than other experiments
when compared to the verification analysis. It is noted
that the WJS is stronger in CSC2, while it is weaker
in the CSC1 and CSC4 experiments than in the
observed analysis. It is found that locations of the
core WJS in CSC2, CSC3 and CSC4 experiments are
closer to NNRP2, while it is slightly shifted north-
ward in the CSC1 experiment. The low-level weak
easterlies over the 5�N–10�N latitudinal belt are well-
simulated in all four experiments and are in good
agreement with NNRP2, except a northward shift by
about 58 in CSC4. Sectorial (averaged over 288E–
1288E) meridional wind is shown in Fig. 7. It reveals
that the meridional winds at upper-pressure levels
(200-100 hPa) are stronger in the model simulations
(except in CSC2) than NNRP2. The areas with
stronger meridional winds are shifted northward in
CSC1 and CSC4 experiments as compared to the
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 509
NNRP2. However, locations with stronger meridional
wind in CSC3 are closer to ones in the verification
analysis. The sectorial zonal and meridional wind for
excess and deficit years are shown in Fig. 8. The
upper air wind is stronger during an excess year than
in the normal and deficit years, both in the verifica-
tion and model simulations. The CSC2 and CSC3
experiments are able to represent the upper air wind
strength similar to NNRP2. The analysis of sectorial
meridional wind (shown in Figure S2: provided in the
supplementary material) illustrates that upper air
wind strength and patternss are represented better in
CSC3 than other cumulus schemes when compared to
the verification analysis for excess and deficit years.
Figure 4Seasonal (DJF) mean wind magnitude (in m/s) and vector at 500 hPa of a NNRP2, RegCM4 simulations at 90 km resolution using b CSC1,
c CSC2, d CSC3 and e CSC4 cumulus schemes for excess precipitation year; panels f–j are same as panels a–e, respectively, but for the
deficit precipitation year
510 P. Sinha et al. Pure Appl. Geophys.
Overall, the intensity, location and pattern of the
zonal, as well as meridional wind, are well repre-
sented in CSC3 experiments compared to NNRP2 for
all years.
Seasonally averaged (DJF) temperatures at 500
and 200 hPa obtained from NNRP2 and the model
simulations for all three years were studied. Average
seasonal temperatures for two contrasting years (for
excess and deficit year) are shown in Fig. 9 for
500 hPa and in Figure S3 of the supplementary
document for 200 hPa pressure levels. In the verifi-
cation analysis, during the excess and deficit years,
the atmosphere is colder over northern India and the
Himalayas than in the southern parts of India. The
temperature gradient persists from the south to the
north where the isotherm lines are oriented in nearly
east–west directions in the upper air over India and its
adjoining area. A south to north temperature gradient
Figure 5Seasonal (DJF) mean wind magnitude (in m/s) and vector at a 200 hPa pressure level of a NNRP2, RegCM4 simulations at 90 km resolution
using b CSC1, c CSC2, d CSC3 and e CSC4 cumulus schemes for a normal precipitation year
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 511
with isotherm lines almost in east–west directions are
well simulated in all the cumulus experiments of
RegCM4. However, the pattern and magnitude of the
upper air temperature and its gradient are brought out
well in CSC3 experiment. It is seen that the location
and areas of the lowest temperature are represented
better individually in the CSC3 experiment in excess,
normal and deficit winter years than in the verifica-
tion analysis.
A comparison of the upper air temperature for
excess and deficit years indicates the temperature
is cooler over the WH region during an excess
year than a deficit year in the NNRP2 and model
simulations. During an excess year, the WH
Figure 6Sectorial (288E–1288E) zonal seasonal mean wind (in m/s) of a NNRP2, RegCM4 simulations at 90 km resolution using b CSC1, c CSC2,
d CSC3 and e CSC4 cumulus schemes for a normal precipitation year
512 P. Sinha et al. Pure Appl. Geophys.
region is cooler probably due to the transport of
cold and dry air mass from the mid-latitudes. This
cold air mass interacts with warm and moist
tropical air resulting in excess precipitation over
this region. The magnitude and pattern of cold
temperature differences between excess and deficit
years over the WH region are represented better in
the CSC3 experiment than in the other three
experiments when compared with the verification
analysis.
Vertical velocity is one of the important param-
eters that play a major role in the model dynamics for
precipitation simulation. It would be useful to
examine the simulations of vertical velocity when
different cumulus schemes are used. For this purpose,
vertical pressure velocity (hereafter referred as
Figure 7Sectorial (288E–1288E) meridional seasonal mean wind (in m/s) of a NNRP2, RegCM4 simulations at 90 km resolution using b CSC1,
c CSC2, d CSC3 and e CSC4 cumulus schemes for a normal precipitation year
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 513
omega) at 500 hPa obtained from the NNRP2
reanalysis and RegCM4 model simulations at a
coarse resolution is analyzed and shown for the
normal year in Fig. 10. Omega is negative over the
WH during the winter season in the reanalysis. All
cumulus experiments in the RegCM4 model are able
to bring out this negative omega over the WH
regions, however, the area with a negative omega is
Figure 8Sectorial (288E–1288E) zonal seasonal mean wind (in m/s) of a NNRP2, RegCM4 simulations at 90 km resolution using b CSC1, c CSC2,
d CSC3 and e CSC4 cumulus schemes for an excess precipitation year; panels f–j are the same as panels a–e, respectively, except for the
deficit precipitation year
514 P. Sinha et al. Pure Appl. Geophys.
smaller in the model as compared to the reanalysis. It
is seen from the figure that a patch of positive omega
is present at the head of the J&K region in the
RegCM4 model simulations during normal year,
which is not seen in the reanalysis. This implies that
the convective activities over this positive omega
region are much less in the model compared to
NNRP2. Figure 10 indicates that areas over the WH
Figure 9Seasonal (DJF) mean temperature (K) at 500 hPa for excess and deficit precipitation years. Panel a was obtained from NNRP2, and RegCM4
simulations at 90 km resolution using b CSC1, c CSC2, d CSC3 and e CSC4 cumulus schemes; panels f–j are same as panels a–e,
respectively, except for the deficit precipitation year
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 515
region with stronger vertical velocities are well
represented in CSC3 and CSC4 experiments. The
area with a negative omega is smaller in the model
than the NNRP2 in all years. Analyses of omega for
excess and deficit years (shown in Figure S4:
provided in the supplementary material) indicate that
the negative omega is stronger in the model, as well
as in NNRP2, over the WH region during an excess
year. Comparison among the cumulus schemes shows
that the representation of negative omega in CSC3 is
closer to the NNRP2 during both the excess and
deficit years. However, a positive omega patch over
the head of the J&K region, as seen during a normal
year, is also present in all the cumulus experiments
Figure 10Seasonal (DJF) mean vertical pressure velocity (Omega; Pa/s) at 500 hPa for a normal precipitation year. Panel a obtained from NNRP2, and
panels b–e obtained from RegCM4 simulations at 90 km resolution using CSC1, CSC2, CSC3 and CSC4 cumulus schemes, respectively
516 P. Sinha et al. Pure Appl. Geophys.
during excess and deficit years. This is probably due
to improper representation of the orography over this
region in the model.
3.2. Sensitivity Study on Convective Schemes
to Model Resolution
The sensitivity of cumulus schemes to horizontal
resolutions (90 and 30 km) in simulating seasonal
precipitation is studied using RegCM4 model.
Numerical experiments using different cumulus
schemes namely Kuo-AS, Grell-AS, Grell-FC and
MIT-FC, were carried out with coarse resolution
model. These are referred to as CSC1, CSC2, CSC3
and CSC4, respectively, as mentioned earlier.
Another set of four experiments using the different
cumulus schemes, as mentioned above, was also
carried out with fine resolution model. These are
referred to as FSC1, FSC2, FSC3 and FSC4, as
indicated previously. The qualitative and quantitative
descriptions of the results are presented in three sub-
sections: (1) spatial distribution of precipitation, (2)
statistical analysis of model-simulated precipitation,
and (3) model precipitation validation at observed
locations.
3.2.1 Spatial Distribution of Precipitation
In order to understand the capabilities of the various
cumulus schemes in simulating winter precipitation
distribution and intensity over the IWH region,
seasonal (DJF) average precipitation for all three -
years were analyzed. The seasonal mean
precipitation for all cumulus experiments at coarse
and fine resolution simulations was analyzed for
each year. In the coarse resolution simulations
(shown in Figure S5: provided in the supplementary
material), the distribution and intensity of simulated
precipitation over the IWH region is well repre-
sented by the RegCM4 model for each year. During
an excess year, the precipitation intensity is under-
estimated in CSC1 and overestimated in the
remaining experiments (CSC2, CSC3 and CSC4)
as compared to the observed intensity. Model-
simulated precipitation intensity is less during
normal and deficit years in all the experiments,
except in CSC3 for a deficit year. However, the
pattern and intensity in CSC3 are closer to the
observations during normal and deficit years. The
model-simulated seasonal precipitation using a fine
resolution is shown in Fig. 11. It shows that all the
cumulus schemes can exhibit higher precipitation
during the excess year than the deficit and normal
years and agrees well with observations. The finer-
scale model overestimates the precipitation magni-
tude as compared to observations during the excess
year. The FSC1 underestimates precipitation during
normal and deficit years. However, the spatial
pattern and intensity of precipitation in FSC2 and
FSC3 experiments are closer to observations during
normal and deficit years.
The behavior of the model in simulating precip-
itation patterns in three distinct years were studied.
For this purpose, differences between excess and
normal, and deficit and normal years were computed
and presented in Figs. 12 and 13, respectively.
Observational analysis illustrates that major parts of
the IWH region received excess precipitation by
about 1–2 mm/day or more during the excess year as
compared to the normal year. It is also seen that the
zone of maximum precipitation is located over the
Jammu and Kashmir (J&K) region. All the cumulus
experiments, except for the CSC1 experiment, are
able to depict more rainfall over most parts of the
IWH region during the excess than in the normal
years. It is seen that the precipitation amount is less
over major parts of the IWH region in CSC1, which
does not agree well with the observed analysis.
Although, the magnitude of excess precipitation
(*1 mm/day) is less in CSC2, both CSC3 and
CSC4 are able to bring out excess precipitation by an
about 1–2 mm/day or more. The maximum precipi-
tation zone in CSC3 is almost located in a similar
position, as seen in the observations, but it is shifted
south-eastwards in CSC4. During the excess year, the
pattern, intensity, and location of higher precipitation
are closer to observations in CSC3 than in the other
experiments. It is clearly depicted in the observations
that the IWH region received less precipitation by an
about 1–2 mm/day during the deficit year as com-
pared to the normal year (Fig. 13). The area with the
minimum precipitation was confined over the east
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 517
part of the J&K region. The figure reveals that all the
experiments demonstrate less precipitation over the
IWH region during the deficit year than in the normal
years. However, the magnitude in precipitation
difference is higher in the observations than in the
model simulations, expect in CSC3. The precipitation
difference pattern and intensity are represented better
in CSC3 than in the other experiments.
Figure 11Seasonal (DJF) average precipitation (mm/day) for excess, deficit and normal precipitation years. Panel a obtained from IMD gridded
precipitation data, and RegCM4 simulations at 30 km resolution using b FSC1, c FSC2, d FSC3 and e FSC4 cumulus schemes; panels f–j are
same as panels a–e, respectively, except for the deficit precipitation year; panels k–o are same as panels a–e, respectively, except for the
normal precipitation year
518 P. Sinha et al. Pure Appl. Geophys.
Differences in precipitation between excess and
normal years and deficit and normal years obtained
via IMD analysis and fine resolution model simula-
tions are shown in Figs. 14 and 15, respectively. Over
many parts of the IWH, simulated precipitation in the
RegCM4 model at 30 km resolution is greater by
about 2–4 mm/day as compared to IMD observations
during the excess year. It is seen that the zone of
heavy precipitation is located in the J&K region in all
the experiments during the excess year. However,
precipitation patterns and intensity are better repre-
sented in the FSC2 experiment. During deficit and
Figure 12Average seasonal (DJF) precipitation difference (mm/day) between excess and normal precipitation years. Panel a obtained from IMD
gridded precipitation data, and panels b–e obtained from RegCM4 simulations at 90 km resolution using CSC1, CSC2, CSC3 and CSC4
cumulus schemes, respectively
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 519
normal years, precipitation intensity is less in all the
experiments over major parts of the IWH (except in
FSC3) when compared with IMD precipitation.
Analysis reveals that representation of both precip-
itation distribution and intensity in FSC3 are closer to
observations than in the other fine-resolution simu-
lations in each of the 3 years. Model-simulated
precipitation intensity is higher in fine resolutions
than in coarse resolutions in the corresponding year.
Although all the cumulus experiments modeled more
precipitation during the excess year than the normal
year, the magnitude is lower over the eastern parts of
the J&K region in each experiments as compared to
observations, except in FSC3. The variation in
Figure 13Average seasonal (DJF) precipitation difference (mm/day) between deficit and normal precipitation years. Panel a obtained from IMD gridded
precipitation data, and panels b–e obtained from RegCM4 simulations at 90 km resolution using CSC1, CSC2, CSC3 and CSC4 cumulus
schemes, respectively
520 P. Sinha et al. Pure Appl. Geophys.
precipitation intensity for excess-normal is repre-
sented better in FSC3 over all parts of the IWH
region. The difference of seasonal precipitation
between deficit and normal years (Fig. 15) shows
that all experiments modeled less precipitation over
the IWH region during the deficit year. However, the
magnitude of precipitation difference is higher over a
larger area in the FSC3 experiment.
Qualitative evaluation of precipitation distribution
and intensity indicates that, of all the coarse simu-
lations, performance is better in CSC3. Results of the
fine resolution RegCM4 experiments reveal that
Figure 14Average seasonal (DJF) precipitation difference (mm/day) between excess and normal precipitation years. Panel a obtained from IMD
gridded precipitation data, and panels b–e obtained from RegCM4 simulations at 30 km resolution using FSC1, FSC2, FSC3 and FSC4
cumulus schemes, respectively
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 521
FSC2 represents precipitation better during the excess
year, with the same being true for FSC3 during
normal and deficit years. However, the variations in
precipitation distribution and intensity in three dis-
tinct years are depicted better in FSC3.
The method for adjusting the available buoyant
energy in the model is different in the two closure
schemes, namely AS and FC. Since available buoyant
energy is removed in each time step in the AS scheme
and in a given timescale in FC scheme, the strength
of the convective activities in the AS closure is
weaker than in the FC closure. On the other hand, the
convective activities originated when moisture con-
vergence in a column exceeds a given threshold and
Figure 15Average seasonal (DJF) precipitation difference (mm/day) between deficit and normal precipitation years. Panel a obtained from IMD gridded
precipitation data, and panels b–e obtained from RegCM4 simulations at 30 km resolution using FSC1, FSC2, FSC3 and FSC4 cumulus
schemes, respectively
522 P. Sinha et al. Pure Appl. Geophys.
vertical soundings are convectively unstable in the
Kuo scheme, while the convection is triggered in the
Grell scheme when a parcel attains moist convection.
Therefore, the convection process in the Kuo scheme
is slower than in the Grell cumulus scheme. This may
be one of the reasons why more precipitation is
received when the Grell scheme is used rather than
the Kuo scheme or when the FC closure is used rather
than the AS closure. It is noted that in both coarse and
fine resolutions, the Grell-FC (CSC3 and FSC3)
simulates better precipitation distribution and varia-
tion in distinct years. This may be due to better
representation of moisture convergence and buoyant
energy release by clouds over the WH region in
Grell-FC scenario.
Further studies of convective heating rates have
been carried out to obtain a deeper insight into the
role of different cumulus schemes in simulating
winter precipitation. Convective heating rate is
calculated for the RegCM4 model at a 30-km
horizontal resolution with the four convective
schemes (FSC1, FSC2, FSC3 and FSC4) for all
three years. Vertical profiles of seasonal mean con-
vective heating rate obtained from the model
simulation, as well as from the verification analysis,
are shown in Fig. 16. During the excess year, the
convective heating rate is at its maximum (6.4 �C/
day) at 400 hPa in the NNRP2 reanalysis. The
convective heating maximum at 400 hPa is well
demonstrated in FSC2 and FSC4 with a heating rate
of 5.3 and 7.9 �C/day, respectively. FSC3 shows two
heating rate peaks with 3.0 �C/day at 470 hPa and
3.7 �C/day at 900 hPa. The maximum heating in
FSC1 is found at 900 hPa with 3.0 �C/day. The
vertical profile of convective heating in FSC2 is close
to NNRP2 during the excess year. During the deficit
year, the maximum convective activity is seen at
400 hPa in all experiments, except FSC1, in which
the maximum heating is found at 900 hPa. However,
the maximum heating rates at 400 hPa are 2.2, 1.4,
2.0 and 2.5 in �C/day for NNRP2, FSC2, FSC3 and
FSC4, respectively. It is noted that the convective
heating rate during the deficit year is comparatively
less than in the excess year in the model, which is in
agreement with the verification analysis.
During the normal year, the maximum heating
with a magnitude of 2.9 �C/day is also found at
400 hPa in the verification analysis. All the simula-
tions, except FSC1, are able to bring out heating
maxima at 400 hPa. The model-simulated heating
maxima is 2.2, 2.7 and 3.4 �C/day in the FSC2, FSC3
and FSC4 experiments, respectively. It is noted that
warming in the convective layer is higher in FSC4
and lower in the remaining experiments as compared
to verification in all the years. In the FSC1 experi-
ment, the heating is at its maximum at 900 hPa, and
decreases with height in all years. This indicates that
shallow clouds are more common at lower pressures
in the Kuo scheme, which, in turn, moistens and cools
the upper convective layer, leading to less precipita-
tion. Therefore, convective heating profiles indicate
that the precipitation intensity is simulated well in
FSC2 during the excess year, while the same is true
for FSC3 during the deficit and normal years. The
results suggest that the Grell scheme is suitable for
simulating winter precipitation over the Western
Himalaya.
3.2.2 Statistical Analysis of Model-Simulated
Precipitation
In this section, standard statistical techniques are used
to investigate the performance of different cumulus
schemes in simulating precipitation over the WH
region. Skill scores are computed by using model-
simulated and observed IMD gridded precipitation
over the IWH. The model-simulated precipitation is
bi-linearly interpolated to the observation grid points
before carrying out statistical analyses. The anomaly
correlation coefficient (CC), root mean square error
(RMSE), mean absolute error (MAE) and equitable
threat score (ETS) were computed for each experi-
ment. The Student’s t test is used for statistical
significant test of the CC; the critical value is 0.27 at
a 10 % significant level (SL). Tables 3 and 4 provide
the MAE, RMSE and spatial CC for coarse and fine
resolution model simulations, respectively.
Table 3 shows that the RMSE and MAE are less
during the deficit year and higher in the excess year in
coarse resolution simulations using RegCM4. The
RMSE and MAE values in the excess year are more
than twice of the RMSE and MAE in the deficit year
for all experiments. The coarse resolution model fails
to represent enhanced convective activities during the
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 523
excess year due to improper representation of steep
orography over this region. It is noted that the RMSE
and MAE are lower in the CSC3 and higher in the
CSC1 experiments in all years. Analysis of CC
indicates that model precipitation is positively corre-
lated with the observed precipitation. Table 3 shows
that the CC is higher in the CSC3 experiment than in
other experiments in all three years. The maximum
CC in coarse resolution simulations is found in CSC3
with magnitudes of 0.128, 0.133 and 0.191 for excess,
deficit and normal years, respectively. However, none
of the CCs are statistically significant at a 10 % SL.
Although the RMSE and MAE are found to be less in
the deficit year, the maximum CC is during the
normal year for all cumulus RegCM4 experiments.
RMSEs, MAEs and CCs computed for fine
resolution simulations are shown in Table 4. The
RMSEs and MAEs are lower during the deficit year
and higher in the excess year for each cumulus
experiment. This feature is also reflected in the coarse
Figure 16Mean seasonal convective heating rate (�C/day) computed from NNRP2, and RegCM4 simulations at 30 km resolution using FSC1, FSC2,
FSC3 and FSC4 cumulus schemes for a excess, b deficit and c normal precipitation years, respectively
524 P. Sinha et al. Pure Appl. Geophys.
resolution simulation. Comparing the RMSE (or
MAE) among cumulus scheme experiments, FSC3
has minimal error, while FSC1 has maximum error in
all three years. The correlation value indicates that
almost all CCs are statistically significant, except in
FSC1 for excess and deficit years, and FSC2 for the
excess year. The maximum correlation is found in the
FSC3 experiment with magnitudes of 0.359, 0.313
and 0.351 for excess, deficit and normal years,
respectively. It is noted that the RMSE, as well as
the MAE, in fine resolution simulations are reduced
by 2–3 times for each experiment as compared to
coarse resolution simulations. Simultaneously, it is
found that correlation values have also increased 2–3
times in fine resolution experiments compared to
coarse resolution simulations. Therefore, the results
suggest that the Fritsch–Chappell closure performs
better than the Arakawa Schubert closure in simulat-
ing seasonal precipitation over the WH region.
However, the overall performance of the Grell
cumulus scheme is better than other cumulus schemes
at simulating winter circulation and precipitation over
the IWH region.
3.3. Equitable Threat Score (ETS)
The equitable threat score (ETS) is a skill metric
generally used for yes/no forecasting (GILBERT 1884;
WILKS 1995). It measures the fraction of observed and
forecasted events that were correctly predicted and
adjusted for hits associated with random chance; it
has been computed for each scheme for the domain-
averaged precipitation. ETS can be represented
mathematically as:
ETS ¼ ðH � HkÞðH þM þ F � HkÞ
;
where Hk ¼ðH þMÞ � ðM þ FÞ
T
ð3Þ
where M, H, and F are the number of misses, hits, and
false alarms for each category, respectively. Hits due
to random chance are denoted by Hk and T is the total
number of events. ETS varies from -0.33 to 1 with
an ETS = 0 indicating no skill in prediction and an
ETS = 1 indicating perfect skill in prediction. In the
present study, the threshold value for wet days is
considered when observed precipitation exceeds
1 mm/day.
In the present study, daily mean precipitation
from IMD gridded data and model simulations are
used to calculate the ETS. The ETS values for
excess, deficit and normal years are shown in
Fig. 17a, b for coarse and fine resolutions, respec-
tively. In coarse resolution simulations, the ETS
Table 3
Mean absolute error (MAE), root mean square error (RMSE) and
anomaly correlation coefficient (spatial) for excess, deficit and
normal precipitation years at a 90 km resolution
Excess Deficit Normal
MAE
CSC1 7.461 2.619 6.671
CSC2 6.247 2.471 6.439
CSC3 6.018 2.132 5.014
CSC4 6.176 2.293 6.281
RMSE
CSC1 9.343 4.783 7.898
CSC2 9.149 4.592 7.326
CSC3 8.911 3.737 6.697
CSC4 8.976 3.874 6.898
Correlation
CSC1 0.103 0.117 0.152
CSC2 0.119 0.127 0.163
CSC3 0.128 0.133 0.191
CSC4 0.124 0.132 0.189
Italicized values indicate the particular cumulus scheme is better
Table 4
Mean absolute error (MAE), root mean square error (RMSE) and
anomaly correlation coefficient (spatial) for excess, deficit and
normal precipitation years at a 30 km resolution
Excess Deficit Normal
MAE
FSC1 2.937 1.583 2.674
FSC2 2.684 1.517 2.385
FSC3 1.953 1.212 1.608
FSC4 2.179 1.439 1.973
RMSE
FSC1 3.876 1.987 3.217
FSC2 3.683 1.797 3.023
FSC3 3.448 1.587 2.778
FSC4 3.526 1.673 2.931
Correlation
FSC1 0.207 0.263 0.287
FSC2 0.249 0.285 0.301
FSC3 0.359 0.313 0.351
FSC4 0.317 0.298 0.329
Italicized values indicate the particular cumulus scheme is better
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 525
for the wet day’s category indicates that the skill
of the model is higher in the normal year and less
in the deficit year in all cumulus experiments. It is
seen that the ETS is positive in all experiments for
all three years, except in CSC1 during the excess
year. The ETS is higher in CSC3 than in other
experiments for all three years. Thus, the skill of
the model is higher with the Grell cumulus scheme
and FC closure in coarse resolution RegCM4
simulations. However, the maximum value of
ETS is found to be only 0.18 in the coarse
resolution experiments. In fine resolution simula-
tions, all the values of ETS are positive in all the
experiments, except in FSC1 during the excess
year. Computed ETS values show the performance
is better in FSC3 experiments than in other
RegCM4 experiments. It is noted that the ETS
value increases in fine resolution simulations as
compared to coarse resolution simulations in the
corresponding year. However, the maximum ETS
in fine resolution reaches 0.22 in FSC3 during the
excess year.
3.3.1 Model Precipitation Validation at Observed
Locations
It is seen from the previous discussions that fine
resolution models are better at simulating precipitation
during all three years. Here, the RegCM4-simulated
precipitation obtained from fine resolution is validated
with the SASE observations over seventeen stations
located throughout the IWH region. The geographical
locations of these seventeen stations are shown in
Fig. 18. The modeled precipitation (for all cumulus
experiments) is interpolated bi-linearly to the station
locations for excess, deficit and normal years. Station-
wise seasonal mean precipitation obtained from obser-
vations and model simulations are shown in Table 5.
Model precipitation values in the italic format indicate
the ones closest to observations.
The performance of the Grell cumulus scheme
(FSC2 and FSC3 experiments) is better than the Kuo
(FSC1) and MIT (FSC4) experiments for all three -
years (Table 5). The performance of Grell scheme
with the AS closure (FSC2) and FC (FSC3) closure is
Figure 17Equitable threat score (ETS) computed for RegCM4 simulations with a CSC1, CSC2, CSC3 and CSC4 at 90 km resolutions and b FSC1,
FSC2, FSC3 and FSC4 for 30 km resolutions
526 P. Sinha et al. Pure Appl. Geophys.
almost the same for the normal year. However, the
performance of the Grell scheme with an FC closure
is very high during the deficit year. Model-simulated
seasonal precipitation is closer to observations at 10
out of 17 stations in FSC2 during the excess year,
while the same is true for 16 out of 17 stations in
FSC3 during the deficit year. Table 5 illustrates that
the precipitation in FSC4 is overestimated at all the
stations during excess, normal and deficit years. It is
found that the simulated precipitation in FSC4 also
differs by a larger amount (*1.5–10 times from the
observations) as compared to other cumulus exper-
iments. The excess precipitation in FSC4 is probably
due to more convective heating at the upper level in
the model as compared to observations. Based on
Table 5, model performance has been evaluated by
analyzing the variation in precipitation for three
different years. For this purpose, the differences
between excess and normal, as well as deficit and
normal, were computed for the SASE observations
and model simulations for each observing station.
Phase-synchronizing events (hereafter referred to as
PSE) have been calculated using model outputs and
observations from all stations. The PSE method
matches the sign (positive or negative) of the
precipitation differences (excess—normal or defi-
cit—normal) obtained from observations and model
simulations. This method evaluates the ability of the
model to replicate observed inter-annual variations in
precipitation. The PSE is computed as follows
PSE ¼ N � N 0
N
� �� 100 ð4Þ
where, N is the total number of events and N0 is the
number of events in the model simulation that are
opposite in sign as compared to observations (i.e., out
of phase). Thus, PSE equals 100 for the model results
when the sign of model anomalies (here, the differ-
ence of excess and deficit years from the normal year)
are the same as observations for all stations, and
Figure 18Geographical locations of the 17 stations considered for this study. Station precipitation observations obtained from the Snow and Avalanche
Study Establishment (SASE) are used for validation of model results. The study region is denoted by a red rectangular box; station locations
are shown in the inset (Courtesy: The color geographical generated from http://woodshole.er.usgs.gov/mapit)
Vol. 172, (2015) Sensitivity Studies of Convective Schemes and Model Resolutions 527
PSE = 0 when none of the stations have a similar
sign as the observations. Using the Table 5, it is seen
that the PSE value is the same for all cumulus
experiments (i.e., the sign of the model output mat-
ches observations 82 % of the time) for the excess-
minus-normal year. However, the PSE is the highest
in FSC3 with 94 % and the lowest in FSC2 with
44 % for deficit-minus-normal year. It is noted that
the performance of FSC1 and FSC4 is similar, with
an 82 % PSE for the deficit-minus-normal year.
Considering both the extreme (excess and deficit)
years as compared to the normal year (i.e., excess-
normal and deficit-normal), it is found that the PSE is
the highest in the FSC3 experiment (88 %), followed
by FSC1 and FSC4 (82 %), and the lowest in FSC2
(64 %). Therefore, the inter-annual variation of pre-
cipitation over the IWH region during winter seasons
is represented better by the FCS3 experiment.
4. Conclusion
In the present study, a series of experiments with a
regional climate model were carried out to examine the
sensitivity of cumulus parameterization schemes to two
different horizontal model resolutions (90 and 30 km).
Model simulations were conducted for three distinct
winter precipitation years (normal, excess and deficit)
over the Western Himalayas using three convective
parameterizations schemes and two closure schemes
(i.e., Kuo-AS, Grell-AS, Grell-FC and MIT-FC) avail-
able in the ICTP Regional Climate Model RegCM4
(version 4). Model integration is carried out in a one-
way nested mode in which the outer domain is at a
90 km resolution, while the inner domain is at a 30 km
resolution. The performance evaluation of the RegCM4
simulations led to the conclusions discussed below.
The RegCM4 model is able to demonstrate the
mean seasonal upper air circulation patterns (wind
field and temperature) at different pressures reason-
ably well during three distinct precipitation years.
The model represents the contrasting features of the
circulation pattern and circulation intensity in excess
and deficit years. The performance of the RegCM4
model varies with different cumulus parameterization
schemes; however, the Grell cumulus scheme with a
Fritsch–Chappell closure performs better than other
cumulus and closure combinations.
Table 5
Mean seasonal precipitation at seventeen (17) stations from SASE observation, and RegCM simulations at fine resolutions with all cumulus
experiments for three different years
Station name (number) 1997–1998 (excess) 2000–2001 (deficit) 2003–2004 (normal)
SASE FSC1 FSC2 FSC3 FSC4 SASE FSC1 FSC2 FSC3 FSC4 SASE FSC1 FSC2 FSC3 FSC4
Bahadur (1) 3.78 4.02 3.58 3.89 6.83 1.79 3.04 2.60 2.01 4.02 1.08 3.91 3.04 3.05 4.78
Banihal (2) 5.62 8.56 6.82 7.87 15.05 0.75 4.65 4.78 2.45 9.41 1.94 5.62 5.19 4.60 8.60
Bhang (3) 4.97 9.13 7.33 7.72 12.62 1.32 5.28 5.00 3.11 8.14 4.57 6.32 5.83 6.03 8.33
Dhundi (4) 9.79 9.27 7.71 7.97 12.63 2.49 5.60 5.01 3.48 8.48 7.75 6.52 6.35 6.48 8.37
Dras (5) 1.97 6.16 5.42 5.56 10.08 1.36 4.15 2.60 2.66 5.99 1.71 4.87 4.78 4.75 6.77
Gulmarg (6) 8.05 7.53 6.06 6.57 13.51 2.92 3.78 4.65 2.47 7.52 4.96 5.10 4.08 4.68 8.67
H-Taj (7) 10.22 9.41 7.47 7.98 16.25 2.37 4.73 5.32 2.52 8.41 8.11 5.96 4.50 4.65 10.66
Kanzalwan (8) 7.91 9.36 8.52 8.69 15.12 3.28 5.72 5.45 2.81 8.62 6.54 5.67 5.03 5.38 9.76
Kumar (9) 1.26 4.27 3.78 4.06 6.93 0.77 3.11 2.25 2.13 3.95 0.99 3.71 2.94 3.00 5.16
Neeru (10) 2.49 8.57 7.77 7.96 14.05 2.08 5.79 4.64 4.11 7.88 3.85 6.17 5.63 5.60 10.24
Patsio (11) 2.66 5.32 4.73 4.73 7.85 1.46 3.46 2.49 2.40 5.19 3.33 4.52 4.15 4.24 5.39
Pharki (12) 9.10 9.71 8.23 8.55 15.91 3.46 5.41 5.25 2.93 8.25 5.21 5.72 4.68 4.84 11.04
Solang (13) 5.75 4.71 2.88 3.29 8.20 1.13 2.11 2.33 1.52 3.99 5.11 3.66 2.05 2.59 6.23
Stg-II (14) 8.98 10.07 8.19 8.62 16.77 2.02 5.20 5.44 2.63 8.58 8.46 6.15 4.71 4.84 11.26
Z-Gali (15) 5.08 9.74 8.38 8.76 15.74 2.73 5.62 5.58 2.94 8.75 5.65 5.58 4.91 5.34 10.44
Gugaldhar (16) 5.71 9.80 8.25 8.64 15.93 2.05 5.48 5.41 2.96 8.48 4.69 5.64 4.78 5.07 10.90
Dawar (17) 4.47 9.21 8.33 8.37 14.54 1.59 5.68 5.11 2.88 8.25 3.96 5.87 5.29 5.47 9.50
Italicized values indicate values closer to observations
528 P. Sinha et al. Pure Appl. Geophys.
Representation of the intensity and spatial dis-
tribution of precipitation is poor in coarse resolution
simulations; however, fine resolution simulations are
significantly better. The MIT cumulus scheme
overestimates precipitation by a large amount during
all three years probably due to more convective
heating over the orography of the region. The Kuo
scheme in this regional climate model is probably
not suitable for studying small-scale convective
processes over the WH. The Grell scheme works
better than other schemes in both coarse and fine
resolution simulations of winter circulations and
precipitation.
Acknowledgments
This study was financially supported by the Snow
Avalanche Study Establishment (SASE). The Reg-
CM4 model, installed at IIT Delhi, was developed at
the ICTP, Trieste, Italy. Authors sincerely acknowl-
edge the IMD for providing daily gridded
precipitation data. The authors would like to
acknowledge the NCEP for reanalysis 2 data and
the NOAA for optimum interpolated SST version 2
data provided by the NOAA/OAR/ESRL PSD,
Boulder, Colorado, USA, from their Web site at
http://www.esrl.noaa.gov/psd/. The authors duly
acknowledge Bianca C. for editing the English of the
manuscript. Authors also acknowledge the comments
by the anonymous reviewers that helped improve the
earlier version of the manuscript.
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(Received February 17, 2014, revised August 27, 2014, accepted September 17, 2014, Published online December 9, 2014)
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