Sensitivity Studies of Convective Schemes and Model ...web.iitd.ac.in/~sagnik/PAG2014.pdfcipitation during the summer monsoon over the Indian region are well represented in RCMs. How-ever,

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:

[email protected] 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

http://dx.doi.org/10.1007/s00024-014-0935-3

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)


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)


ii) Grell cumulus with Arakawa Schubert closure


iii) Grell cumulus scheme with Fritsch Chappell closure (Grell-FC)


iii) Grell cumulus scheme with Fritsch Chappell closure


iv) MIT-Emanuel cumulus scheme with Fritsch Chappell closure (MIT-FC)


iv) MIT-Emanuel cumulus scheme with Fritsch Chappell closure



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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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)


http://woodshole.er.usgs.gov/mapit

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


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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