Influence of land-surface and turbulent parameterization schemes on regional-scale boundary layer characteristics over northern India

Atmospheric Research 112 (2012) 89–111

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Influence of land-surface and turbulent parameterization schemes on regional-scale boundary layer characteristics over northern India Jagabandhu Panda 1, Maithili Sharan ⁎ Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

a r t i c l e

i n f o

Article history: Received 24 May 2011 Received in revised form 24 February 2012 Accepted 6 April 2012 Keywords: Boundary layer features WRF modeling system Turbulent parameterizations Land-surface models Thar Desert

a b s t r a c t The influence of turbulent and land-surface parameterizations on regional scale boundary layer features over north India is analyzed using the Weather Research and Forecasting (WRF) modeling system during two contrasting cases of summer and winter. The model predicted surface temperatures, wind speeds, potential temperature profiles and wind speed profiles are compared with the observations from India Meteorological Department and Wyoming Weather Web data archive. The qualitative and quantitative analyses indicate that the model predictions are relatively better over three north Indian cities namely Delhi, Ahmedabad and Jodhpur when the Mellor–Yamada–Janjic boundary layer scheme along with Noah landsurface model is used. The near surface flow features during both summer and winter cases indicate the major role of land surface models (LSMs) as compared to the boundary layer parameterizations in governing the regional scale flow fields. The role of the LSMs and boundary layer parameterizations in the regional scale transport of dust particles from Thar region toward Delhi and its neighborhood depends upon their point of origin during summer. However, the flow trajectories travel in the opposite direction during the winter case because of the contrasting nature of the flow patterns and consequently, the formation of haze-like conditions over Delhi due to Thar dusts is not expected. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The partitioning of solar energy at the Earth surface into ground heat storage and sensible and latent heat fluxes plays a vital role in determining the temperature and humidity near the ground surface. The horizontal variation in the relative amounts of these fluxes might be due to the variation in landsurface characteristics (e.g. soil type, vegetation and soil moisture content). Thus, altering of the ground conditions in a model can cause the variation in these fluxes over a region. The land-surface and boundary layer parameterizations in a

⁎ Corresponding author at: Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110 016, India. Tel.: +91 11 2659 1312; fax: +91 11 2658 2037. E-mail address: [email protected] (M. Sharan). 1 Present address: School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore. 0169-8095/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2012.04.001

model modify the flow features within the atmospheric boundary layer (ABL) or planetary boundary layer (PBL) and in turn influence the transport and diffusion of air pollutants (e.g. Boybeyi et al., 1995; Sharan et al., 2000). The transport of air pollutants at local or regional scale is influenced by the corresponding local or regional scale circulations. For example, the extensive observational study carried out by Gautam et al. (2009a) indicates the climatic impact of air pollutants especially the Thar dusts over north Indian region. The Indo-Gangetic plane bounded by the Himalayas (Fig. 1) is found to be strongly influenced by the transported dust particles from the Thar region during the pre-monsoon days. The studies of Gautam et al. (2009b) indicate that unusually weak dust period occurs because of the plausible role of the immediately preceding excess winter monsoon rainfall in contrast to the strong dust period. Further, the accumulation of dusts over north Indian region could influence the rainfall pattern (Gautam et al. 2009c) as

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Fig. 1. The selected domain over north Indian region. Here the shaded part with star sign represents Himalayan region, the shaded region with cross sign represents Indo-Gangetic plane, the shaded region with plus sign represents Thar Desert, 1—Gujarat, 2—Madhya Pradesh, 3—Uttar Pradesh, 4—Uttaranchal, 5—Himachal Pradesh, 6—Jammu and Kashmir, 7—Punjab, 8—Haryana, 9—Rajasthan, AH—Ahmedabad, DE—Delhi and JO—Jodhpur. [Adopted from Panda et al. (2009)].

well. However, these studies did not use any mesoscale or regional scale model to show the differences in flow patterns during the summer and/or winter. The boundary layer parameterization coupled with a land-surface model (LSM) within a mesoscale modeling system provides more realistic information about the near surface atmosphere. The modeling of land– atmospheric interactions is not straightforward since they have a larger role in the mesoscale models (Chen and Dudhia, 2001a,b). The past studies based on various atmospheric processes at mesoscale level, have used numerical models for both research and operational purposes. For example, the studies of Boybeyi et al. (1995) used a three dimensional mesoscale model coupled with a three dimensional Monte Carlo dispersion model to investigate the possible role of local meteorology during the Bhopal gas accident. Similarly, Sharan et al. (1995, 2000) have used University of Virginia mesoscale model (Pielke, 1974) coupled with a Lagrangian particle dispersion model to simulate the episodic dispersion of methyl isocyanate and investigate the possible urban influences on the meteorological conditions during the infamous Bhopal gas leak. Han et al. (2008) have used MM5 model (Grell et al., 1995) over East Asia to carry out a sensitivity study to evaluate the model results using surface, radio-sonde, aircraft and satellite measurements. Recently, Weather Research and Forecasting (WRF) modeling system (Skamarock et al., 2005) has been developed as a result of multi-institutional efforts by incorporating advanced dynamics, physics and numerical schemes like that of MM5 model (Grell et al., 1995). It has been used in several studies for simulating local and mesoscale events (e.g. Lin et al., 2008; Miao

et al., 2009). For example, the WRF modeling system is used to investigate the impact of heterogeneous land-surface and prevailing synoptic conditions on the regional scale flow features in our earlier study based on the regional scale boundary layer characteristics over north Indian region (Panda et al., 2009). The study indicates that the thermally driven regional circulations play a major role in the transport of particulate matter from the Thar Desert to Delhi and its neighboring regions during summer. However, it does not emphasize extensively upon the role of land-surface and boundary layer parameterizations in governing the regional flow patterns over north India. When this follow up study was carried out, prior published results on the evaluation of boundary layer and land-surface parameterizations using WRF modeling system were not available extensively even though similar studies using MM5 modeling framework were abundant (e.g. Miao et al., 2007; Srinivas et al., 2007; Sanjay, 2008). However, few sensitivity studies with respect to boundary layer (Gilliam and Pleim, 2009; Hu et al., 2010; Shin and Hong, 2011) and land-surface (Chang et al., 2009) parameterizations are recently available in literature. For example, Gilliam and Pleim (2009) made a performance assessment of Pleim Xiu land-surface model, Pleim surface layer scheme, and asymmetric convective model version 2 (ACM2) using setups of both WRF and MM5 modeling framework over eastern United States (or USA) and recommended them for the use of air quality applications. In view of the significance of boundary layer parameterizations in the air pollution modeling, Hu et al. (2010) also evaluated three boundary layer schemes

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over Texas, USA during September 2005 using observations from south-central USA. Shin and Hong (2011) carried out a single day case study chosen from CASES-99 experiment (http://www.cora. nwra.com/cases/CASES-99.html) for the inter-comparison of boundary layer parameterization schemes within WRF modeling system. Recently, Gibbs et al. (2011) have also evaluated WRF model results over southern Great Plains using three boundary layer parameterizations and numerical large-eddy simulation data for conditions corresponding to a dry atmospheric convective boundary layer. On the other hand, Chang et al. (2009) have examined the role of three different land-surface models (coupled with WRF model) in the simulation of the July 26, 2005 heavy rainfall event over Mumbai in the Indian context. The present work is undertaken in order to examine the influence of land-surface and turbulent parameterization schemes on regional-scale boundary layer characteristics over northern India during two contrasting cases of summer and winter. A brief description about the WRF modeling system is given in Section 2. Section 3 describes about the experimental design. The results from the simulations are described in Section 4 and Section 5 briefly summarizes the significant conclusions obtained from the results. 2. Numerical model The present study uses advanced research WRF modeling system version 2.1 (Skamarock et al., 2005). It uses a set of governing equations based on conservation of mass, momentum, energy and scalars such as moisture (Ooyama, 1990). These equations are written in flux form on a Eulerian solver and use mass-based vertical “η” co-ordinate that varies from 1 at the surface to 0 at the upper boundary of the model domain. These equations are solved using initial and boundary conditions provided to the model from global FNL data. In the present study, the model is initialized with the six hourly NCEP (National Center for Environmental Prediction)/NCAR (National Center for Atmospheric Research) global FNL data (http:// dss.ucar.edu/datasets/ds083.2/data/) of 1° × 1° resolution at 00 UTC in both summer and winter cases. The model is initialized through the standard initialization process that defines the physical grid, interpolates the static fields (terrestrial data) from USGS (United States Geological Survey) global data and interpolates the meteorological data from global analysis data to the model coordinates in both horizontal and vertical directions within the specified domain. The simulations of both summer and winter cases use a set of physics options containing shortwave radiation parameterization from Dudhia (1989), Rapid Radiative Transfer Model (RRTM) for longwave radiation (Mlawer et al., 1997), Ferrier scheme (Rogers et al., 2001) for microphysics, Betts–Miller– Janjic cumulus convection scheme (Betts and Miller, 1986; Janjic, 1994), land-surface parameterizations such as (i) thermal diffusion (THD) LSM, (ii) Rapid Update Cycle (RUC) LSM (Smirnova et al., 1997, 2000) and (iii) Noah LSM (Chen and Dudhia, 2001a) and PBL parameterizations such as (i) Mellor– Yamada–Janjic (MYJ) scheme (Janjic, 1990), (ii) Medium Range Forecasting (MRF) scheme (Hong and Pan, 1996) and (iii) Yonsei University (YSU) scheme. The surface layer schemes used along with the PBL parameterizations are primarily based upon Monin–Obukhov approach. Since the present study focuses on

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the role of land-surface and boundary layer parameterizations in governing the regional scale boundary layer characteristics over north Indian region, a brief description of these is given here. 2.1. A brief description of land-surface physics The LSMs are primarily based upon the surface energy budget at the Earth surface that is mainly composed of four terms: net radiation, sensible heat flux, latent heat flux and ground heat flux. The net radiation consists of incoming shortwave radiation from the sun, the reflected shortwave radiation, longwave down-welling radiation and longwave upwelling radiation. Each LSM uses atmospheric information from surface layer scheme, information regarding shortwave and longwave radiations from the corresponding radiation schemes, precipitation information from microphysics and cumulus convection schemes along with the land's state variables and land-surface properties from terrestrial static data to provide the surface fluxes. These fluxes are used as lower boundary conditions in the PBL schemes to govern the vertical diffusion and transport throughout the atmospheric regime. The LSMs do not provide tendencies. However, they update the state variables (e.g. the skin temperature, soil temperature and moisture profiles, snow cover and the canopy properties). The THD LSM in WRF modeling system is a soil temperature model (Skamarock et al., 2005) considering five layers of subsurface soil of thickness 1, 2, 4, 8, and 16 cm. The temperature below these layers is considered to be fixed as a deep-layer average. It only considers the sub-surface soil temperature as the soil variable and does not consider any explicit vegetation effect. The soil moisture is assumed to be fixed with the type of land-use and is also season dependent. The RUC LSM considers six sub-soil layers (ranging from ground surface to 3 m in depth) and up to two snow layers (Smirnova et al., 1997, 2000). In this LSM, the heat and moisture balance equations are solved separately for soil temperature and moisture (for both bare soil and vegetated surfaces) using finite difference technique (Smirnova et al., 1997). The Noah LSM (Chen and Dudhia, 2001a,b) considers four sub-surface soil layers of thickness 10, 30, 60 and 100 cm from the ground surface to the bottom respectively along with one canopy layer. It considers the prognostic variables such as soil temperature and moisture in the soil layers, water stored on the canopy and snow stored on the ground (one snow layer considered). The total soil depth is 2 m with the root zone in the upper 1 m of the soil. The lower 1 m soil layer acts like a reservoir with gravity drainage at the bottom. In addition to the vegetation effect, it also considers evapotranspiration, soil drainage and surface runoff. Each of the LSMs in WRF modeling system described above considers the fluxes from the soil in the energy budget in order to compute the ground temperature. 2.2. A brief description of PBL parameterizations The PBL physics is responsible for the vertical sub-grid-scale fluxes due to eddy transports in the chosen atmospheric column. The PBL schemes provide the flux profiles within the whole atmospheric column including the mixed layer and stable boundary layer (SBL).

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Out of the three PBL schemes indicated earlier (in the second paragraph of Section 2), the MRF PBL scheme (Hong and Pan, 1996) is based on the so-called “non-local K” approach (Troen and Mahrt, 1986; Holtslag and Boville, 1993; Hong and Pan, 1996) adopted for mixed layer diffusion and the local diffusion approach for the layer above it. The turbulent diffusivity coefficients in MRF PBL scheme are calculated from a prescribed profile shape, as a function of boundary layer height (h) and scale parameters derived from similarity theory to satisfy the matching conditions between the surface layer top and boundary layer bottom. The momentum diffusivity coefficient (Kzm) is given as (Troen and Mahrt, 1986; Hong and Pan, 1996):
z p K zm ¼ kws z 1− h

ð1Þ

where, p (=2) is the profile shape exponent, k (=0.4) is the von Kármán constant, z is the height from the surface and ws is the velocity scale for mixed layer given by, ws ¼ u Φm

−1

ð2Þ

in which, u* is the surface frictional velocity scale and Φm is the similarity function for momentum evaluated at the top of the surface layer. To satisfy the compatibility between the surface layer top and the bottom of the PBL, the identical profile functions to those in surface layer physics are used (Hong and Pan, 1996). For unstable and neutral conditions, different similarity functions for wind (Φm), potential temperature (Φh) and moisture (Φq) at the top of the surface layer (hs) are used unlike the stable condition similarity functions where, similar profiles are used for parameterization of the turbulent diffusivity coefficients (Hong and Pan, 1996). For all of the similarity functions, hs is considered to be ‘0.1h’, where h is given as (Hong and Pan, 1996): h ¼ RiBcr

θva jV ðhÞj2 g ðθv ðhÞ−θs Þ

ð3Þ

where, w*b is the convective velocity scale for the moist air. In comparison to MRF PBL scheme, the YSU PBL scheme increases boundary layer mixing in the thermally induced free convection regime and decreases it in the mechanically induced forced convection regime (Hong et al., 2006). Further, the YSU PBL takes into account both of the local-K approach of Louis (1979) and the entrainment flux irrespective of local stability at the PBL top in order to parameterize the free atmospheric diffusion (Noh et al., 2003). It considers both effects within the entrainment zone and the local-K approach above it (Hong et al., 2006). In contrast to MRF and YSU PBL schemes, MYJ PBL (Janjic, 1990, 2002) is a prognostic turbulent kinetic energy (TKE) based scheme and represents a non-singular implementation of Mellor–Yamada turbulence closure theory of level 2.5 (Mellor and Yamada, 1982) within the PBL. It estimates turbulent exchange coefficients for momentum (Kzm) and heat (Kzt) as follows: K zm ¼ ‘Ek SM

ð5Þ

K zt ¼ ‘Ek SH

ð6Þ

where ‘ represents the master length scale (MLS) used in MYJ (Mellor–Yamada–Janjic) scheme and ‘Ek2/2’ represents the TKE. The values of SM and SH are computed from Mellor– Yamada level 2.5 closure model (Mellor and Yamada, 1982) and ‘ is determined from the diagnostic formula of the form (Mellor and Yamada, 1982; Janjic, 2002): h

‘¼

‘0 kz ; ‘ ¼α kz þ ‘0 0

∫ jzjEk dz 0 h

ð7Þ

∫ Ek dz 0

in which, α is an empirical constant. An upper limit is imposed on ‘ in both stable and unstable conditions. This upper limit depends on TKE, buoyancy in the atmospheric column and the shear of the driving flow. 3. Experimental design

in which, RiBcr is the critical bulk Richardson number, V(h) is the horizontal wind speed at a height z=h, g is the acceleration due to gravity, θva is the virtual potential temperature at the lowest model level, θv(h) is the virtual potential temperature at h, and θs is the potential temperature near the surface. The value of h is obtained iteratively and the diffusion coefficients for temperature and moisture are determined from the expressions similar to that of Kzm (Hong and Pan, 1996). For free atmospheric diffusion, the MRF PBL uses local-K approach in view of Louis (1979). In this scheme, the vertical diffusion coefficients are represented in terms of the mixing length, the stability functions and the vertical wind shear, where the stability functions are expressed in terms of the local gradient Richardson number (Hong and Pan, 1996). The YSU PBL scheme (Hong et al., 2006) is an advancement of MRF PBL scheme. In this scheme, the velocity scale for mixed layer is calculated as:
1=3 3 3 ws ¼ u þ Φm kwb z=h

ð4Þ

To study the role of PBL and land-surface parameterizations in regional scale boundary layer characteristics, a model domain containing Indo-Gangetic plane, Thar Desert and north Indian cities such as Delhi, Ahmedabad and Jodhpur is considered (Fig. 1). The resolution of the domain is taken as 15 km with 126 grid points along both the horizontal directions and the vertical resolution of the model contains 31 “η” levels (i.e. 1.000, 0.993, 0.980, 0.966, 0.950, 0.933, 0.913, 0.892, 0.869, 0.844, 0.816, 0.786, 0.753, 0.718, 0.680, 0.639, 0.596, 0.550, 0.501, 0.451, 0.398, 0.345, 0.290, 0.236, 0.188, 0.145, 0.108, 0.075, 0.046, 0.021, 0). At the top of the model grid, pressure of 250 hpa is considered to be constant for all simulations. The central latitude and longitude for all experiments (Table 1) are taken to be 27°N and 76°E respectively. The simulations are carried out for two synoptically contrasting cases: (i) May 20–22, 2005 representing summer case and (ii) Dec 09–11, 2004 representing winter case. The summer days are accompanied by high and variable winds in

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Table 1 Different sets of experiments carried out for sensitivity studies. Name of the simulation

Land-surface model (LSM)

Boundary layer parameterization

Surface layer scheme

No of sub-soil layers

CONTROL YSU + NOAH MRF + NOAH MYJ + THD MYJ + RUC

NOAH LSM NOAH LSM NOAH LSM Thermal diffusion LSM RUC LSM

MYJ PBL scheme YSU PBL scheme MRF PBL scheme MYJ PBL scheme MYJ PBL scheme

MYJ surface layer similarity based on Monin–Obukhov theory (1954) Surface layer similarity taken from MM5 model Surface layer similarity taken from MM5 model MYJ surface layer similarity based on Monin–Obukhov theory (1954) MYJ surface layer similarity based on Monin–Obukhov theory (1954)

4 4 4 5 6

convective boundary layer (CBL) whereas the winter days are associated with low to moderate winds in a relatively stable environment (Panda et al., 2009). Each of the simulations is carried out for 48 h. For this purpose, the model is initialized at 00 UTC using the global FNL data through a standard initialization process along with an input of land-surface characteristics from USGS (United States Geological Survey) geographical data during both summer and winter cases. The values at the lateral boundaries are also updated every six hours from FNL data. For each case, five sets of experiments are designed (Table 1) in order to analyze the relative performance of PBL schemes and LSMs. The experiment with MYJ PBL along with Noah LSM is designated as “CONTROL” simulation. First three experiments (Table 1) allow us to analyze the role of PBL parameterizations whereas the experiments CONTROL, MYJ + THD and MYJ + RUC are used to study the role of land-surface parameterizations in governing the regional scale boundary layer characteristics over north Indian region. The experimental design also includes the determination of model spin-up time. Usually, the spin-up time for mesoscale models like WRF is few hours. In order to address the spin-up issue, several cases were run using the same set of physics and dynamics as that of CONTROL experiment. The temporal variation of variables including the domain averaged turbulent kinetic energy (TKE), domain averaged (w+)2 and domain averaged vorticity variance was analyzed at several pressure levels. Here w + = w − (domain averaged w), where w denotes the vertical component of velocity. The temporal variations of these parameters (figures not shown for brevity) suggest that the spin-up time within the boundary layer could be considered as 3 h. On the other hand, the spin-up time above the boundary layer could be considered as 4–5 h or 6 h. However, the diurnal variations for near surface variables are discussed in this study by taking into account the initial hour (00 UTC or 0530 IST) for the sake of continuity. 4. Results and discussion The results obtained from five sets of experiments (Table 1) carried out for both summer and winter cases are presented in this section. The simulated diurnal variation of surface wind speed, temperature, friction velocity, latent heat flux, sensible heat flux and the vertical profiles of temperature and wind speed over Delhi, Ahmedabad and Jodhpur are analyzed to understand the surface layer characteristics and the evolution of boundary layer over these three places. The diurnal variation of simulated surface temperature and wind is compared with those observed over the three cities. The surface temperature and wind observations are taken from India Meteorological

Department (IMD). The measured surface wind is reported at 10 m and the temperature at a level of 2 m above the ground as per the WMO (World meteorological Organization) guidelines. The observations for comparison with the model simulated vertical profiles of potential temperature and wind are taken from Wyoming Weather Web (UWY) data archive. The surface observations are the measurements obtained by the instruments available at local meteorological stations usually operational and maintained by IMD. The vertically observed potential temperature and wind are the upper-air sounding station data measured at various heights and/or pressure levels. The comparison of the vertical profiles is done at synoptic hours 00 universal coordinated time (UTC) or 0530 h local Indian standard time (IST) and 12 UTC (or 1730 h IST). The height levels (e.g. 0.06, 0.1, 0.2….up to 3 km) for the vertical profiles were specified in the “namelist” in order to post-process the model results at these levels using WRF2GrADS post-processor (presently ARWpost is used for this purpose) at the synoptic hours of a particular date. The postprocessed results for vertical profiles are plotted against the observations for a qualitative comparison (the station elevation is subtracted from the prescribed levels before plotting in order to maintain a coherence of the heights and observations). In addition to the diurnal variation of surface layer parameters and the vertical profiles of potential temperature and wind speed, the simulated surface flow fields along with the trajectories of the flow are also discussed to examine the relative performance of the LSMs and PBL parameterizations. 4.1. Surface temperature The trend of surface temperatures with different PBL schemes for a given LSM (i.e. Noah LSM) is almost similar to that observed (Fig. 2) over the cities Delhi, Ahmedabad and Jodhpur during both summer and winter cases. This is supported by the high values of correlation coefficients (~0.9 or greater) between computed and observed temperatures over a city (not given for sake of brevity). The ranges of observed surface temperatures over Delhi, Ahmedabad and Jodhpur during summer case are 27.6°–41 °C, 28.2°–44 °C and 28.6°–43.6 °C respectively. The degree of agreement of the CONTROL simulation with the observations over Jodhpur (Fig. 2e) is better during summer case whereas the diurnal surface temperature variation at Delhi and Ahmedabad in YSU+NOAH simulation agrees better with that of observation in this case (Fig. 2c). This is further supported by the computed values of RMSE (Root Mean Square Error), which suggest that the model errors from CONTROL simulation are relatively small over Jodhpur as when compared to that of YSU+NOAH and MRF+NOAH simulation in summer case (Table 2). However, the performance of YSU+NOAH simulation is relatively better

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(a) 45

(b)

32

Temperature ( oC)

42

Temperature ( oC)

35

39 36 33 30 27

29 26 23 20 17 14 11

24

8

21 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00

5 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00

Time (UTC)

(c) 45

Time (UTC)

(d) Temperature ( oC)

Temperature ( oC)

39 36 33 30 27 24

29 26 23 20 17 14 11 8

21

5 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00

00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00

Time (UTC)

Time (UTC)

(e) 45

(f)

35 32

Temperature ( oC)

42

Temperature ( oC)

35 32

42

39 36 33 30 27 24

29 26 23 20 17 14 11 8 5

21 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00

00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00

Time (UTC)

Time (UTC)

Fig. 2. Diurnal variation of surface temperature over three north Indian cities with PBL schemes: (a) Delhi during summer case, (b) Delhi during winter case, (c) Ahmedabad during summer case, (d) Ahmedabad during winter case, (e) Jodhpur during summer case and (f) Jodhpur during winter case. Here ─── CONTROL (MYJ+NOAH) simulation; ——— MRF+NOAH simulation; —————— YSU+NOAH simulation; ● ● ● observations from IMD.

over Delhi and Ahmedabad in the summer case (Table 2). In the winter case, all of these three sets (i.e. CONTROL, YSU+NOAH and MRF+NOAH) show over warming at night over Delhi (Fig. 2b) whereas over Ahmedabad (Fig. 2d) and Jodhpur (Fig. 2f), the variation is not so. It is because, the temperature over Delhi during night time is usually quite low in the month of December due to prevailing winter and the model is not able to capture the same in this case. The quantitative analysis shows that the model errors are relatively small over Delhi and Jodhpur in winter case during CONTROL simulation as compared to that of YSU+NOAH and MRF+NOAH simulations (Table 2). On the other hand, the study by Shin and Hong (2011) reveals that the discrepancies among thermodynamic surface variables from different PBL schemes are large at day time, while the variables converge at nighttime.

For a given turbulent parameterization scheme (i.e. MYJ), the diurnal evolution of surface temperature with different LSMs (Fig. 3) indicates that the THD LSM under-predicts surface temperatures and predictions from other two schemes are comparable to each other during the summer case. This is justifiable because the ground heat flux is relatively large during summer and THD LSM does not account for it. The RUC LSM and THD LSM are found to under‐predict and give rise to almost similar surface temperatures during winter case (Fig. 3). As the winter case is associated with the weak winds and the ground heat flux is expected to be small in these conditions, the comparable surface temperatures from RUC LSM and THD LSM appear to be reasonable. On the other hand, the values of RMSE indicate that with a given PBL scheme, Noah LSM is performing relatively better over all the cities in both summer and winter

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Table 2 Root Mean Square Error (RMSE) of computed surface temperature and wind speed with respect to IMD observations during summer and winter cases. Cities

CONTROL (MYJ + NOAH) simulation

MYJ + THD simulation

MYJ + RUC simulation

RMSE for surface temperature in summer (May 20–22) case Delhi 1.16 1.31 Ahmedabad 1.63 1.91 Jodhpur 2.39 2.47

YSU + NOAH simulation

MRF + NOAH simulation

1.33 1.99 2.14

5.39 5.80 6.62

2.57 2.66 3.06

RMSE for surface temperature in winter (December 09–11) case Delhi 2.35 2.93 Ahmedabad 3.46 3.27 Jodhpur 2.23 2.25

2.23 3.48 2.00

2.55 6.28 4.74

1.43 5.11 3.76

RMSE for surface wind speed in summer (May 20–22) case Delhi 2.15 2.17 Ahmedabad 0.88 0.78 Jodhpur 2.07 2.04

2.46 0.99 2.17

2.46 0.85 1.72

2.95 1.11 2.25

RMSE for surface wind speed in winter (December 09–11) case Delhi 2.35 2.41 Ahmedabad 1.95 1.90 Jodhpur 1.97 1.97

2.74 2.18 2.46

2.56 2.12 2.34

2.58 2.08 2.06

cases except over Delhi in the winter case (Table 2). The surface temperatures from Noah LSM may be closer to those observed due to accounting for the sources and sinks of water in the soil layers over urban areas. However, the performance of Noah LSM as compared to that of RUC LSM in predicting the near surface temperature is site specific, topography dependent and subjected to atmospheric stability (Prabha et al., 2011). In the summer case, the maximum observed surface temperatures within the first 24 h of simulation over Delhi, Ahmedabad and Jodhpur are 41 °C (at 09 UTC, May 20), 44 °C (at 12 UTC, May 20) and 43.6 °C (at 09 UTC, May 20) respectively. Similarly, the corresponding maximum observed surface temperatures within the next 24 h of simulation are 40.3 °C (at 09 UTC, May 21), 43.2 °C (at 12 UTC, May 21) and 43.4 °C (at 12 UTC, May 21) respectively. The maximum temperatures over Ahmedabad and Jodhpur are under‐predicted by all PBL schemes (considered here) in both of the cases. However, the maximum temperatures are over‐predicted over Delhi during winter case and on the second day of the summer case by the PBL schemes. On the other hand, the predicted diurnal variations from all the three LSM schemes suggest that the computed maximum temperature from Noah LSM simulation is relatively closer to that observed in comparison to other two schemes (i.e. THD and RUC LSMs). For example, the computed values of maximum temperature in the first 24 h of simulation during summer case from CONTROL, MYJ+RUC and MYJ+THD runs over Delhi are 39.67 °C, 37.87 °C and 33.50 °C at 09 UTC with errors 1.33 °C, 3.13 °C and 7.5 °C respectively. Similarly, the corresponding predicted maximum temperatures over Delhi in the next 24 h of simulation during summer case are 40.92 °C, 40.40 °C and 33.44 °C with errors −0.62 °C, −0.1 °C and 6.86 °C respectively (differences are calculated from observed values). Thus, the THD LSM is relatively unsuccessful in predicting the maximum value of surface temperature over Delhi and other places. Fig. 3 reveals that the THD LSM does not predict the diurnal variation of surface temperature well as when compared to the observations and results from Noah and RUC LSMs. It may be due to the fact that THD LSM does not consider ground heat flux in the surface

energy budget and consequently, influencing the surface temperature calculations or it may also be due to the inappropriate consideration of climatological moisture availability over this region. Over Delhi, the Noah LSM and RUC LSM show relatively over‐predicting trend in the last 24 h of the simulation in the summer case (Fig. 3a) and during both the days of winter case (Fig. 3b). However, in case of other two cities (i.e. Ahmedabad and Jodhpur), all of the LSMs show under‐ prediction during both summer (Fig. 3c and e) and winter (Fig. 3d and f) cases. In order to examine the hypothesis that the differences between the values of RMSE in CONTROL and other simulations are significantly different from zero at 95% confidence interval, the significance t-test in line with the bootstrap re-sampling technique (Chang and Hanna, 2004) is carried out. The significance test indicates that these differences, for different turbulent parameterization schemes coupled with Noah LSM, over all the three cities (Table 4) for summer case are not significantly different from zero except over Ahmedabad with respect to YSU+NOAH simulation and Jodhpur with respect to MRF+NOAH simulation. However, the corresponding differences in RMSE values among CONTROL, MYJ + THD and MYJ + RUC are significantly different from zero over all the cities in summer case (Table 5). Similarly, the differences between the RMSE values of CONTROL simulation with respect to YSU + NOAH and MRF+ NOAH simulation are not significantly different from zero (Table 4) except over Delhi during winter case. On the other hand, the corresponding differences with respect to LSMs (i.e. MYJ + THD and MYJ + RUC simulations) for a given PBL scheme (i.e. MYJ PBL) are found to be significantly different over Ahmedabad and Jodhpur in contrast to Delhi (Table 5) during this case. This can be interpreted as if RMSE of a simulation is comparable to the corresponding CONTROL simulation and statistically there is no significant difference between RMSE's, the performance of the scheme can be taken as that of CONTROL. From the above discussions, it is evident that the performance of Noah LSM is relatively better as compared to that of RUC LSM and 5-layer thermal diffusion scheme in

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Fig. 3. Diurnal variation of surface temperature over three north Indian cities with land-surface schemes: (a) Delhi during summer case, (b) Delhi during winter case, (c) Ahmedabad during summer case, (d) Ahmedabad during winter case, (e) Jodhpur during summer case and (f) Jodhpur during winter case. Here ─── CONTROL (MYJ+ NOAH) simulation; — × —× — MYJ + RUC simulation; - -Δ- - -Δ- - - MYJ +THD simulation; ● ● ● observations from IMD.

predicting the near surface (i.e. 2 m) air temperature in both summer and winter cases. The performance of MYJ PBL is quite reasonable as when compared to YSU and MRF schemes. 4.2. Surface wind speed The diurnal variation of simulated and observed surface wind speeds (i.e. 10 m wind speeds) indicates a relatively poor performance of the model with any of PBL and LSM parameterization schemes in both summer and winter cases (Fig. 4). All of the simulations follow almost similar trend. The computed surface wind speeds are found to be positively correlated with those observed in the summer case with all PBL and LSM schemes (values not given for sake of brevity). Over Jodhpur, the computed surface wind speeds are found to be

negatively correlated with the observations in winter case in all the simulations whereas over Ahmedabad, calculated winds are positively correlated with the observed ones with all the schemes except the one with MYJ+NOAH. Similarly, the positive correlation is also found over Delhi in all simulations except MYJ+THD simulation in winter case (values not given for sake of brevity). However, they do not agree with the actual observations as indicated by the values of RMSE (Table 2). Even though the performance of MYJ PBL coupled with Noah LSM (i.e. CONTROL) simulation seems to be relatively better in predicting the trend of the surface wind over most of the places of north Indian region, none of the experiments show a higher degree of agreement with the observations as seen in case of surface temperatures during both the cases of summer and winter (Table 2). For example, the observed

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Fig. 4. Diurnal variation of surface wind speed over three north Indian cities with PBL schemes and LSMs: (a) Delhi during summer case, (b) Delhi during winter case, (c) Ahmedabad during summer case, (d) Ahmedabad during wither case, (e) Jodhpur during summer case and (f) Jodhpur during winter case. Here ─── CONTROL (MYJ+NOAH) simulation; ——— MRF + NOAH simulation; —————— YSU +NOAH simulation; — × — ×— MYJ + RUC simulation; - -Δ- - -Δ- - - MYJ + THD simulation; ● ● ● observations from IMD.

wind speed in the winter case is often 0 ms − 1, whereas the corresponding computed values are non-zero (Fig. 4). The reason for such a discrepancy could be attributed to the poor initial and boundary conditions. The model initial conditions may be improved using a data assimilation technique and would be studied in the future. These results suggest that the representation of wind speed is still uncertain using the state-of-the-art PBL schemes even though the representation of surface temperature is reasonable. A similar result was obtained by Shin and Hong (2011) for the single day case study using WRF modeling system and CASES-99 data (http://www.cora. nwra.com/cases/CASES-99.html). The computed values of RMSE (Table 2) indicate that the wind is poorly predicted by the model over all the three cities

with all PBL and land-surface schemes. The significance test carried out using the observed and computed wind speeds indicates that the values of RMSE in CONTROL simulation are not significantly different at 95% confidence intervals from that of YSU+NOAH and MRF+NOAH simulations during summer case over all the cities (Table 4). However, exceptions are found over Jodhpur with respect to MYJ+THD simulation and over Ahmedabad with respect to MYJ+RUC simulation (Table 5) when the comparison is made in relation to the performance of three LSMs (i.e. Noah, THD and RUC). In the winter case, the differences in RMSE values of CONTROL simulation with respect to that of YSU+NOAH and MRF+NOAH simulations are significantly different from zero over all the cities except over Ahmedabad (Table 4). However, the corresponding differences

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Fig. 5. Diurnal variation of sensible heat flux over three north Indian cities with land-surface schemes: (a) Delhi during summer case, (b) Delhi during winter case, (c) Ahmedabad during summer case, (d) Ahmedabad during winter case, (e) Jodhpur during summer case and (f) Jodhpur during winter case. Here ─── CONTROL (MYJ + NOAH) simulation; — × — × — MYJ + RUC simulation; - -Δ- - -Δ- - - MYJ + THD simulation.

with respect to LSMs are not significantly different from zero except over Ahmedabad and Jodhpur with respect to MYJ+RUC simulation (Table 5). 4.3. Surface sensible and latent heat fluxes The magnitudes of both sensible (Fig. 5) and latent heat (Fig. 6) fluxes vary with the use of different land-surface parameterizations. The THD LSM predicts lowest values of maximum sensible heat flux in both summer and winter cases (Fig. 5) and the values of latent heat fluxes over all the cities during both summer and winter cases are relatively smaller in magnitude from Noah LSM (Fig. 6). The reasons for these are not clear to us at present. During summer case, the values of latent heat flux in the day time from THD LSM are up to a factor of 10 times larger

than those from Noah LSM whereas the values from RUC LSM lie in between these two (Fig. 6a, c and e). However, during winter case, the values of day time latent heat flux from THD LSM are nearly 3 times larger than those from Noah LSM and the corresponding values from RUC LSM mostly lie in between these two (Fig. 6b, d and f). Further, the values of latent heat fluxes from RUC LSM show a large difference between day 1 and day 2 during the summer case (Fig. 6a, c, e). This indicates that some sort of soil moisture adjustment is occurring after day1, possibly due to incompatible initial soil properties. The values of sensible heat flux in winter case computed from Noah LSM are nearly three times larger as compared to that of THD LSM and the corresponding values from RUC LSM lie in between (Fig. 5b, d and f). On the other hand, during summer case, the values of sensible heat flux from RUC LSM

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Fig. 6. Diurnal variation of latent heat flux over three north Indian cities with land-surface schemes: (a) Delhi during summer case, (b) Delhi during winter case, (c) Ahmedabad during summer case, (d) Ahmedabad during winter case, (e) Jodhpur during summer case and (f) Jodhpur during winter case. Here ─── CONTROL (MYJ + NOAH) simulation; — × — × — MYJ + RUC simulation; - -Δ- - -Δ- - - MYJ + THD simulation.

are nearly two times larger as compared to those computed by THD LSM whereas the corresponding values from Noah LSM are comparable to those from RUC LSM (Fig. 5a, c and e). The diurnal variation of sensible and latent heat fluxes over the cities Delhi, Ahmedabad and Jodhpur for the simulations YSU + NOAH, CONTROL and MRF + NOAH indicates that the magnitudes of these surface fluxes with a particular LSM (i.e. Noah LSM) remain almost similar over a particular place in both summer and winter cases (figure not shown for brevity) implying that the prediction of surface fluxes has negligible impact of PBL parameterizations. It is because the surface fluxes from the same LSM are used as the lower boundary condition for the PBL physics. It is quite difficult to state the physical reasons for the performance of the LSMs at this juncture since the observations were not available for comparison with the computed fluxes. However, in view of the studies by Chen et al. (2010), Noah LSM

overestimates the sensible heat flux during day time over an arid or semi-arid region and the results from this study also indicate the same. A recent study by Patil et al. (2011) also shows that Noah LSM overestimates the sensible heat flux during day time whereas underestimates it during nighttime for a wet period. It also further shows that the estimated values of sensible heat flux from Noah LSM are relatively larger during dry conditions specific to a site in the western parts of India. Similarly, the reason of smaller values of latent heat fluxes from Noah LSM could be attributed to the geographical location of the cities. It may be noted that the cities are located in a semiarid region and the earlier studies of Hogue et al. (2005) found discrepancies in the prediction of latent heat flux values by Noah LSM over semi-arid regions. It is because Noah LSM depends upon the greenness fraction and the model may not be able to adjust to the abrupt changes in vegetation response (Kurkowski et al., 2003).

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The problems with the computation of lower values of sensible heat and higher values of latent heat fluxes from THD case indicate a behavior as if the soil moisture content is quite high over these places, which consequently gives an incorrect Bowen ratio. This implies to the inadequacy of the THD scheme in its default mode, and the tunable soil moisture availability in that scheme should be able to correct for much of this problem. In order to address this issue, three sensitivity tests were carried out with 30%, 50% and 70% of the initial soil moisture values as that of the default mode for THD scheme. However, it was found that the sensible and latent heat fluxes remain unchanged as that of the default simulation with THD scheme. Therefore, further investigations were made by analyzing the initial values of soil moisture while considering all the three LSMs. It was noticed that the model considers different nonzero initial soil moisture values for both RUC (Smirnova et al., 1997) and Noah LSMs as these are further used in the computation of soil temperature and other surface fluxes (e.g. Hong et al., 2009). However, the model takes into account zero initial soil moisture values and do not consider it as a variable in the parameterization according to the physics of THD scheme; since it assumes a constant soil moisture value depending upon the land-use type (Sub-section 2.1). In the cases simulated for the present study, the soil moisture happens to be zero for THD scheme and would not be reduced further in order to initialize or make it available as a lower value. Therefore, the soil moisture availability may not be the appropriate cause for the lower values of sensible heat and higher values of latent heat fluxes computed using THD LSM in the present cases. It would need further investigation in order to find out the appropriate reason. The differences in the computation of sensible and latent heat fluxes from the RUC and Noah LSMs may be attributed to the different values of initial soil moisture content and the physics of the LSMs. The studies on land-surface processes (Hong et al., 2009) and numerical weather forecasting (Anantharaj et al., 2008) using Noah LSM indicate that the heterogeneity in vegetation has a significant impact on the land-surface processes and soil moisture variability has reasonable impact on the evolution of atmospheric boundary layer and associated weather. However, explicit studies using RUC LSM are limited in literature. The results of Hill and Lackmann (2009) imply that the values of surface latent heat fluxes from the simulation using MYJ PBL are larger as compared to those obtained using YSU PBL parameterization with the same wind speeds and the difference increases with wind speed. According to their study, the reason of predicting larger surface latent heat flux is due to the computation of larger exchange coefficient for moisture in the surface layer scheme used in line with MYJ PBL parameterization whereas the similarity principle used in line with YSU scheme predicts the surface exchange coefficient for moisture being consistent with observations. In the present study, significant differences in computed values of surface fluxes are realized with different land-surface parameterizations (i.e. Noah LSM (in CONTROL simulation), THD LSM (in MYJ+THD simulation) and RUC LSM (in MYJ+RUC simulation)) during both summer and winter cases. Unlike the study of Hill and Lackmann (2009), the values of surface latent heat fluxes with the same wind speeds (with the same initial and lateral boundary conditions) are found to be almost similar when the same LSM is used with different PBL schemes (figure not shown for brevity).

In view of the above findings, it is quite reasonable to use Noah and RUC LSMs as compared to the 5-layer thermal diffusion scheme for inclusion of land-surface variables and surface fluxes. However, Noah LSM needs further improvement for the representation of these parameters over a tropical semiarid region (Patil et al., 2011) like that of north India, even though it is used extensively for urban boundary layer studies worldwide (e.g. Miao et al., 2009; Shem and Shepherd, 2009; Tewari et al., 2010). 4.4. Potential temperature profiles The vertical profiles of potential temperature are shown in Figs. 7–8 for those hours when observational data are available. The qualitative inter-comparison of the results from different PBL parameterization schemes for a given LSM (i.e. Noah LSM) implies that all of these behave similarly for both summer and winter cases (figures not shown for brevity). The trends of the vertical profiles are relatively closer with the observations during winter case (figure not shown for brevity). However, the computed values of RMSE indicate that the results from CONTROL simulation are comparable to that of YSU + NOAH and MRF + NOAH over all the cities in winter case and over Ahmedabad during summer case (Table 3). On the other hand, the errors are relatively less over Ahmedabad and Jodhpur in CONTROL simulation as compared to MYJ + THD and MYJ+ RUC simulations during winter case (Table 3). The vertical potential profiles (Figs. 7–8) are influenced by the land-surface schemes within the boundary layer in both summer and winter cases. The effect is more pronounced during the summer (Fig. 7) in comparison to winter (Fig. 8). Even for summer or winter, the impact is observed more during the day compared to night time. The values of potential temperatures within ABL are mostly under‐predicted by THD LSM and the computed profiles from Noah LSM are relatively closer to the observations in most of the situations. This result is supported by the computed values of RMSE (Table 3) using the observed and corresponding model predicted potential temperatures at standard pressure levels within 2 km from the surface. In most of the cases, the observations are closer to the simulated profiles away from the surface. The discrepancy near the surface may be attributed to atmospheric stability, which the model is not able to determine accurately. It may be noted that the determination of atmospheric stability needs appropriate consideration of mixing ratio (Friedrich et al., 2012), which is quite important for an unstable atmosphere. The significance test indicates that the computed RMSE from CONTROL simulation is significantly different at 95% confidence interval from those with YSU+NOAH and MRF+NOAH simulations over all the cities during summer case (Table 4) except over Ahmedabad (with respect to that of YSU+NOAH simulation) and Delhi (with respect to that of MRF+NOAH simulation). However, the differences in RMSE values between CONTROL and MYJ+THD simulation are significantly different from zero whereas the corresponding differences with respect to MYJ+RUC simulation are not significantly different from zero (Table 5). In winter case, the differences in RMSE values of CONTROL simulation and that of YSU+NOAH and MRF+NOAH simulations are significantly different from zero except over

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Fig. 7. Vertical profiles of potential temperature during summer case over three north Indian cities with land-surface schemes: (a) Delhi at 00UTC on 21 May, (b) Delhi at 12UTC on 21 May, (c) Ahmedabad at 12UTC on 20 May, (d) Ahmedabad at 00UTC on 21 May, (e) Jodhpur at 12UTC on 20 May and (f) Jodhpur at 00UTC on 21 May, 2005. Here ─── CONTROL (MYJ+NOAH) simulation; —×—×— MYJ+RUC simulation; - -Δ- - -Δ- - - MYJ+THD simulation; ● ● ● observations from Wyoming Weather Web.

Delhi (Table 4). On the other hand, the corresponding differences are found to be significantly different from zero over Delhi and Ahmedabad when considered with respect to THD and RUC LSMs (Table 5). 4.5. Wind profiles PBL scheme parameterizes the turbulent diffusion in the boundary layer and thus, influences the vertical profiles of wind (Figs. 9–10). The effect is more realized during summer case (Fig. 9) in comparison to winter case (Fig. 10). The

vertical variation of observed wind speeds over all the three cities Delhi, Ahmedabad and Jodhpur is not well-captured by the model with any of PBL and LSM schemes both in summer and winter cases. However, the qualitative analysis shows that the model results are relatively closer to the observations during winter case (Fig. 10) as compared to the summer case (Fig. 9). It is probably because of the relatively stable environment during winter case as compared to the summer case and the model is not able to capture the significant variability in wind speed during summer case, where the atmosphere is relatively unstable. This is because the wind

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Table 3 Root Mean Square Error (RMSE) of computed potential temperature and wind speed profiles with respect to IMD observations during summer and winter cases. Cities

MYJ + THD simulation

MYJ + RUC simulation

RMSE for potential temperature profiles in summer (May 20–22) case Delhi 2.16 1.74 2.02 Ahmedabad 3.09 3.19 3.29 Jodhpur 2.62 2.41 2.73

YSU + NOAH simulation

MRF + NOAH simulation

CONTROL (MYJ + NOAH) simulation

5.27 5.07 4.16

1.98 3.07 2.98

RMSE for potential temperature profiles in winter (December 09–11) case Delhi 2.09 2.11 2.29 Ahmedabad 3.87 3.82 3.91 Jodhpur 1.35 1.36 1.32

1.96 5.93 1.48

1.94 5.07 1.52

RMSE for wind speed profiles in summer (May 20–22) case Delhi 4.43 3.71 Ahmedabad 2.72 2.74 Jodhpur 4.93 4.53

4.52 2.70 6.26

3.73 3.43 6.20

4.59 3.16 3.76

RMSE for wind speed profiles in winter (December 09–11) case Delhi 1.69 1.72 Ahmedabad 1.91 1.97 Jodhpur 4.95 4.90

1.53 2.01 4.73

1.33 1.80 4.78

1.32 1.96 4.78

speed is largely dependent upon the vertical diffusion formulations in the convective regime (Shin and Hong, 2011). A qualitative analysis of wind profiles implies that the MYJ PBL scheme performs better in most of the situations as compared to the MRF and YSU schemes over these cities. Near the surface, the results from Noah LSM are comparable to RUC LSM and THD LSM during winter case (Fig. 10). It may be because the near-surface variability is largely governed by the surface-layer formulations (Shin and Hong, 2011) as compared to the PBL mixing algorithms. However, in summer case, the wind profiles are qualitatively captured by CONTROL, MYJ+THD and MYJ+RUC simulations (Fig. 9). The quantitative comparison using the computed values of RMSE (Table 3) shows that the errors from CONTROL simulation are either relatively small or comparable during the winter case as compared to that of YSU +NOAH and MRF+NOAH simulations. However, the values of RMSE from CONTROL simulation are relatively smaller over Ahmedabad during both the simulations. This is because the MRF and YSU PBL schemes are non-local closure schemes, which are usually favorable in unstable conditions whereas MYJ PBL is a local TKE closure scheme, which shows better performance in stable conditions (Shin and Hong, 2011). The significance test indicates that there is no significant difference between the RMSE values of CONTROL, YSU+ NOAH and MRF+NOAH simulations over all the cities in both summer and winter cases (Table 4). However, the corresponding differences with respect to MYJ+THD and MYJ+RUC simulations are significantly different from zero over Ahmedabad in the summer case and over Delhi in the winter case (Table 5). 4.6. Regional scale circulation The development of regional scale flow over north India is shown in Figs. 11–14 during both summer and winter cases. The comparison of flow patterns at 06 UTC on May 20, 2005 indicates that the anti-cyclonic low prevailing in FNL analysis is also simulated during all experiments for the summer case (Fig. 11). While, the intensity of this anti-cyclonic low centered at (29.1°N, 73.1°E) in FNL analysis is hardly affected due to change of PBL parameterizations (Fig. 11a–d), the intensity

decreases when THD LSM is used (Fig. 11e). However, the intensity of this anti-cyclonic low is not significantly influenced by changing the Noah LSM (Fig. 11b) to RUC (Fig. 11f). The dynamic convergence over Indo-Gangetic plane is stronger in CONTROL simulation (Fig. 11b) as compared to MRF+NOAH (Fig. 11c) and YSU+NOAH (Fig. 11d) which was initially weaker in the FNL analysis (Fig. 11a). Similarly, the flow over IndoGangetic plane is stronger from CONTROL simulation as compared to others when the LSM is kept constant (Fig. 11a–d). If the Noah LSM is changed to RUC, the wind speed further increase over the Indo-Gangetic plane and eventually the dynamic convergence becomes stronger over the region (Fig. 11f). At 12 UTC on May 20 (figures not shown for brevity), the FNL analysis does not show any anti-cyclonic circulation as it was seen at 06 UTC. However, the effect of this anti-cyclonic circulation still prevails in CONTROL simulation even though it is weakened after 6 h and shifted its center northwards slightly. This is weakening further if the MRF and YSU schemes are used in place of MYJ PBL scheme. With the use of THD LSM, the center of the weaker anti-cyclonic circulation shifts toward south east side unrealistically. If RUC LSM is used, the weaker anticyclonic circulation still exists with its center shifting slightly toward north like CONTROL simulation. The flow pattern on the second day of simulation during summer case changes its course and a relatively weaker anticyclonic circulation prevails over Indo-Gangetic plane in FNL analysis at 06 UTC on May 21, 2005 (Fig. 12a). However, this pattern is stronger and over a relatively larger region in CONTROL simulation (Fig. 12b). This is still persistent even though MRF and YSU schemes are used, which show the regional circulation to be over a smaller area as compared to that of the CONTROL simulation (Fig. 12a–d). This pattern is also captured well by MYJ + RUC experiment in contrast to MYJ + THD simulation (Fig. 12e–f). During winter case, the flow patterns do not show much change as when compared with the FNL analysis even though different combinations of PBL and LSM parameterizations are used (figures not shown for brevity). It might be because of

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Fig. 8. Vertical profiles of potential temperature during winter case over three north Indian cities with land-surface schemes: (a) Delhi at 12UTC on 09 December, (b) Delhi at 00UTC on 10 December, (c) Ahmedabad at 12UTC on 09 December, (d) Ahmedabad at 00UTC on 10 December, (e) Jodhpur at 12UTC on 09 December and (f) Jodhpur at 00UTC on 10 December, 2004. Here ─── CONTROL (MYJ+NOAH) simulation; —×—×— MYJ+RUC simulation; - -Δ- - -Δ- - - MYJ+THD simulation; ● ● ● observations.

the existing stable conditions during winter though a little change is observed in flow patterns due to the alteration in parameterizations. On the other hand, the summer time flow patterns at the surface are relatively more sensitive to the LSMs than to the PBL parameterizations (e.g. Figs. 11–12).

surface parameterizations in the transport of dust particles from Thar region toward Delhi in the CBL. The position of a particle (x(t + Δt), y(t + Δt), z(t + Δt)) at time (t + Δt) from its position (x(t), y(t), z(t)) at time t is given by: )

4.7. Flow trajectories

xðt þ Δt Þ ¼ xðt Þ þ uðt ÞΔt yðt þ Δt Þ ¼ yðt Þ þ vðt ÞΔt zðt þ Δt Þ ¼ zðt Þ þ wðt ÞΔt

The trajectories of the air parcels are drawn on the basis of flow patterns to examine the possible role of the PBL and land-

Here u, v and w are the velocity components. Since the aim is to show the regional scale trajectories, the influence

:

ð8Þ

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Table 4 Results of significance testing to determine whether the difference in RMSE values between the CONTROL (MYJ + NOAH), YSU + NOAH and MRF + NOAH experiments for (i) surface temperature variation, (ii) surface wind variation, (iii) potential temperature profiles and (iv) wind speed profiles are significantly different from zero. An ‘x’ indicates that the results are significant at 95% confidence intervals. Cases

Experiments

Cities

Summer case (i.e. May 20–22, 2005)

YSU + NOAH

Delhi Ahmedabad Jodhpur Delhi Ahmedabad Jodhpur Delhi Ahmedabad Jodhpur Delhi Ahmedabad Jodhpur

MRF + NOAH Winter case (i.e. December 09–11, 2004)

YSU + NOAH MRF + NOAH

For surface temperature

For surface wind speed

x

For potential temperature profiles

For wind speed profiles

x x

x x

x

x

x

x x

x

x

vicinity of Delhi whereas the trajectory obtained with THD LSM deviates away in the north–west direction of Delhi region. Fig. 13b indicates that the trajectories originating from the point ‘Y’ (27°N, 72°E) are almost similar with respect to PBL schemes. The corresponding trajectory with THD LSM is reaching in the regions closer to Delhi. On the other hand, all of the trajectories from the point “Z” (28°N, 72.5°E) are going toward Delhi and its surroundings except the one from MYJ+ THD. Our earlier study on regional scale boundary layer characteristics (Panda et al., 2009) implies that thermally driven regional circulations play a major role in the transport of particulate matter from the Thar Desert to Delhi and its neighboring regions during summer. However, the present study based on the sensitivity experiments with respect to land-surface and boundary layer parameterizations suggests that the role of the PBL schemes and LSMs in the regional scale transport of Thar dusts depends upon the point of origin of these particulate matters. In contrast to summer, the winter case trajectories (Fig. 14) emanating from the points taken in Thar region are in the direction opposite to that of the summer case (Fig. 13) implying that the dust particles from Thar region do not reach to Delhi and its neighboring region during the winter case due to the contrasting flow patterns observed in winter (figures not shown for brevity).

of turbulence in computing the possible positions of dust particles has been neglected. The trajectories are drawn using the six hourly horizontal velocity components at 10 m from the model. Analysis of surface variables (Figs. 2–6), profiles of potential temperature and wind speeds (Figs. 7–10) over three cities and the surface flow fields (e.g. Figs. 11–12) over north Indian region indicates that the land-surface parameterizations have a significant influence on the boundary layer characteristics at the surface or within 1 km from the surface as compared to the PBL schemes. In order to analyze the influence of the LSM and PBL parameterizations on the movement of the air parcels, the forward trajectories from three different locations of Thar region such as X (26.18°N, 73.04°E), Y (27°N, 72°E) and Z (28°N, 72.5°E) are shown in Figs. 13–14, from various simulations. For this purpose, a small region with latitudes 22°N–33°N and longitudes 69°E–82°E from the simulated domain (Fig. 1) is considered. During summer case, the trajectory originating from ‘X’ (Fig. 13a) in CONTROL simulation reaches in the vicinity of Delhi. However, the corresponding trajectories in YSU+NOAH and MRF+NOAH simulations are almost similar to that of CONTROL one up to a latitude of 76°N beyond which they deviate away from Delhi due to the changes in local/regional flow patterns. On the other hand, the corresponding trajectory with RUC LSM reaches in the

Table 5 Results of significance testing to determine whether the difference in RMSE values between the CONTROL (MYJ + NOAH) and MYJ + THD and MYJ + RUC experiments for (i) surface temperature variation, (ii) surface wind variation, (iii) potential temperature profiles and (iv) wind speed profiles are significantly different from zero. An ‘x’ indicates that the results are significant at 95% confidence intervals. Cases

Experiments

Cities

For surface temperature

Summer case (i.e. May 20–22, 2005)

MYJ + THD

Delhi Ahmedabad Jodhpur Delhi Ahmedabad Jodhpur Delhi Ahmedabad Jodhpur Delhi Ahmedabad Jodhpur

x x x x x x

MYJ + RUC

Winter case (i.e. December 09–11, 2004)

MYJ + THD

MYJ + RUC

For surface wind speed

For potential temperature profiles

For wind speed profiles x

x

x x x

x

x x x x

x x

x x x

x

x x

x

J. Panda, M. Sharan / Atmospheric Research 112 (2012) 89–111

(b)2000

DELHI: 00UTC 21 MAY 2005

1750

1750

1500

1500

Height (m)

Height (m)

(a) 2000

1250 1000 750

DELHI: 12UTC 21 MAY 05

1250 1000 750

500

500

250

250

0

0 0

3

6

9

12

0

15

3

6

(d)2000

AHMEDABAD: 12UTC 20 MAY 2005

2000

Height (m)

Height (m)

15

AHMEDABAD: 00UTC 21 MAY 2005

1500

1500 1250 1000 750

1250 1000 750

500

500

250

250 0

3

6

9

12

0

15

0

2

4

Speed (m/s)

(e) 2000

(f) 2000

JODHPUR: 12UTC 20 MAY 2005

1750

1750

1500

1500

1250 1000 750

250

0 9

12

14

12

Speed (m/s)

15

JODHPUR: 00UTC 21 MAY 2005

750

250 6

10

1000

500

3

8

1250

500

0

6

Speed (m/s)

Height (m)

Height (m)

12

1750

1750

0

9

Speed (m/s)

Speed (m/s)

(c)

105

0

0

3

6

9

12

15

Speed (m/s)

Fig. 9. Vertical profiles of wind speed during summer case over three north Indian cities with PBL and land-surface schemes: (a) Delhi at 00UTC on 21 May, (b) Delhi at 12UTC on 21 May, (c) Ahmedabad at 12UTC on 20 May, (d) Ahmedabad at 00UTC on 21 May, (e) Jodhpur at 12UTC on 20 May and (f) Jodhpur at 00UTC on 21 May, 2005. Here ─── CONTROL (MYJ+NOAH) simulation; ——— MRF +NOAH simulation; —————— YSU + NOAH simulation; —×—× — MYJ+RUC simulation; - -Δ- - Δ- - - MYJ+ THD simulation; ● ● ● observations from Wyoming Weather Web.

5. Concluding remarks The primary objective of the present study is to examine the influence of PBL and land-surface parameterizations available in WRF modeling system (version 2.1) on the regional scale boundary layer characteristics and associated transport over northern India during two contrasting cases of summer and winter. For this purpose, a domain containing the Indo-Gangetic plane, Thar Desert, north Indian cities like Delhi, Ahmedabad and Jodhpur (Fig. 1) is considered. Each of

the cases is simulated for 48 h initializing the model at 00 UTC for all experiments carried out in the present study. Five sets of experiments (Table 1) are designed for each of the cases by using various PBL and LSM parameterizations available in WRF modeling system version 2.1. The results from these experiments are analyzed both qualitatively and quantitatively in each of the cases. These results indicate that the PBL schemes do not have significant influence on the surface layer variables as compared to that of LSMs. Further, MYJ PBL coupled with Noah LSM

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J. Panda, M. Sharan / Atmospheric Research 112 (2012) 89–111 DELHI: 12UTC 09DEC 2004

(b) 2000

1800

1800

1600

1600

1400

1400

Height (m)

Height (m)

(a) 2000

1200 1000 800

1200 1000 800

600

600

400

400

200

200

0

0

Speed (m/s) AHMEDABAD: 12UTC 09DEC 2004

2000

Speed (m/s)

(d)

AHMEDABAD: 00UTC 10DEC 2004

2000

1800

1800

1600

1600

1400

1400

Height (m)

Height (m)

(c)

1200 1000 800

1200 1000 800

600

600

400

400

200

200

0

0

Speed (m/s) JODHPUR: 12UTC 09DEC 2004

Speed (m/s)

(f)

JODHPUR: 00UTC 10DEC 2004

2000

1800

1800

1600

1600

1400

1400

Height (m)

Height (m)

(e) 2000

DELHI: 00UTC 10DEC 2004

1200 1000 800

1200 1000 800

600

600

400

400

200

200

0

0

Speed (m/s)

Speed (m/s)

Fig. 10. Vertical profiles of wind speed during winter case over three north Indian cities with PBL and land-surface schemes: (a) Delhi at 12UTC on 09 December, (b) Delhi at 00UTC on 10 December, (c) Ahmedabad at 12UTC on 09 December, (d) Ahmedabad at 00UTC on 10 December, (e) Jodhpur at 12UTC on 09 December and (f) Jodhpur at 00UTC on 10 December, 2004. Here ─── CONTROL (MYJ + NOAH) simulation; ——— MRF + NOAH simulation; —————— YSU + NOAH simulation; — × — × — MYJ + RUC simulation; - -Δ- - -Δ- - - MYJ + THD simulation; ● ● ● observations.

performs relatively better in predicting the surface and boundary layer variables over the cities Delhi, Ahmedabad and Jodhpur. However, relatively poor performance of the model is observed with any of the PBL and LSM parameterization schemes in predicting the diurnal variation of surface wind and vertical profiles of wind speed during both of the cases. The results from the present study indicate that the consideration of MYJ and YSU boundary layer schemes is quite reasonable for the prediction of surface and boundary layer variables in a regional scale during

both stable and unstable situations. However, the studies of Shin and Hong (2011) suggest that the local TKE closure schemes (e.g. MYJ scheme) perform better in stable conditions whereas a non-local scheme with the entrainment flux proportional to the surface flux (e.g. YSU scheme) is favorable for unstable conditions. Another study by Hu et al. (2010) also suggested earlier that MYJ scheme produces larger bias in the lower atmosphere as compared to that of YSU scheme. The studies by Gibbs et al. (2011) show that the differences in predictions

J. Panda, M. Sharan / Atmospheric Research 112 (2012) 89–111

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Fig. 11. Surface flow (10 m wind) fields during summer case at 06 UTC, May 20, 2005: (a) from NCEP analysis, (b) from CONTROL simulation, (c) from MRF + NOAH simulation, (d) from YSU+ NOAH simulation, (e) from MYJ + THD simulation and (f) from MYJ + RUC simulation.

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Fig. 12. Surface flow (10 m wind) fields during summer case at 06 UTC, May 21, 2005: (a) from NCEP analysis, (b) from CONTROL simulation, (c) from MRF + NOAH simulation, (d) from YSU + NOAH simulation, (e) from MYJ + THD simulation and (f) from MYJ + RUC simulation.

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(a) 33 32 31

YSU+NOAH MYJ+THD DELHI

CONTROL MYJ+RUC

109

MRF+NOAH X

Latitude

30 29 28 27 26 25 24 23 22

Longitude

(b) 33 Y

32 31

Latitude

30 29 28 27 26 25 24 23 22

Longitude

(c) 33 32

Z

31

Latitude

30 29 28 27 26 25 24 23 22

Longitude Fig. 13. Near surface forward trajectories from three places of Thar Desert during summer case originating at: (a) X (26.18°N, 73.04°E), (b) Y (27°N, 72°E) and (c) Z (28°N, 72.5°E). Here ─── YSU +NOAH simulation; —— CONTROL (MYJ+NOAH) simulation; ─ ─ MRF+NOAH simulation; ——— MYJ+THD simulation; — — MYJ+RUC simulation; ♦ Delhi; ■ X (Jodhpur); ● Y; ▲Z. The latitudes and longitudes are considered in northern hemisphere.

from MYJ and YSU schemes to be quite small though there is a better matching of model results from non-local schemes with the observational data. On the other hand, MRF scheme does not contain the entrainment effect and YSU scheme is an advanced version of MRF, which already includes this effect. Since the surface fluxes from the land-surface parameterization are used as lower boundary conditions for the PBL physics, magnitudes of these surface fluxes with a particular

LSM remain similar with different PBL parameterization schemes. However, significant difference in computed values of surface fluxes is realized with different land-surface parameterizations over the three cities considered in the present study. With the availability of turbulence observations, the computed surface fluxes can be compared with those observed and accordingly they may be improved further. Nevertheless, the land-surface parameterizations for

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33 32 31

YSU+NOAH MYJ+THD DELHI

CONTROL MYJ+RUC Z

MRF+NOAH X Y

30

Latitude

29 28 27 26 25 24 23 22

Longitude Fig. 14. Near surface forward trajectories from three places of Thar Desert during winter case. Here YSU + NOAH simulation; ——— CONTROL (MYJ + NOAH) simulation; ——— MRF + NOAH simulation; —————— MYJ + THD simulation; — — — MYJ + RUC simulation; ♦ Delhi; ■ X (26.18°N, 73.04°E), ● Y (27°N, 72°E); ▲ Z (28°N, 72.5°E). Here, ‘X’ represents Jodhpur and the latitudes and longitudes are considered in northern hemisphere.

tropical semi-arid regions require further studies to improve the representation of surface fluxes (Patil et al., 2011). In view of this study, the use of Noah and RUC LSM within the WRF modeling system is reasonable in scope for mesoscale or regional scale studies. On the other hand, the studies of Chang et al. (2009) found that the relatively simple THD scheme performed better as compared to the other two versions of Noah LSM (i.e. the Noah and a modified version of it with a photosynthesis module) and therefore, suggested the need for its improvement in order to use it in the Indian context. While Noah LSM has a greater scope for urban boundary layer modeling studies because of the special consideration of urban effects and a scope for coupling with several urban canopy models in the newer versions, RUC LSM is not widely used. During the winter case, the flow patterns are not sensitive to different combinations of PBL and LSM parameterizations. In contrast, the surface flow patterns in the summer case are largely sensitive to the land-surface parameterizations as compared to the PBL schemes. Accordingly, the trajectories from three places of Thar region indicate that the role of the PBL and land-surface parameterizations in the regional scale transport of Thar dusts depends upon their point of origin during summer. However, the trajectories during the winter case are completely opposite because of the contrasting nature of the flow patterns and consequently, the formation of haze-like events due to Thar dusts is not expected. Even though additional cases for summer (e.g. May 26–28, 2005) and winter (e.g. December 11–12, 2004) were also considered for simulation, the results from May 20–22, 2005 and December 09–11, 2004 cases are discussed in this paper for the sake of consistency and brevity. We wish to point out that the results computed from the additional simulations are qualitatively similar to those observed in May 20–22, 2005 and December 09–11, 2004 cases for surface parameters and vertical profiles.

The present study is carried out on a single processor IBM server with AIX 5.1 operating system and 2 GB memory using the WRF model version 2.1. The conclusions drawn in this study may be updated with the availability of higher computing power and with the use of latest version of WRF modeling system. Acknowledgments Authors are thankful to Dr. S. G. Gopalakrishnan, Hurricane Research Division, NOAA/AOML/OAR/DOC, Miami, Florida for his valuable suggestions and help. They also thank IMD (New Delhi), Dr. Larry Oolman, and the Wyoming Weather Web for their observational data support. The authors wish to thank the anonymous reviewers for their valuable comments and suggestions. References Anantharaj, A., Mostovoy, G., Fitzpatrick, P.J., 2008. Impact of soil moisture initialization on numerical weather forecasting over the Mississippi Delta region. 88th Annual Meeting of the American Meteorological Society, January 20–24, New Orleans, LA. Betts, A.K., Miller, M.J., 1986. A new convective adjustment scheme. Part II: single column tests using GATE wave, BOMEX, and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc. 112, 693–709. Boybeyi, Z., Raman, Sethu, Zannetti, P., 1995. Numerical investigation of possible role of local meteorology in Bhopal gas accident. Atmos. Environ. 29, 479–496. Chang, J.C., Hanna, S.R., 2004. Air quality model performance evaluation. Meteorol. Atmos. Phys. 87, 1–3. Chang, H.-I., Kumar, A., Niyogi, D., Mohanty, U.C., Chen, F., Dudhia, J., 2009. The role of land surface processes on the mesoscale simulation of the July 26, 2005 heavy rain event over Mumbai, India. Glob. Planet. Chang. 67, 87–103. Chen, F., Dudhia, J., 2001a. Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part 1: model description and implementation. Mon. Weather Rev. 129, 569–585. Chen, F., Dudhia, J., 2001b. Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system, part II: model validation. Mon. Weather Rev. 129, 587–604.

J. Panda, M. Sharan / Atmospheric Research 112 (2012) 89–111 Chen, Y., Yang, K., Zhou, D., Qin, J., Guo, X., 2010. Improving the Noah land surface model in arid regions with an appropriate parameterization of thermal roughness length. J. Hydrometeorol. 11, 995–1006. Dudhia, J., 1989. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci. 46, 3077–3107. Friedrich, K., Lundquist, J.K., Aitken, M., Kalina, E.A., Marshall, R.F., 2012. Stability and turbulence in the atmospheric boundary layer: a comparison of remote sensing and tower observations. Geophys. Res. Lett. 39, L03801, http://dx.doi.org/10.1029/2011GL050413. Gautam, R., Liu, Z., Singh, R.P., Hsu, N.C., 2009a. Two contrasting dust dominant periods over India observed from MODIS and CALIPSO data. Geophys. Res. Lett. 36, L06813, http://dx.doi.org/10.1029/2008GL036967. Gautam, R., Hsu, N.C., Lau, K.-M., Tsay, S.-C., Kafatos, M., 2009b. Enhance premonsoon warming over the Himalayan–Gangetic region from 1979 to 2007. Geophys. Res. Lett. 36, L07704, http://dx.doi.org/10.1029/2009GL037641. Gautam, R., Hsu, N.C., Lau,, K.-M., Kafatos, M., 2009c. Aerosol and rainfall variability over the Indian monsoon region: distributions, trends and coupling. Ann. Geophys. 27, 3691–3703. Gibbs, J.A., Fedorovich, E., Eijk, A.M.J.V., 2011. Evaluating Weather Research and Forecasting (WRF) model predictions of turbulent flow parameters in a dry convective boundary layer. J. Appl. Meteorol. Climatol. 50, 2429–2444. Gilliam, R.C., Pleim, J.E., 2009. Performance assessment of new land surface and planetary boundary layer physics in the WRF–ARW. J. Appl. Meteorol. Climatol. 49, 760–774. Grell, G.A., Dudhia, J., Stauffer, D.R., 1995. A description of fifth generation Penn state/NCAR mesoscale model (MM5). NCAR Technical Note, NCAR/ TN-398+STR. Han, Z., Ueda, H., An, J., 2008. Evaluation and intercomparison of meteorological predictions by five MM5-PBL parameterizations in combination with three land-surface models. Atmos. Environ. 42, 233–249. Hill, K.A., Lackmann, G.M., 2009. Analysis of idealized tropical cyclone simulations using the Weather Research and Forecasting model: sensitivity to turbulence parameterization and grid spacing. Mon. Weather Rev. 137, 745–765. Hogue, T.S., Bastidas, L., Gupta, H., Sorooshian, S., Mitchell, K., Emmerich, W., 2005. Evaluation and transferability of the Noah land surface model in semiarid environments. J. Hydrometeorol. 06, 68–83. Holtslag, A.A., Boville, B.A., 1993. Local versus nonlocal boundary layer diffusion in a global climate model. J. Climate 06, 1825–1842. Hong, S.-Y., Pan, H.L., 1996. Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Weather Rev. 124, 2322–2339. Hong, S.-Y., Noh, Y., Dudhia, J., 2006. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 134, 2318–2341. Hong, S., Lakshmi, V., Small, E.E., Chen, F., Tewari, M., Manning, K.W., 2009. Effects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model. J. Geophys. Res. 114 (D18118), http://dx.doi.org/10.1029/2008JD011249. Hu, X.-M., Gammon, J.W.N., Zhang, F., 2010. Evaluation of three planetary boundary layer schemes in the WRF model. J. Appl. Meteorol. Climatol. 49, 1831–1844. Janjic, Z.I., 1990. The step-mountain coordinate: physical package. Mon. Weather Rev. 118, 1429–1443. Janjic, Z.I., 1994. The step-mountain Eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes. Mon. Weather Rev. 122, 927–945. Janjic, Z.I., 2002. Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note, No. 437. 61 pp. Kurkowski, N.P., Stensrud, D.J., Baldwin, M.E., 2003. Assessment of implementing satellite-derived land cover data in the Eta model. Weather Forecast. 18, 404–416. Lin, C.-Y., Chen, F., Huang, J.C., Chen, W.-C., Liou, Y.-A., Chen, W.-N., Liu, S.-C., 2008. Urban heat island effect and its impact on boundary layer development and land–sea circulation over northern Taiwan. Atmos. Environ. 42, 5635–5649. Louis, J.F., 1979. A parametric model of vertical eddy fluxes in the atmosphere. Bound. Layer Meteorol. 17, 187–202.

111

Mellor, G.L., Yamada, T., 1982. Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys. 20, 851–875. Miao, J.F., Chen, D., Borne, D., 2007. Evaluation and comparison of Noah and Pleim–Xiu land surface models in MM5 using GÖTE2001 data: spatial and temporal variations in near-surface air temperature. J. Appl. Meteorol. Climatol. 46, 1587–1605. Miao, S., Chen, F., LeMone, M.A., Tewari, M., Li, Q., Wang, Y., 2009. An observational and modeling study of characteristics of urban heat island and boundary layer structures in Beijing. J. Appl. Meteorol. Climatol. 48, 484–501. Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., Clough, S.A., 1997. Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res. 102, 16663–16682. Noh, Y., Cheon, W.G., Hong, S.-Y., Raasch, S., 2003. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound. Layer Meteorol. 107, 401–427. Ooyama, K.V., 1990. A thermodynamic foundation for modeling the moist atmosphere. J. Atmos. Sci. 47, 2580–2593. Panda, J., Sharan, M., Gopalakrishnan, S.G., 2009. Regional scale boundary layer characteristics over northern India with special reference to the role of Thar Desert in regional scale transport. J. Appl. Meteorol. Climatol. 48, 2377–2402. Patil, M.N., Waghmare, R.T., Halder, S., Dharmaraj, T., 2011. Performance of Noah land surface model over the tropical semi-arid conditions in western India. Atmos. Res. 99, 85–96. Pielke, R.A., 1974. A three-dimensional numerical model of sea breeze over South Florida. Mon. Weather Rev. 102, 115–139. Prabha, T.V., Hoogenboom, G., Smirnova, T.G., 2011. Role of land surface parameterizations on modeling cold-pooling events and low-level jets. Atmos. Res. 99, 147–161. Rogers, E., Black, T., Ferrier, B., Lin, Y., Parrish, D., DiMego, G., 2001. Changes to the NCEP Meso Eta Analysis and Forecast System: increase in resolution, new cloud microphysics, modified precipitation assimilation, modified 3DVAR analysis, http://www.emc.ncep.noaa.gov/mmb/mmbpll/eta12tpb/ 2001available at. Sanjay, J., 2008. Assessment of atmospheric boundary layer processes represented in the numerical model MM5 for a clear sky day using LASPEX observations. Bound. Layer Meteorol. 129, 159–177. Sharan, M., McNider, R.T., Gopalakrishnan, S.G., Singh, M.P., 1995. Bhopal gas leak: a numerical simulation of episodic dispersion. Atmos. Environ. 29, 2061–2074. Sharan, M., Gopalakrishnan, S.G., McNider, R.T., Singh, M.P., 2000. Bhopal gas leak: a numerical investigation on the possible influence of urban effects on the prevailing meteorological conditions. Atmos. Environ. 34, 539–552. Shem, W., Shepherd, M., 2009. On the impact of urbanization on summertime thunderstorms in Atlanta: two numerical model case studies. Atmos. Res. 92, 172–189. Shin, H.H., Hong, S.-Y., 2011. Intercomparison of planetary boundary layer parameterizations in the WRF model for a single day from CASES-99. Bound. Layer Meteorol. 139, 261–281. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., Powers, J.G., 2005. A description of the advanced research WRF version 2. NCAR Technical Note, NCAR/TN-468+STR. Smirnova, T.G., Brown, J.M., Benjamin, S.G., 1997. Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Weather Rev. 125, 1870–1884. Smirnova, T.G., Brown, J.M., Benjamin, S.G., Kim, D., 2000. Parameterization of cold-season processes in MAPS land-surface scheme. J. Geophys. Res. 105, 4077–4086. Srinivas, C.V., Venkatesan, R., Singh, A.B., 2007. Sensitivity of mesoscale simulations of land–sea breeze to boundary layer turbulence parameterization. Atmos. Environ. 41, 2534–2548. Tewari, M., Kusaka, H., Chen, F., Coirier, W.J., Kim, S., Wyszogrodzki, A.A., Warner, T.T., 2010. Impact of coupling a microscale computational fluid dynamics model with a mesoscale model on urban scale contaminant transport and dispersion. Atmos. Res. 96, 656–664. Troen, I., Mahrt, L., 1986. A simple model of the atmospheric boundary layer: sensitivity to surface evaporation. Bound. Layer Meteorol. 37, 129–148.