The sensitive ecosystem of the central Himalayan (CH) region, which is experiencing
enhanced stress from anthropogenic forcing, requires adequate atmospheric
observations and an improved representation of the Himalaya in the models.
However, the accuracy of atmospheric models remains limited in this region
due to highly complex mountainous topography. This article delineates the
effects of spatial resolution on the modeled meteorology and dynamics over
the CH by utilizing the Weather Research and Forecasting (WRF) model
extensively evaluated against the Ganges Valley Aerosol Experiment (GVAX)
observations during the summer monsoon. The WRF simulation is performed over a
domain (d01) encompassing northern India at 15 km
The Himalayan region is one of the most complex and fragile geographical systems in the world, and it has paramount importance for climatic implications and air composition at the regional to global scales (e.g., Lawrence and Lelieveld, 2010; Pant et al., 2018; Lelieveld et al., 2018). The ground-based observations of meteorology and fine-scale dynamics are highly sparse and limited. In this direction, an intensive field campaign known as the Ganges Valley Aerosol Experiment (GVAX) (Kotamarthi, 2013) was carried out over a mountainous site in the central Himalaya, which provided valuable meteorological observations for atmospheric research, model evaluation, and further improvements. Accurate simulations of meteorology are needed for numerous investigations, such as to study the regional and global climate change, snow cover change, trapping and transport of regional pollution, and the hydrological cycle, especially the monsoon system (e.g., Sharma and Ganju, 2000; Bhutiyani et al., 2007; Pant et al., 2018). Studies focusing on this region have become more important due to increasing anthropogenic influences resulting in enhanced levels of short-lived climate-forcing pollutants (SLCPs) along the Himalayan foothills (e.g., Ojha et al., 2012; Sarangi et al., 2014; Rupakheti et al., 2017; Deep et al., 2019; Ojha et al., 2019). Although global climate models (GCMs) simulate the climate variabilities over the global scale, their application for reproducing observations in regions of complex landscapes is limited due to coarse horizontal resolution (e.g., Wilby et al., 1999; Boyle and Klein, 2010; Tselioudis et al., 2012; Pervez and Henebry, 2014; Meher et al., 2017). Mountain ridges, rapidly changing land cover, and low-altitude valleys often lie within a grid box of typical global climate models, resulting in significant biases in model results when compared with observations (e.g., Ojha et al., 2012; Tiwari et al., 2017; Pant et al., 2018). On the other hand, regional climate models (RCMs) at finer resolutions allow better representation of the topographical features, thus providing improved simulations of the atmospheric variability over regions of complex terrain. Several mesoscale models (e.g., Christensen et al., 1996; Caya and Laprise, 1999; Skamarock et al., 2008; Zadra et al., 2008) have been developed and successfully applied over different parts of the world. These studies have revealed that RCMs provide significantly new insights by parameterizing or explicitly simulating atmospheric processes over finer spatial scales. Nevertheless, large uncertainties are still seen over highly complex areas, indicating the effects of further unresolved terrain features (e.g., Wang et al., 2004; Laprise, 2008; Foley, 2010) and the need to improve the simulations.
Anthropogenic influences and climate forcing have been increasing over the Himalaya and its foothill regions since pre-industrial times (Bonasoni et al., 2012; Srivastava et al., 2014; Kumar et al., 2018). Consequently, an increase in the intensity and frequency of extreme weather events has been observed over the Himalayan region (e.g., Nandargi and Dhar, 2012; Sun et al., 2017; Dimri et al., 2017) in the past few decades. These events include extreme rainfall and resulting flash floods, cloudbursts, and landslides, and the associated weather systems range from mesoscale to synoptic-scale phenomena. Unfortunately, the lack of an observational network covering the Himalaya and foothills with sufficient spatiotemporal density inhibits the detailed understanding of the aforementioned processes as well as meteorological and dynamical conditions in the region. Therefore, the usage of regional models, evaluated against available in situ measurements, can fill the gap for investigating atmospheric variability in the observationally sparse and geographically complex mountain terrain of the Himalaya.
The biases in simulating the meteorological parameters, especially in the lower troposphere, are associated with several factors, e.g., representation of topography, land use, surface heat and moisture flux transport, and parameterization of physical processes (e.g., Lee et al., 1989; Hanna and Yang, 2001; Cheng and Steenburgh, 2005; Singh et al., 2016). The Weather Research and Forecasting (WRF) model has been used for experiments over complex terrain around the world, e.g., the Himalaya region (e.g., Sarangi et al., 2014; Singh et al., 2016; Mues et al., 2018; Potter et al., 2018; Norris et al., 2020; Wang et al., 2020), the Tibetan Plateau (e.g., Gao et al., 2015; Zhou et al., 2018), and multiple mountain ranges in the western United States (e.g., Zhang et al., 2013), to evaluate and study meteorology and dynamics. A cold bias was reported in this model over the Tibetan Plateau and the Himalayan region by Gao et al. (2015). The near-surface winds showed biases linked to unresolved processes in the model, such as sub-grid turbulence and land–surface atmospheric interactions, in addition to the boundary layer parametrization (Hanna and Yang, 2001; Zhang and Zheng, 2004; Cheng and Steenburgh, 2005). Zhou et al. (2018) found lower biases in simulated winds after considering the turbulent orographically formed drag over the Tibetan Plateau.
The WRF model, with suitably chosen schemes, has been shown to reproduce the
regional-scale meteorology (Kumar et al., 2012) and to some extent also the
mountain–valley wind systems (Sarangi et al., 2014) and boundary layer
dynamics (Singh et al., 2016; Mues et al., 2018) over the Himalayan region.
Nevertheless, local meteorology is still difficult to simulate accurately.
Mues et al. (2018) performed a high-resolution WRF simulation over the
Kathmandu valley of the Himalaya and reported overestimation of 2 m temperature
and 10 m wind speed, which they attributed to insufficient resolution of the
complex topography, even at a resolution of 3 km. Although few studies have
used the WRF model at very high resolution over the Himalayan region (e.g.,
Cannon et al., 2017; Mues et al., 2018; Potter et al., 2018; Zhou et al.,
2018, 2019; Norris et al., 2020; Wang et al., 2020), the
model performance over complex terrains like the Himalaya still requires
improvement, which can be achieved through an extensive evaluation at
sub-kilometer resolution against an intensive field campaign. The main
objectives of the study are as follows:
to examine the model performance over the CH at varying resolutions (15,
5 and 1 km) by evaluating several model diagnostics against the
observations made during the GVAX campaign; to investigate the effect of feedback from the nest to the parent domain, as
this might allow configuring a model setup covering the larger Indian region
with more accurate results over the Himalaya; and downscaling to a sub-kilometer (333 m) resolution with the implementation
of a very high-resolution (3 s) topographical input into the model to
examine the potential of simulations in reproducing local-scale dynamics.
The subsequent section (Sect. 2) describes the model setup, followed by the experimental design and a discussion of datasets used for model evaluation. Section 3 provides a comparison of model results with the ERA-Interim reanalysis (Sect. 3.1), radiosonde observations (Sect. 3.2), and ground-based measurements (Sect. 3.3). Analysis of domain feedback is presented in Sect. 3.4, and the effect of implementing high-resolution topography is investigated in Sect. 3.5, followed by the summary and conclusions in Sect. 4.
The WRF model version 3.8.1 has been used in the present study. WRF is a
mesoscale, non-hydrostatic, numerical weather prediction (NWP) model with
advanced physics and numerical schemes for simulating meteorology and
dynamics. WRF-ARW uses an Eulerian mass-based dynamical core with
terrain-following vertical coordinates (Skamarock et al., 2008). ERA-Interim
reanalysis from the European Centre for Medium-Range Weather Forecasts
(ECMWF), available at a temporal resolution of 6 h and a horizontal
resolution of
The Goddard scheme is used for shortwave radiation (Chou and Suarez, 1994),
while longwave radiation is simulated by the rapid radiative transfer
model scheme (Mlawer et al., 1997). For resolving the boundary layer
processes the first-order Yonsei University (YSU)
scheme based on non-local closure (Hong et al., 2006) is used, including an explicit entrainment layer
with the K-profile in an unstable mixed layer. The planetary boundary layer (PBL) height is determined from
the Richardson number (
The model is configured with three domains of 15 km (d01), 5 km (d02), and 1 km (d03) horizontal grid spacing using Mercator projection centering at
Manora Peak (79.46
Topography represented in the WRF model domains
For d01, boundary conditions are provided from the ERA-Interim reanalysis, as
explained earlier. Model simulations have been performed for the 4 months
of the summer monsoon: 1 June 2011 to 30 September 2011 (JJAS). This simulation
period is chosen considering the availability of continuous observations
from 11 June 2011 and to allow a sufficient spin-up time of 10 d for the
model to achieve its equilibrium state (Angevine et al., 2014; Seck et al.,
2015; Jerez et al., 2020). Only the outer domain d01 is nudged with the
global reanalysis for temperature, water vapor, and the zonal and meridional (
We utilize observations during an intensive field campaign –
the Ganges Valleys Aerosol Experiment (GVAX) – to evaluate model simulations.
The GVAX campaign was carried out using the Atmospheric Radiation Measurement
(ARM) Climate Research Facility of the U.S. Department of Energy (DOE) from
10 June 2011 to 31 March 2012 at ARIES, Manora Peak in Nainital (e.g.,
Kotamarthi, 2013; Singh et al., 2016; Dumka et al., 2017). This
observational site (79.46
The vertical profiles of temperature, pressure, relative humidity, and
horizontal wind (speed and direction) were obtained by four launches (00:00,
06:00, 12:00, and 18:00 UTC) of the radiosonde each day during the campaign
(Naja et al., 2016). The continuous vertical profiles of the meteorological
parameters except wind speed and direction were available from the end of
June 2011 through the entire study period, whereas valid and quality wind data
were available only for September 2011. Hence, in this study, radiosonde
measurements from 1 July 2011 onwards are used for the model evaluation of
meteorological parameters, except wind speed and direction, which are
evaluated only for September. A total of 309 valid profiles of temperature
and relative humidity and 104 profiles of wind are used. Statistical
metrics such as the mean bias (MB), root mean square error (RMSE), and
correlation coefficient (
Here, we have used the ERA-Interim data for comparison with WRF output. We first compare the WRF-simulated spatial distribution of meteorological parameters (surface pressure, 2 m air temperature, 2 m RH, and 10 m WS) with the ERA-Interim reanalysis over the common region of all the domains averaged for the entire simulation period (Fig. 2). The three contours of the topographic height of 500, 1500, and 2000 m are used to relate the meteorological features to the resolved topography in three domains. The common area in all domains includes the low-altitude IGP region in the south (elevation of less than 400 m; Fig. 1) and elevated mountains of the central Himalaya in the north. Also, for a consistent comparison, model-simulated values are taken at the same time intervals as in ERA-Interim data (i.e., every 6 h). From the comparison presented in Fig. 2, it is evident that the meteorological parameters simulated by the model are dependent on the model grid resolution. The existence of the sharp-gradient topographic height (SGTH) of about 1600 m from the foothill of the Himalaya to the observational site modifies the wind pattern and moisture content differently at different grid resolutions, indicating the critical role of mountain orography. The surface pressure explicitly depends upon the elevation of a location from mean sea level. The contour of the pressure parameter from ERA-Interim data shows the surface pressure of about 900 hPa for the observational site Manora Peak, and it varied from 550 to 975 hPa within this region, while WRF-simulated pressure is 869, 835, and 827 hPa for d01, d02, and d03, respectively. WRF-simulated surface pressure ranges from 821.9 hPa over the high-altitude CH region to 977.0 hPa in the IGP region within d01. Simultaneously, the range of variation in the surface pressure is 788.1–977.5 and 760.4–977.7 hPa within d02 and d03, respectively, and the minimum pressure decreases from d01 to d03, which is attributed to the improvement in resolved topography on increasing model grid resolution. However, the effects of the SGTH are not observed for temperature, wind, and RH in ERA-Interim contours due to the unresolved topographic features. Simulated maps show the spatial homogeneity of meteorological parameters over the flat terrain of IGP in the foothills of the Himalaya compared to the elevated central Himalayan region.
Contours in the first three columns show WRF results for the three
domains (first column: d01, second column: d02, third column: d03), and
the fourth column shows corresponding parameters from the ERA-Interim
reanalysis. The first row shows mean surface pressure during the monsoon (JJAS),
the second row shows 2 m temperature (in
The effect of spatial resolution is clearly observed over the mountainous region of the Himalaya, where the size of the mountains changes abruptly, with the modeled output showing increasingly distinct features with increasing grid resolution. On the other hand, there are minimal differences in the topography of the IGP, and hence the meteorological features associated with the topography are well captured in the model even at a coarser resolution of 15 km.
Model simulations show the topography-dependent spatial variation in 2 m
temperature in the ranges of 20.0–29.5
The wind speed is highly dependent upon the model grid resolution and
orography-induced circulations during different seasons (Solanki et al.,
2016, 2019), and this is reflected in Fig. 2. As mentioned earlier, although the
topography of the IGP region does not vary abruptly, the magnitude of the
wind speed over this region and over the complex Himalayan region is
found to change significantly at different model resolutions, thereby
indicating that the wind speed is very sensitive to both model resolution
and topography. The wind speed in d01 varies from 1.3 to 2.8 m s
The intensive radiosonde observations made during the GVAX field campaign at
Manora Peak (79.46
The comparison of simulated vertical profiles of
The inversion of temperature at the top of the troposphere occurred at
Lower correlations for temperature and wind speed near the surface (750 hPa) could be due to terrain-induced effects, which are most significant in the local boundary layer. The surface-level winds and turbulence are some of the boundary layer features affected mainly by the surface and terrain characteristics. The vertical profiles of these parameters up to 500 hPa in all three model domains are shown in Fig. 4. Differences between the simulated vertical profile of temperature and radiosonde observations are in general similar in all the domains. Except for the relative humidity in d01, other meteorological parameters (temperature and wind speed) do not reveal strong dependencies on the model resolution. However, the model overestimates the relative humidity near the 500 hPa level in d02 and d03 on some days. In the case of the wind speed, the model underestimates the magnitude of the wind in the first few days up to 500 hPa, though by and large the model is able to capture the vertical profiles.
Difference between model (d01: first row, d02: second row, d03:
third row) and radiosonde observations for temperature
Figure 5 shows the vertical profiles of the following statistical metrics for the three simulations
(d01, d02, d03): mean bias
(MB), root mean square error (RMSE), and correlation coefficient (
The vertical profiles of the mean bias (MB), root mean square error
(RMSE), and correlation coefficient (
The model-simulated 2 m temperature (
The mean (
Mean diurnal variations of
Due to the complex terrain and the grid size of the model, the simulated
altitude of the observational site could differ from reality. In this study,
the model underestimated station altitude by about 588, 480, and 270 m in
d01, d02, and d03. We performed an additional evaluation to explore and
achieve possible improvement by linearly interpolating the vertical profile
of meteorological parameters to the actual altitude of the station (Fig. 6d–f), as done in a few previous studies (e.g., Mues et al., 2018). The altitude
adjustment was made as per the equation of linear interpolation given in the
Supplement (Eq. 4). The analysis shows that the
correlation coefficient values between the model and observations do not show
any clear improvement in model output (e.g., for
We evaluate the MB values (Tables 1 and S1) in model simulations
considering the benchmarks suggested by Emery et al. (2001). In the d03
simulation, MB values for both
Taylor diagram with the correlation coefficient, normalized
standard deviation, and normalized root mean square difference (RMSD) error
for
Comparison of the wind speed and direction represented by the wind rose (top panel) and the frequency distribution of the wind direction (bottom panel) for model simulations over the three domains (d01, d02, d03) and observations (obs) during June–September 2011. Different colors and radii of wind roses show the wind speed and frequency of occurrences, respectively.
The wind direction is strongly influenced by the surrounding topography over
mountainous regions, and the evaluation of the wind direction at
horizontal resolution is depicted in Fig. 8. The winds varying between
meteorological directions 337.5 and 22.5
In the preceding section, the simulations were carried out without any
feedback (WRF-WF) from the finer-resolution domain to its parent domain, and
results have been discussed. This WRF-WF experiment was conducted in such a
way that it could explicitly account for the grid resolution effects on the
model performance. The simulated meteorology with this model setup (with
feedback) depicted different model performance in the outermost coarse-resolution domain d01 compared to d02 and d03. The model performance depends
upon the boundary and initial conditions. Another model simulation is
carried out in this section using the same configuration but with two-way
interactive nesting and feedback (WRF-F) from the nested domain to its parent
domain. The simulated meteorological parameters in the higher nests are fed
back to their parent domains, and the boundary conditions are replaced there. The
model results over the CH region in the regional-scale simulation (d01)
show better agreement with the observations because of the feedback from the
high-resolution nested simulation. The comparison of the simulated
meteorological parameters (
Diurnal variation of the
The comparison of mean values (Table 2) shows a decrease in model bias for
The effect of the two-way nesting on d01 is shown. The difference between the simulations with feedback (WRF-F) and without feedback (WRF-WF) is shown for surface pressure, 2 m temperature, and 2 m relative humidity along with three elevation contours at 500 m (dashed), 1500 m (thin solid), and 2000 m (thick solid).
Comparison of the simulated meteorology for surface pressure (
Effects of the feedback on surface pressure, 2 m temperature, and relative humidity in the domain d01 are shown by Fig. 10. Feedback effects are seen to be more pronounced over the mountainous region than over the flat terrain of the IGP. The feedback from the nested domain to the parent domain mostly modifies the meteorology over the mountainous region, as shown by the topography contours in Fig. 10. The analyses of biases and correlations suggest an improvement in the model-simulated pressure, temperature, and humidity through feedback from well-resolved nests. This further underpins the fact that better representations of the Himalaya over local scales can be adopted to simulate meteorology at the regional scale with lower biases over complex terrain in the given domain. Nevertheless, further modeling studies along with more observations are needed to improve the model performance. We extended the efforts to improve the wind speed and direction simulated over the complex topography by implementing a high-resolution (3 s) topographical input in the model to evaluate finer-resolution features over the Himalaya in the following section.
Simulations described in previous sections were performed using the 30 s
(
The topography from GMTED at 30 s in domain d03
The comparison of the topographic height between GMTED and SRTM3s in Fig. S7 shows that the differences are larger over the mountainous region, which
vary from
In d04, surface pressure is seen to be simulated more realistically (809 hPa), and the dry bias in 2 m relative humidity is improved by
Wind roses for
Simulation of the wind directions improved from d03 to d04 by using the
SRTM3s topography, except certain wind directions such as southeasterly. An
improvement is noticed in simulated surface pressure, 2 m relative humidity,
and 10 m wind speed using the SRTM3s topography. Topographical data at
different resolutions are found to show RH differences in the range of
This study using the WRF model mainly elucidated upon the various diagnostics it calculates for its multiple domains, the comparison of model results to an intensive field campaign, and downscaling to a sub-kilometer resolution with 3 s resolution SRTM topography data that resolves individual peaks and valleys over the CH region. The effects of spatial resolution on model-simulated meteorology have been examined by combining the WRF model with ground-based and in situ observations, as well as reanalysis datasets. Owing to the highly complex topography of the central Himalaya, model results show strong sensitivity towards the model resolution and adequate representation of terrain features. Model-simulated meteorological profiles do not show much dependency on the resolution, except in the lower atmosphere, which is directly influenced by terrain-induced effects and surface characteristics, emphasizing the need to evaluate various physics schemes over this region. The biases in 2 m temperature, relative humidity, and surface pressure show a decrease on increasing the model resolution, indicating a better-resolved representation of topographical features. Diurnal variations in meteorological parameters also show better agreement on increasing the grid resolution. Although the surface pressure does not show a pronounced diurnal variation, the biases in simulated surface pressure are significantly reduced over fine-resolution simulations. Interpolation of coarser simulations (d01, d02) to the station altitude reduces the bias in surface pressure and temperature, but it suppresses the diurnal variability. The results highlight the significance of accurately representing terrains at finer resolutions (d03). The model is generally not able to reproduce the frequency distribution of the wind direction, except in some of the major components in all the simulations with varying resolutions. The directionality of the simulated winds shows improvements over finer grid resolutions; however, reproducing the diurnal variability still remains a challenge. Biases are stronger typically during daytime and also during transitions of low to high wind conditions and vice versa. This is attributed to the uncertainties in representing the interaction of slope winds with the synoptic mean flow and local circulations, despite an improved representation of terrain features. A sensitivity experiment with domain feedback turned on shows that the feedback process can improve the representation of the CH in simulations covering a larger region of the northern Indian subcontinent. It is suggested that further improvements in the model performance are limited due to the lack of high-resolution topographical biases through input meteorological fields and model physics. Nevertheless, the implementation of a very high-resolution (3 s) topographical input using the SRTM data shows the potential to reduce the biases related to topographical features to some extent.
Observational data from the GVAX campaign are freely available
(
The supplement related to this article is available online at:
NS and AP designed and supervised the study. JS performed the simulations, assisted by NO and AS. JS, NO, and AS analyzed the model results, and NKK, KR, and SSG contributed to the interpretations. VRK significantly contributed to conceiving and realizing the GVAX campaign. JS and NS wrote the first draft, and all the authors contributed to the paper.
The authors declare that they have no conflict of interest.
We are thankful to the director of ARIES, Nainital. We acknowledge NCAR for the WRF-ARW model, ECMWF for the ERA-Interim reanalysis datasets, and the ARM Climate Research Facility of the U.S. Department of Energy (DOE) for the observations made during the GVAX campaign. Computing resources from the Max Planck Computing and Data Facility (MPCDF) are profoundly acknowledged. Narendra Ojha acknowledges the computing resources Vikram-100 HPC at the Physical Research Laboratory (PRL) and valuable support from Duggirala Pallamraju and Anil Bhardwaj. Constructive comments and suggestions from the anonymous reviewers and the handling editor are gratefully acknowledged.
Jaydeep Singh is the senior research fellow for the ABLN&C:NOBLE project under ISRO-GBP. The article processing charges for this open-access publication were covered by the Max Planck Society.
This paper was edited by Juan Antonio Añel and reviewed by three anonymous referees.