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  • View in gallery

    WRF Model domain with elevation (m). Boxes indicate the two averaging regions: offshore Arabian Sea (10°–14°N, 72°–74°E) and west coast of India (10°–14°N, 75°–77°E).

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    Longitudinal–vertical cross section of WRF-simulated cloud mixing ratios (cloud water, rainwater, ice, snow, and graupel; g kg−1; contours), zonal and vertical wind vector (m s−1) at 11.5°N for 1000 UTC 26 Jul 2008. The white area on the bottom indicates the topography of the Western Ghats.

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    Mean rainfall rate (mm day−1) of the wet case (20–29 Jul 2008) in (a) the TRMM 3B43 product and (b) the WRF CTL simulation. (c),(d) As in (a),(b), but for the dry case (3–12 Aug 2009).

  • View in gallery

    Comparison between MODIS Aqua cloud optical thickness, TRMM rainfall (mm day−1), and WRF-simulated rainfall (mm day−1) at (a)–(c) 0900 UTC 24 Jul 2008 and (d)–(f) 0900 UTC 28 Jul 2008.

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    Comparison of WRF-simulated rainfall (mm day−1) in (a) 6- and (b) 2-km simulations for the wet case, averaged between 0000 UTC 20 Jul 2008 and 0000 UTC 23 Jul 2008.

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    TRMM 1998–2015 JJA climatological rainfall rate (mm day−1).

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    Hovmöller diagram at 11.5°N in the WRF CTL simulation of the wet case for (a) rainfall (mm day−1), (b) CAPE (J kg−1), and (c)vertically integrated zonal WVF (kg m−1 s−1). (d)–(f) As in (a)–(c), but for the dry case. The dot–dashed gray line indicates the location of India’s west coast.

  • View in gallery

    Time-averaged longitudinal–vertical cross section of WRF-simulated relative humidity (%) at 11.5°N for the 10-day simulation of (a) the dry case in 2009 and (b) the wet case in 2008. The white area on the bottom indicates the topography of the Western Ghats.

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    Mean rainfall rate (mm day−1) in WRF simulations for the wet case. The simulations are listed in Table 1.

  • View in gallery

    As in Fig. 9, but for the dry case.

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    Coastal region mean rainfall rate (mm day−1; shaded) for (a) CTL of the wet case, (b) NoLH of the wet case, (c) CTL of the dry case, and (d) NoLH of the dry case. Contours show elevation (m). Note the color scale is different from previous maps; it is shifted to show more high rainfall rates.

  • View in gallery

    Diurnal cycle of rainfall (mm day−1) over the coast (10°–14°N, 75°–77°E; see Fig. 1 for the location) averaged in the 10-day simulations of the wet case (solid lines) and the dry case (dashed lines). The time shown is LST, which is 6 h ahead of UTC time.

  • View in gallery

    Initial SST (°C) in the boundary conditions of the WRF CTL simulation of (a) the wet case and (b) the dry case.

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    Relation between NOAA OI monthly SST (°C) averaged in the eastern Arabian Sea (10°–14°N, 68°–74°E) and TRMM monthly rainfall rate over two study regions: offshore Arabian Sea (blue; 10°–14°N, 72°–74°E) and west coast of India (red; 10°–14°N, 75°–77°E) during JJA 1998–2014. The correlation coefficient between SST and offshore rainfall is 0.38 with a p value of 0.0056. The correlation coefficient between SST and coastal rainfall is 0.07 with a p value of 0.6187.

  • View in gallery

    SST anomalies (K) added to the WRF SST simulation, with a maximum of 2 K at 12°N, 71°E and a radius of 350 km.

  • View in gallery

    Time series of rainfall rate (mm day−1) averaged over the offshore region (blue; 10°–14°N, 72°–74°E) and coastal region (red; 10°–14°N, 75°–77°E) in the CTL (solid lines) and SST (dot–dashed lines) simulations for the wet case. The x axis shows time at 0000 UTC. The averaging regions are shown in Fig. 1.

  • View in gallery

    Differences of (a) rainfall rate (mm day−1) and (b) CAPE (J kg−1) between the SST sensitivity simulation and the CTL simulation (SST minus CTL) for period 1. (c),(d) As in (a),(b), but for period 2. The dashed circle indicates the SST warm pool as shown in Fig. 15.

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Numerical Study of Physical Processes Controlling Summer Precipitation over the Western Ghats Region

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  • 1 Department of Geology and Geophysics, Yale University, New Haven, Connecticut
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Abstract

Summer precipitation over the Western Ghats and its adjacent Arabian Sea is an important component of the Indian monsoon. To advance understanding of the physical processes controlling this regional precipitation, a series of high-resolution convection-permitting simulations were conducted using the Weather Research and Forecasting (WRF) Model. Convection simulated in the WRF Model agrees with TRMM and MODIS satellite estimates. Sensitivity simulations are conducted, by altering topography, latent heating, and sea surface temperature (SST), to quantify the effects of different physical forcing factors. It is helpful to put India’s west coast rainfall systems into three categories with different causes and characteristics. 1) Offshore rainfall is controlled by incoming convective available potential energy (CAPE), the entrainment of midtropospheric dry layer in the monsoon westerlies, and the latent heat flux and SST of the Arabian Sea. It is not triggered by the Western Ghats. When offshore convection is present, it reduces both CAPE and the downwind coastal rainfall. Strong (weak) offshore rainfall is associated with high (low) SSTs in the Arabian Sea, suggested by both observations and sensitivity simulations. 2) Coastal convective rainfall is forced by the coastline roughness, diurnal heating, and the Western Ghats topography. This localized convective rainfall ends abruptly beyond the Western Ghats, producing a rain shadow to the east of the mountains. This deep convection with mixed phase microphysics is the biggest overall rain producer. 3) Orographic stratiform warm rain and drizzle dominate the local precipitation on the crest of the Western Ghats.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gang Zhang, gang.zhang@yale.edu; gz@utexas.edu

Abstract

Summer precipitation over the Western Ghats and its adjacent Arabian Sea is an important component of the Indian monsoon. To advance understanding of the physical processes controlling this regional precipitation, a series of high-resolution convection-permitting simulations were conducted using the Weather Research and Forecasting (WRF) Model. Convection simulated in the WRF Model agrees with TRMM and MODIS satellite estimates. Sensitivity simulations are conducted, by altering topography, latent heating, and sea surface temperature (SST), to quantify the effects of different physical forcing factors. It is helpful to put India’s west coast rainfall systems into three categories with different causes and characteristics. 1) Offshore rainfall is controlled by incoming convective available potential energy (CAPE), the entrainment of midtropospheric dry layer in the monsoon westerlies, and the latent heat flux and SST of the Arabian Sea. It is not triggered by the Western Ghats. When offshore convection is present, it reduces both CAPE and the downwind coastal rainfall. Strong (weak) offshore rainfall is associated with high (low) SSTs in the Arabian Sea, suggested by both observations and sensitivity simulations. 2) Coastal convective rainfall is forced by the coastline roughness, diurnal heating, and the Western Ghats topography. This localized convective rainfall ends abruptly beyond the Western Ghats, producing a rain shadow to the east of the mountains. This deep convection with mixed phase microphysics is the biggest overall rain producer. 3) Orographic stratiform warm rain and drizzle dominate the local precipitation on the crest of the Western Ghats.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gang Zhang, gang.zhang@yale.edu; gz@utexas.edu

1. Introduction

The Western Ghats, located along the west coast of the Indian subcontinent, is an active factor in influencing the regional precipitation along the west coast of India and perhaps over the offshore Arabian Sea and the interior continent (Smith and Lin 1983; Grossman and Durran 1984; Ogura and Yoshizaki 1988; Flynn et al. 2017; Shige et al. 2017). Using hourly in situ observation of rainfall, Deshpande et al. (2012) show that the diurnal cycle of rainfall has an afternoon and an early morning peak, with a diurnal variation of 22 and 12 mm day−1 at Mhabaleshwar and Mumbai, India, respectively, in the Western Ghats. They also find that stations along the west coast show an extreme rainfall rate of more than 100 mm h−1. Satellite remote sensing products of precipitation are also used to examine the precipitation over this region. Tawde and Singh (2015) validate the Tropical Rainfall Measuring Mission (TRMM) rainfall product (3B42) with Indian Meteorological Department (IMD) gridded rainfall data over the Western Ghats, and find that the TRMM data agrees with IMD data regarding the spatial distribution and monthly to annual variations. However, TRMM underestimates the intensity of rainfall, especially for the heavy rainfall events. They also suggest that the orographic precipitation over the Western Ghats is related to two factors of the topography: 1) the length and width of the mountain barrier and 2) the slope and elevation of the windward side slope. Using TRMM Precipitation Radar data, Biasutti et al. (2012) also find that the maxima of rainfall frequency occur over the windward slope of the Western Ghats. Romatschke and Houze (2011) suggest that the horizontal size of convective systems is important in determining the rainfall amount, and they find that small and medium systems dominate over the Western Ghats and medium systems prevail over the eastern Arabian Sea.

The physical processes causing precipitation over the Western Ghats have been explored in previous studies. Using in situ observations from windward and lee sides of the Western Ghats, Konwar et al. (2014) examine the cloud processes and suggest that the condensational growth of cloud is dynamically forced by the mountains, which generates warm rain over the Western Ghats. Kumar et al. (2014) also confirm the dominance of warm rain process in producing rainfall over the Western Ghats, and find that the total rainfall amount is mainly contributed by heavy rainfall (more than 40 mm day−1). Maheskumar et al. (2014) suggest that the high rainfall spells over the west coast of India are associated with warm SST, low-level convergence, high CAPE, and low convective inhibition (CIN). They also clarify that both deep and shallow convection are responsible for high rainfall.

Models with different levels of complexity are also used to explore the physical factors interacting with the precipitation over the Western Ghats region. Smith and Lin (1983) suggest that most of rainfall over the west coast of India is contributed by deep convection. Grossman and Durran (1984) highlight the offshore convection over the Arabian Sea as an important component of the regional precipitation, and state that the offshore convection is triggered by deceleration and convergence of low-level flow caused by the Western Ghats. However, Smith (1985) argues that the blocking effect of the Western Ghats on upstream convection needs further examination because of the omission of latent heating effect in Grossman and Durran (1984). Building on these modeling studies, Ogura and Yoshizaki (1988) further suggest the essential roles of zonal wind shear and surface fluxes from the Arabian Sea in shaping precipitation both over the Arabian Sea and the Western Ghats. The modeling work in Xie et al. (2006) also show the importance of the latent heating effect, and suggest that the local-scale orographic precipitation influences the large-scale Asian monsoon. Using regional model simulations with and without realistic topography, Wu et al. (1999) suggest that orographic lifting is an important triggering mechanism of rainfall over the west coast of India, because it produces instability and enhances latent heating aloft. Sijikumar et al. (2013) also show similar results to Wu et al. (1999) and conclude that the existence of the Western Ghats enhances the rainfall over the west coast of India and reduces the rainfall in the inland rain shadow regions. Wang and Chang (2012) find that the drag effect dominates when the mountain is below the lifting condensation level (LCL). When the mountain is higher than the LCL, a positive feedback related to latent heating is formed between orographic precipitation and the low-level westerly flow.

The regional precipitation is also associated with the large-scale environment (e.g., the SST of the adjacent Arabian Sea). The low-level westerly flow over the Arabian Sea (Findlater 1969) interacts with the Western Ghats and transports water vapor from the Arabian Sea to the Indian subcontinent. Using observational analysis, Shukla and Huang (2016) show that the warm SST anomalies in the Arabian Sea reduce sea level pressure and generate anomalous cyclonic flow favorable for regional precipitation. Other observational and modeling studies (Sijikumar and Rajeev 2012; Roxy 2014) suggest that an increase of offshore rainfall is associated with warm SST anomalies in the Arabian Sea. The cold SST bias in the Arabian Sea in the coupled global climate models results in a weakened Indian monsoon because of the reduction of water vapor fluxes (Levine and Turner 2012; Levine et al. 2013). The warm SSTs also enhance the convection in the Indian monsoon on intraseasonal time scales (Li et al. 2016).

In this study, we investigate the regional precipitation over the Western Ghats and the adjacent Arabian Sea through a series of convection-permitting simulations. These high-resolution simulations better characterize mountain topography and avoid using parameterized cumulus convection. Coastal rainfall over the Western Ghats region is compared with the offshore rainfall over the Arabian Sea. Physical processes related to the mountains and SST are quantified through additional sensitivity simulations. The rest of this paper is organized as follows. Section 2 describes the observational datasets and the configurations of the convection-permitting simulations used in this study. The control simulations using default configuration are validated against observations in section 3. Results are presented in section 4, and conclusions are summarized in section 5.

2. Data and methods

a. Observational data

We use the 3-hourly TRMM 3B42 precipitation product that provides the TRMM-adjusted merged-infrared (IR) precipitation rate (Huffman et al. 2007). This product has continuous coverage from 50°S to 50°N, with a 0.25° resolution. To examine the finer structure of precipitating clouds, the MODIS level-2 products of the atmosphere are used in this study. Particularly, we use optical thickness from the MODIS cloud products (King et al. 2003; Platnick et al. 2003; Frey et al. 2008; Ackerman et al. 2008; King et al. 2013) as a proxy to identify deep convections over the study region. These satellite images have a 1-km resolution and are available for discrete times when the swath coincides with our focused domain. To explore the relation between regional precipitation and the SST in the Arabian Sea, the monthly mean of NOAA daily Optimum Interpolation (OI) SST data (version 2) at 0.25° resolution (Reynolds et al. 2007) is used to correlate with monthly mean TRMM rainfall for their common available period of 1998–2014.

Several study cases are selected during the summers of 2008–10 which is the period of the Years of Tropical Convection (YOTC; Waliser et al. 2012). We use TRMM rainfall data to select several simulation cases with distinct wet and dry conditions of precipitation for the Western Ghats region. Two cases will be presented in this paper: a wet case for the period of 0000 UTC 20 July 2008–0000 UTC 30 July 2008 during which the observation shows strong precipitation over both the Western Ghats and the offshore Arabian Sea; and a dry case for the period of 0000 UTC 3 August 2009–0000 UTC 13 August 2009 during which only weak precipitation occurs along the coastal region of the Western Ghats.

b. Numerical experiments

This study is mainly based on realistic three-dimensional numerical modeling experiments by using the Weather Research and Forecasting Model [Advanced Research version of WRF (WRF-ARW), version 3.4.1, Skamarock et al. (2008)]. We use the WRF Model to conduct convection-permitting simulations at 6-km horizontal resolution, which allow us to explicitly resolve moist convection without the use of cumulus parameterization. The model domain is shown in Fig. 1, which includes the Indian subcontinent and the adjacent oceans. The Western Ghats is the continuous mountain range along the west coast of India. A narrow belt of plains with a width of about 50 km is located between the west coastline and the Western Ghats. The model domain includes 330 grid points in longitude by 314 grid points in latitude. Such a large domain is useful in reducing the impacts of the lateral boundary conditions on the model solutions in the interior domain. All the WRF simulations use 80 vertical levels of the atmosphere, with the top located at 5 hPa.

Fig. 1.
Fig. 1.

WRF Model domain with elevation (m). Boxes indicate the two averaging regions: offshore Arabian Sea (10°–14°N, 72°–74°E) and west coast of India (10°–14°N, 75°–77°E).

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

Initial and boundary conditions are supplied from the 6-hourly National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; Saha et al. 2010), which has a horizontal resolution of 0.5° for atmospheric fields and 0.31° for surface fields. Sea surface temperature is also updated 6-hourly from CFSR. CFSR is used because of its high spatial resolution. The integration time step of the WRF Model is 20 s, and hourly model output is archived for analysis. Physical parameterizations selected for use in the WRF Model simulations include the Lin et al. microphysics scheme (Lin et al. 1983; Rutledge and Hobbs 1984; Chen and Sun 2002), the RRTM longwave radiation scheme (Mlawer et al. 1997), the Dudhia shortwave radiation scheme (Dudhia 1989), the Yonsei University boundary layer scheme (Hong et al. 2006), the MM5 Monin–Obukhov surface layer scheme (Skamarock et al. 2008), and the Noah land surface model (Chen and Dudhia 2001). The cumulus convection parameterization is disabled. This combination of parameterization schemes in WRF has been validated in a season-long convection-permitting simulation over a similar monsoon region in West Africa (Zhang et al. 2016).

Table 1 lists all the simulations used in this study. A control (CTL) simulation is conducted using the above configuration and other default settings of the WRF Model. Figure 2 displays an instantaneous longitudinal–vertical cross section, showing the zonal wind shear and deep convective clouds simulated in the WRF control run. The arrows show slowing boundary layer winds at the coast, partly responsible for triggering the convection. A reversal from monsoon westerly to easterly winds is seen aloft at the 275-hPa level. The convection is deep, reaching 225 hPa with snow and westward-drifting detrained cirrus cloud ice. Two rain shafts are seen, each seeded by graupel from above. In the main updraft, liquid cloud water is found up to the 250-hPa level, in agreement with Khain et al. (2001). The convection ends abruptly to the east.

Table 1.

List of WRF Model simulations. Yes and no indicate whether the simulation is conducted for the listed case or not.

Table 1.
Fig. 2.
Fig. 2.

Longitudinal–vertical cross section of WRF-simulated cloud mixing ratios (cloud water, rainwater, ice, snow, and graupel; g kg−1; contours), zonal and vertical wind vector (m s−1) at 11.5°N for 1000 UTC 26 Jul 2008. The white area on the bottom indicates the topography of the Western Ghats.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

To illustrate the sensitivity of cumulus convection parameterization, an extra simulation is conducted identically to the control simulation of the wet case, but using the Kain–Fritsch convection scheme (Kain 2004). This simulation produces unrealistic rainfall with excessive rainfall over the Western Ghats persistently but little offshore rainfall (results not shown), which validates that the cumulus convection parameterization needs to be disabled in the 6-km simulations.

To better quantify the role of different forcing factors, a series of sensitivity experiments are also conducted. To explicitly explore the role of the Western Ghats on precipitation, a no mountain (NoM) simulation is conducted by removing the surface elevation (set to zero) all over the domain but still keeping the surface properties. A no latent heating (NoLH) simulation is conducted by disabling the latent heating effect in the cloud processes through the WRF microphysics parameterization. Both of the effects of topography and latent heating are disabled in a no mountain no latent heating (NoMNoLH) simulation by removing the surface elevation and turning off latent heating together. The rest of the configurations for these sensitivity experiments are as same as the control simulation.

The NoLH simulation with latent heat L = 0 J kg−1 is a convenient but imperfect way to isolate the effect of moist convection. With this modification, air parcels rise along a dry adiabat even while they condense water. The more rapid parcel cooling rate increases the amount of condensate. In cases when the forced orographic lifting is large and the convection is not important, the rain amount with L = 0 will increase because of the dry adiabatic cooling. In the case of the Western Ghats however, the rain decreases sharply with L = 0 (section 4b). This implies that the moist convection is an essential process. The remaining precipitation is due to direct orographic lifting with stratiform clouds. This component might be slightly overestimated with L = 0.

To better understand the impact of SST anomalies in the Arabian Sea on regional precipitation, we conduct an additional sensitivity simulation (SST) by adding an idealized patch of warm SST anomaly to the default SST forcing supplied from reanalysis. The same warm SST patch is added constantly to every 6-hourly SST input forcing used for boundary condition. The comparison between this idealized SST experiment and the control simulation will show the role of SST anomalies in shaping regional precipitation.

3. Model validation

Figures 3a,b show the comparison of temporally averaged rainfall rate during the simulation period of the wet case (0000 UTC 20 July 2008–0000 UTC 30 July 2008) between the TRMM satellite product and the WRF output. The spatial distribution of the rainfall maximum is captured by the WRF Model (e.g., the rainfall maximum over the Western Ghats and over the offshore Arabian Sea to the west of the coast). The magnitude of rainfall is much higher in the WRF simulation for the regions with rainfall maximum. But TRMM is known to have an underestimation issue of heavy rainfall in this region (Tawde and Singh 2015). In addition to the systematic bias of TRMM, we note that the difference of spatial resolution may also contribute to this distinction. The TRMM product has a much coarser resolution (about 25 km) than the WRF output (6 km); therefore, the magnitude of rainfall in TRMM may be reduced because of spatial smoothing. A prominent rain shadow region is also shown with low rainfall rates over the interior continent to the east of the Western Ghats in both the observation and the model. The WRF-simulated rain shadow is drier than observed. With a much wetter condition over the Western Ghats, the WRF Model shows a stronger wet–dry contrast between the mountains and the rain shadow.

Fig. 3.
Fig. 3.

Mean rainfall rate (mm day−1) of the wet case (20–29 Jul 2008) in (a) the TRMM 3B43 product and (b) the WRF CTL simulation. (c),(d) As in (a),(b), but for the dry case (3–12 Aug 2009).

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

The above evaluation of the model-simulated precipitation is for the wet case in the summer 2008. We also studied a dry case in summer 2009 (0000 UTC 3 August 2009–0000 UTC 12 August 2009), which shows a relatively dry condition. For the dry case, very little rainfall is shown in the TRMM observation (Fig. 3c) over the Arabian Sea and a weak rain belt is located along the coastal region of the Western Ghats. Figure 3d displays the WRF-simulated mean rainfall rate for this dry case. Compared with the wet case (Fig. 3b), the rainfall over offshore Arabian Sea is marginal, consistent with observations. The model also produces rainfall along the crest of the Western Ghats. The rain shadow to the east of the Western Ghats is also much drier than in the wet period. The comparison between wet and dry cases illustrates that the summer rainfall over the Western Ghats and the Arabian Sea has strong interannual variation. Many factors can result in such a variation, including large-scale circulation, CAPE, SST forcing, etc. In section 4c, the role of SSTs in shaping rainfall will be explored.

To further validate the WRF-simulated rainfall against observations, two individual moments are used to compare the WRF output with both TRMM and MODIS products. The two moments are 0900 UTC 24 July 2008 and 0900 UTC 28 July 2008, selected based on the availability of MODIS image for the study domain. Figure 4a shows the MODIS Aqua cloud optical depth for the case of 0900 UTC 24 July 2008. It appears that deep clouds are embedded in large-scale shallow clouds over the Arabian Sea and the Western Ghats. The lee of the Western Ghats shows mostly clear sky. Figure 4b displays the TRMM instantaneous rainfall rate at this time. The locations of heavy rainfall correspond well with those of deep clouds in MODIS. Figure 4c shows the rainfall rate at this time from the WRF simulation. The 1-h accumulated rainfall is used as an approximation of instantaneous rainfall rate in order to compare with TRMM. Although the WRF Model shows large-scale rainfall over the offshore region and the Western Ghats, the exact locations of heavy rainfall clusters do not match with MODIS or TRMM. Note that the WRF simulation was initialized at 0000 UTC 20 July 2008, which is more than four days earlier than the selected moment here. Since we did not use reinitialization by assimilating observational information, it is expected that the model-simulated locations of rainfall may not match with observations exactly. Figures 4d–f show the same comparison for another moment at 0900 UTC 28 July 2008 among MODIS, TRMM, and WRF. In this case, the observations only show coastal clouds and rainfall along the Western Ghats, with heavy rainfall and deep clouds around 20°N over the west coast. The WRF Model also produces rainfall along the Western Ghats. The model does not produce strong rainfall over the offshore region corresponding to the deep clouds around 20°N.

Fig. 4.
Fig. 4.

Comparison between MODIS Aqua cloud optical thickness, TRMM rainfall (mm day−1), and WRF-simulated rainfall (mm day−1) at (a)–(c) 0900 UTC 24 Jul 2008 and (d)–(f) 0900 UTC 28 Jul 2008.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

We conducted another WRF convection-permitting simulation at 2-km horizontal resolution using the same initial and boundary conditions as the 6-km control simulation of the wet case. This simulation lasts for a shorter period (3 days) because of computational cost. The purpose was to explore the sensitivity of the model-simulated precipitation to grid resolution. Figure 5 shows the mean rainfall rate from the 6- and 2-km simulations for their common period. The rainfall rate simulated in the 2-km simulation shows very similar magnitude and spatial distribution to those in the 6-km resolution. In the rest of the paper, only the results from 6-km-resolution simulations are analyzed.

Fig. 5.
Fig. 5.

Comparison of WRF-simulated rainfall (mm day−1) in (a) 6- and (b) 2-km simulations for the wet case, averaged between 0000 UTC 20 Jul 2008 and 0000 UTC 23 Jul 2008.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

4. Results

a. Observed and simulated relation between coastal and offshore precipitation

The apparent spatial coincidence between rainfall and the Western Ghats is well known from observations. However, there are debates in the literature on the exact location of rainfall maxima: whether they are located over the offshore Arabian Sea (e.g., Grossman and Durran 1984; Grossman and Garcia 1990; Ogura and Yoshizaki 1988) or over the coast along the Western Ghats (e.g., Sijikumar et al. 2013; Tawde and Singh 2015; Shrestha et al. 2015). These discrepancies come from different sources of observational data and averaging periods. To better characterize the base state of the spatial distribution of rainfall, here we examine 18-yr TRMM climatology in the boreal summer [June–August (JJA)] averaged over 1998–2015 (Fig. 6). The eastern Arabian Sea is generally drier than the west coast of India. The rainfall increases abruptly closer to the coast (east of 72°E), and reaches its maximum right along the coast. The rainfall slightly decreases over the Western Ghats (according to topography in Fig. 1), and reduces significantly toward the lee of the mountains.

Fig. 6.
Fig. 6.

TRMM 1998–2015 JJA climatological rainfall rate (mm day−1).

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

To examine the location and time evolution of rainfall during the simulation, Fig. 7a shows the Hovmöller diagram of rainfall at latitude 11.5°N from the WRF control simulation for the wet case in 2008. The rainfall occurs mainly over the Arabian Sea during 21–25 July 2008. In the meantime, rainfall along the coastal region of the Western Ghats is limited. During 26–29 July, the offshore rainfall over the Arabian Sea weakens and the coastal rainfall strengthens. Then, during the last three days (27–29 July) of the simulation, the offshore rainfall weakens further and the coastal rainfall reaches its maximum. This inverse association between the offshore rainfall over the Arabian Sea and the coastal rainfall along the Western Ghats suggests a competing mechanism between them.

Fig. 7.
Fig. 7.

Hovmöller diagram at 11.5°N in the WRF CTL simulation of the wet case for (a) rainfall (mm day−1), (b) CAPE (J kg−1), and (c)vertically integrated zonal WVF (kg m−1 s−1). (d)–(f) As in (a)–(c), but for the dry case. The dot–dashed gray line indicates the location of India’s west coast.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

Figure 7b shows the Hovmöller diagram of CAPE from the control simulation for the wet case. Higher incoming CAPE corresponds well with the stronger rainfall rate in Fig. 7a. CAPE over the oceans is generally much larger than over the land. In the earlier period (21–25 July), the offshore rainfall occurs with high CAPE (larger than 2500 J kg−1) over the Arabian Sea, and depletes CAPE that can be advected to its downstream region, producing a coastal region with much lower CAPE. In the later period (26–29 July) without the offshore convection, CAPE increases during the advection from west to east, reaching its maximum at the coast, producing stronger coastal convection. The convection is also associated with large-scale flow and water vapor fluxes. Figure 7c displays the vertically integrated zonal water vapor fluxes (WVF) from the control simulation for the wet case. Strong convection offshore reduces the WVF reaching the coast.

Figures 7d–f show similar Hovmöller diagrams as Figs. 7a–c, respectively, for the dry case in 2009. In Fig. 7d, for the rainfall, the competing mechanism between the offshore rainfall and coastal rainfall identified in the wet case (Fig. 7a) does not exist because the rainfall is mainly located along the coast. The Arabian Sea receives very little rainfall during this period. CAPE is smaller than during the wet period (Fig. 7b) by about 1000 J kg−1 (Fig. 7e). The contrast in CAPE contributes the differences of rainfall between these two periods. The water vapor fluxes are weak and relatively constant over the upstream Arabian Sea (Fig. 7f), compared with the wet case (Fig. 7c).

We further explore the impact of the incoming westerly flow on the regional precipitation by noting the midtropospheric dry layer. According to Parker et al. (2016), the onset of the monsoon rainfall in India is controlled by a layer of dry air in the middle troposphere that suppresses convective development through dry air entrainment. Only when this layer is eroded by shallow moist convection can deeper clouds and precipitation develop. The suppression of moist convection by dry air aloft has also been seen in the study of trade wind precipitation on the Caribbean island of Dominica at a similar latitude of 15°N (Watson et al. 2015; Nugent et al. 2016).

In Fig. 8a, we show the vertical cross section of the dry layer temporally averaged for the dry case. In the incoming westerly flow, a striking dry layer is present at 400–700 hPa. Moist convection gradually erodes this layer with vertical water vapor transport, but it persists and seems to prevent deep convection. In the wet case (Fig. 8b), the dry layer around 600 hPa is much weaker and completely eroded by the time the air reaches India. This allows deep convection to reach the upper troposphere.

Fig. 8.
Fig. 8.

Time-averaged longitudinal–vertical cross section of WRF-simulated relative humidity (%) at 11.5°N for the 10-day simulation of (a) the dry case in 2009 and (b) the wet case in 2008. The white area on the bottom indicates the topography of the Western Ghats.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

Another significant difference between the wet and dry cases is the SST and latent heat fluxes. The slightly warmer SST in the wet case is accompanied by an increased in latent heat flux from about 170 to 250 W m−2.

This distinction between the wet and dry cases emphasizes the importance of environmental factors in controlling precipitation. The wet case has greater water vapor flux, higher CAPE, more latent heat from the ocean, and wetter dry layer aloft than the dry case. In the next section, we address the main subject of this study: the role of the Western Ghats.

b. Impacts of the Western Ghats on precipitation

While the influence of the Western Ghats on regional precipitation has been discussed in previous studies (section 1), here we use a series of convection-permitting simulations to quantify the role of the Western Ghats on regional precipitation. Particularly, we use sensitivity experiments to separate the convection-induced precipitation from the orographic stratiform precipitation. The configurations of the WRF Model experiments are described in Table 1 and section 2.

Figure 9 displays the comparison of temporally averaged rainfall rate in the wet case among the sensitivity simulations (Table 1) by altering the forcing of topography and latent heating. Figure 9a shows the CTL simulation for reference. In the NoM simulation (Fig. 9b), with topography removed, some rainfall still remains over the coast along the Western Ghats. However, compared with the control simulation, the rainfall is much reduced for the coastal regions, especially over the southern part of the Western Ghats (south of 15°N). The offshore rainfall over the Arabian Sea does not show significant change. The rain shadow to the east of the Western Ghats becomes slightly wetter than in the control simulation. In the NoLH simulation (Fig. 9c), the large-scale rainfall disappears because no deep moist convection is allowed to generate in the model. Only a narrow belt of rainfall occurs along the crest of the Western Ghats. This rainfall is stratiform, because of local orographic uplift. When the topography is also removed in the NoMNoLH simulation (Fig. 9d), the stratiform precipitation over the Western Ghats vanishes, too. Therefore, the formation of stratiform precipitation along the Western Ghats crest is merely determined by the topography, while the latent heating plays an important role in the convection over both the Western Ghats region and the offshore Arabian Sea.

Fig. 9.
Fig. 9.

Mean rainfall rate (mm day−1) in WRF simulations for the wet case. The simulations are listed in Table 1.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

Figure 10 displays the comparison of WRF sensitivity simulations for the dry case in 2009. When the topography is removed in the NoM simulation (Fig. 10b), the rainfall maximum along the west coast disappears and the rain shadow becomes wetter. In the NoLH simulation (Fig. 10c), the orographic stratiform precipitation is also located over the crest of the Western Ghats, similar to the wet case.

Fig. 10.
Fig. 10.

As in Fig. 9, but for the dry case.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

In both the wet and dry cases, coastal convection is driven by ascent as a result of frictional convergence, diurnal heating, and upslope flow. The detailed mechanism of convective triggering is almost certainly the “differential LCL” process described by Nugent et al. (2014). With heterogeneous humidity in the incoming flow, the moist parcels hit their lifting condensation level first causing differential virtual temperature and buoyancy. The depth of convection is controlled by the dryness of the midtroposphere air through dry air entrainment (Watson et al. 2015; Nugent et al. 2016).

Over the Western Ghats region, both convection-induced rainfall and orographic stratiform rainfall exist as suggested by the above comparisons. We use the rainfall in the NoLH simulation as an approximation for the orographic stratiform rainfall, given that there is no deep moist convection in that case. The stratiform precipitation comes from shallow “cap clouds” right over the crest of the Western Ghats. We do not claim that convection could not influence the cap cloud. The convection could seed the cap cloud with hydrometeors, or convective downdrafts could modify the environment. Further, we are not confident that we modeled the microphysics of these clouds correctly. It is well known in the forest ecology literature on “cloud forests” that a significant fraction of precipitation on the Western Ghats hilltops comes from “leaf drip”; the capture of cloud droplets or drizzle by leaves (Bruijnzeel and Proctor 1995). This process is not included in the model. Also, the precipitation efficiency of these cap clouds may be sensitive to poorly parameterized aerosol concentrations and processes.

To further examine these two types of rainfall over the mountains, Fig. 11 shows detailed comparisons between the control and NoLH simulations for the Western Ghats region. The orographic stratiform rainfall estimated from the NoLH simulations (Figs. 11b,d) occurs exactly over the crest of the Western Ghats, which proves that this stratiform rainfall is induced by the mountain topography. The control by mountains on this stratiform rainfall is also suggested by the removal of the stratiform rainfall in the NoMNoLH simulations (Figs. 9d and 10d). In the wet case, over the mountain crest, the amount of the orographic stratiform rainfall is up to 100 mm day−1 (Fig. 11b), which approximately equals to the rainfall over the crest in the control simulation (Fig. 11a). The clusters of heavy rainfall (up to 100 mm day−1) over the coastal plain in the control simulation (Fig. 11a) are due to convection, because they vanish in the NoLH simulation (Fig. 11b). In the dry case, the orographic stratiform rainfall over the crest is up to 60 mm day−1 (Fig. 11d), which is also comparable to that in the control simulation (Fig. 11c). We conclude that the orographic stratiform rainfall dominates in a small region on the crest of the Western Ghats. More broadly, however, the amount of stratiform rainfall is only a small fraction of total rainfall in the region. Most of the rainfall over the Western Ghats region is induced by convection.

Fig. 11.
Fig. 11.

Coastal region mean rainfall rate (mm day−1; shaded) for (a) CTL of the wet case, (b) NoLH of the wet case, (c) CTL of the dry case, and (d) NoLH of the dry case. Contours show elevation (m). Note the color scale is different from previous maps; it is shifted to show more high rainfall rates.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

The impacts of the Western Ghats on the diurnal cycle of rainfall are examined in Fig. 12, using area-averaged rainfall over a coastal region (10°–14°N, 75°–77°E, see Fig. 1). There are two accepted mechanisms for generating convection over tropical mountains: 1) thermally triggered convection resulting from surface heating and 2) mechanically triggered convection caused by forced lifting of the incoming flow (Nugent et al. 2014). Thermally triggered convection typically has an afternoon maximum in the diurnal cycle. For the wet case, the control simulation (see Table 1) shows a diurnal cycle with an afternoon maximum at 1800 local solar time (LST). In the NoM simulation, the diurnal cycle remains the same but the magnitude is reduced. The difference between the control and NoM simulations mostly comes from the effect of mechanically triggered convection, and the diurnal cycle in the NoM simulation is controlled by thermally triggered convection. Both mechanisms operate in the control simulation. In the NoLH simulation, the diurnal cycle of orographic stratiform rainfall is weak with an afternoon minimum. For the dry case, a weak diurnal cycle with an afternoon peak is shown in both control and NoM simulations. The mechanically triggered convection still dominates. The orographic stratiform rainfall in the NoLH has a similar diurnal cycle to that in the wet case with an afternoon minimum. We suggest that the afternoon surface heating reduces the relative humidity within the boundary layer, thereby reducing the hilltop stratiform precipitation.

Fig. 12.
Fig. 12.

Diurnal cycle of rainfall (mm day−1) over the coast (10°–14°N, 75°–77°E; see Fig. 1 for the location) averaged in the 10-day simulations of the wet case (solid lines) and the dry case (dashed lines). The time shown is LST, which is 6 h ahead of UTC time.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

c. Impacts of the SSTs of the Arabian Sea on precipitation

The large-scale SST forcing is analyzed to understand its influence on offshore rainfall. Figures 13a and 13b show the SST inputs at the beginning of the wet case and the dry case simulations, respectively. The SST is relatively constant during these 10-day simulations. The SST in the wet case is about 1-K higher than in the dry case, especially near the west coast of India.

Fig. 13.
Fig. 13.

Initial SST (°C) in the boundary conditions of the WRF CTL simulation of (a) the wet case and (b) the dry case.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

We first use long-term records of observations during JJA 1998–2014 to investigate the relation between the SST anomalies in the Arabian Sea and rainfall over both the offshore Arabian Sea and the coastal region of the Western Ghats (Fig. 1). Because the SST has slow synoptic variations, monthly SST and precipitation data are used here. Figure 14 shows the monthly mean relation between SST in the Arabian Sea and offshore and coastal rainfall during JJA 1998–2014. While the scatter is relatively large, the SST is significantly correlated with rainfall over the offshore Arabian Sea, but not with the coastal rainfall. Note that this correlation is based on monthly data. During intraseasonal time scales, the SST may also influences the coastal rainfall indirectly through the inverse associate between offshore and coastal rainfall as shown in section 4a. The lack of correlation between Arabian Sea SST and coastal precipitation is because SST can work two ways. If it is too large, it promotes offshore convection that steals CAPE and WVF and reduces coastal rainfall. With SST in a smaller range, increases of SST can enhance coastal rainfall.

Fig. 14.
Fig. 14.

Relation between NOAA OI monthly SST (°C) averaged in the eastern Arabian Sea (10°–14°N, 68°–74°E) and TRMM monthly rainfall rate over two study regions: offshore Arabian Sea (blue; 10°–14°N, 72°–74°E) and west coast of India (red; 10°–14°N, 75°–77°E) during JJA 1998–2014. The correlation coefficient between SST and offshore rainfall is 0.38 with a p value of 0.0056. The correlation coefficient between SST and coastal rainfall is 0.07 with a p value of 0.6187.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

To better explain the role of SST in shaping the regional precipitation, a WRF sensitivity simulation is conducted by adding an idealized pattern of warm SST anomalies to the default SST forcing for the wet case. Figure 15 displays the idealized pattern of SST anomalies, which is defined as a peak of 2 K at 12°N, 71°E, and the SST anomaly linearly decreases from 2 K at the peak to 0 K at a radius of 350 km. This warm pool pattern is added to the default SST input data constantly at each of the 6-hourly time steps. The added warm pool results in an increase of both sensible heat flux (up to 37 W m−2 in the center of the warm pool) and latent heat flux (up to 220 W m−2 in the center of the warm pool) from the ocean surface. Note that the maximum change of latent heat flux (220 W m−2) is comparable to the latent heat flux from the Arabian Sea in the control simulation.

Fig. 15.
Fig. 15.

SST anomalies (K) added to the WRF SST simulation, with a maximum of 2 K at 12°N, 71°E and a radius of 350 km.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

Figure 16 shows the time series of rainfall over the offshore and the west coast from both CTL and SST simulations for the wet case. We define two periods as follows: period 1 from 0000 UTC 20 July 2008 to 0000 UTC 26 July 2008, during which the rainfall occurred mainly over the offshore Arabian Sea, but the coastal region is relatively dry; and period 2 from 0000 UTC 26 July 2008 to 0000 UTC 30 July 2008, during which the offshore rainfall is reduced significantly and the rainfall is concentrated along the coastal region of the Western Ghats (see Fig. 7a). The temporally averaged differences of rainfall and CAPE for these two periods are shown in Fig. 17. The changes of rainfall are small in the upstream direction to the west, but prominent over the region with added SST anomalies and the downstream direction to the east.

Fig. 16.
Fig. 16.

Time series of rainfall rate (mm day−1) averaged over the offshore region (blue; 10°–14°N, 72°–74°E) and coastal region (red; 10°–14°N, 75°–77°E) in the CTL (solid lines) and SST (dot–dashed lines) simulations for the wet case. The x axis shows time at 0000 UTC. The averaging regions are shown in Fig. 1.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

Fig. 17.
Fig. 17.

Differences of (a) rainfall rate (mm day−1) and (b) CAPE (J kg−1) between the SST sensitivity simulation and the CTL simulation (SST minus CTL) for period 1. (c),(d) As in (a),(b), but for period 2. The dashed circle indicates the SST warm pool as shown in Fig. 15.

Citation: Journal of Climate 31, 8; 10.1175/JCLI-D-17-0002.1

During period 1, the offshore rainfall is increased generally in the SST simulation compared to the control simulation (Fig. 17a). The coastal region shows little change because both SST and control simulations produces negligible rainfall along the coast. The increase of offshore rainfall is associated with an overall increase of CAPE over the location where the SST anomalies are applied (Fig. 17b). CAPE is reduced in the downstream coastal region as a consequence of the consumption of CAPE by the enhanced convection over the Arabian Sea.

During period 2, in the absence of offshore rainfall, the SST simulation produces an increase of rainfall over the near-coast region close to the west coast of India (Fig. 17c). This enhanced rainfall is associated with an increase of CAPE in its upstream direction (i.e., to the west) and also causes a reduction of CAPE in the vicinity of the west coast and the downstream Western Ghats (Fig. 17d).

5. Conclusions

The summer precipitation over the Western Ghats in India and the adjacent Arabian Sea is explored by conducting realistic WRF convection-permitting simulations. The physical processes controlling the summer precipitation are analyzed through a series of sensitivity tests. Both a 10-day wet case in summer 2008 and a 10-day dry case in summer 2009 are investigated to generalize the findings in this study. The Arabian Sea environment differs for these two cases with respect to water vapor flux, SST, CAPE, and dry air aloft. Rainfall in the WRF control simulations for the wet and dry cases approximately agrees with TRMM and MODIS satellite estimates. The WRF Model captures the spatial distribution of rainfall over the upstream Arabian Sea, coastal plain, Western Ghats, and its downstream rain shadow. Besides the simulations at 6-km resolution, a sensitivity simulation is conducted with a 2-km resolution using the same domain. The rainfall is similar between these two simulations.

The rainfall maximum from TRMM climatology is located over the flat coastal plain, slightly upstream of the highest Western Ghats terrain. For the wet case in summer 2008, rainfall occurs over both the Arabian Sea and the coastal region of the Western Ghats. The inverse association between offshore rainfall and coastal rainfall is related to the competition for CAPE. For the dry case in summer 2009, rainfall is concentrated over the coastal region, weaker than the wet case. CAPE and zonal water vapor fluxes are weaker and the dry layer is more pronounced.

The impacts of the Western Ghats on both convection-induced rainfall and orographic stratiform rainfall are quantified in a series of WRF experiments by altering the topography and latent heating. The offshore rainfall is not controlled by the Western Ghats. Over the west coast of India, both convection-induced rainfall and orographic stratiform rainfall occur. Except on the mountain crest, the amount of stratiform rainfall is a small fraction of total rainfall, indicating that most of the coastal rainfall (except the crest of the Western Ghats) is produced by convection. The Western Ghats enhances convection-induced rainfall over the coastal region and produces a rain shadow to the east. On the crest of the Western Ghats, the orographic stratiform rainfall dominates.

To understand to role of SST in the Arabian Sea in determining regional precipitation, an SST sensitivity simulation is conducted for the wet case. An idealized warm pool of SST anomaly is added to the default SST forcing. With this added warm pool of SST, the rainfall increases over the warm pool region during the period when the offshore rainfall dominates. Then, in the period of prevailing coastal rainfall, the SST simulation produces more rainfall over the near-coast region close to the Western Ghats. These modeling results agree with the observed relation between SST and offshore precipitation during JJA 1998–2014.

An improved understanding of the physical processes controlling regional precipitation over the Western Ghats is important for advancing weather and climate prediction for regional precipitation and also for the large-scale Indian summer monsoon. This study contrasts the different characteristics and causes of 1) deep offshore convective rainfall over the Arabian Sea, 2) deep coastal convective rainfall just upstream and over the Western Ghats, and 3) shallow orographic stratiform rainfall on the hilltops. Future work needs to recognize the distinction between these three precipitation systems as well as the physical processes of competition for CAPE, dry air entrainment, deep mixed-phase convection, coastal convergence, and shallow orographic warm rain.

Acknowledgments

This research is supported by National Science Foundation (NSF) fund Grant AGS-1338655: Deep Propagating Gravity Wave (DEEPWAVE). The WRF Model simulations are conducted on the National Center for Atmospheric Research (NCAR) Yellowstone Supercomputer. TRMM and MODIS products are provided by the National Aeronautics and Space Administration (NASA). The SST data are provided by the National Oceanic and Atmospheric Administration (NOAA). We also appreciate suggestions from Christopher Kruse, William Boos at Yale University, and René Garreaud at Universidad de Chile. Thanks also to three anonymous reviewers for their helpful comments.

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