1. Introduction
A large part of southern India receives most of its annual precipitation during October–December. During these months, the intertropical convergence zone (ITCZ) lies over the northern flank of the equatorial Indian Ocean; northeasterly winds prevail over the Bay of Bengal (BoB) and the south Indian peninsula (Fig. 1). This period of the year is known as “winter monsoon” or “northeast monsoon” (Rajeevan et al. 2012). During this period, low pressure systems (LPSs) often develop over the eastern equatorial Indian Ocean and make landfall at the east coast of India by moving northwestward due to the planetary β effect (Chan 2005). Some of the LPSs grow into tropical cyclones (TCs).
Map showing 950-hPa winds’ climatology, orography, and other geographical features mentioned in the study. Shading indicates height of the orography from ETPO5 data (300-, 500-, 750-, 1000-, and 1200-m contours are plotted); vectors indicate ERA-Interim winds’ (1° grid spacing) climatology at 950 hPa (October–December 2006–15).
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Unlike the Western Ghats mountain range—which lies more or less along the entire west coast of India—the Eastern Ghats mountain range is about 200 km away from the eastern coastline and is discontinuous (Fig. 1). The slope of the Eastern Ghats is also gentler. Along the latitude of Chennai, the mean plateau height is about 750 m. During winter monsoon, winds impinge on the Eastern Ghats from the eastern side.
A strong El Niño–Southern Oscillation (ENSO) event influenced the 2015 Northern Hemisphere winter. ENSO conditions are favorable for winter monsoon over India (Rasmusson and Carpenter 1983; Ropelewski and Halpert 1987; Zubair and Ropelewski 2006; Kumar et al. 2007). The winter monsoon of 2015 was highly active. Three heavy rainfall incidences occurred over Chennai during this season: 7–9 November, 14–16 November, and 30 November–2 December (Chakraborty 2016). Synoptic- and planetary-scale analysis of this season can be found in Chakraborty (2016). Here, the focus is on the extreme rainfall of 1 December 2015. During this event, the precipitating system remained stationary over Chennai. As a result, the accumulated rainfall crossed the 350-mm mark within 24 h at several weather stations in the city, with 494 mm being the highest at Tambaram station—at least a 100-yr rainfall event (Narasimhan et al. 2016; van Oldenborgh et al. 2016). On 2 December, Chennai was declared a “disaster zone” by the state government. Rain-related incidents killed at least 250 people (https://earthobservatory.nasa.gov/IOTD/view.php?id=87131).
Although Chennai is about 200 km away from the Eastern Ghats, observations elsewhere show that precipitation enhancement can occur away from the mountains in the upwind direction in orographically blocked flow cases (Houze et al. 2001; Xu et al. 2012; Viale et al. 2013). Mountain–valley breeze and the associated local convection is the simplest way in which the orography can induce precipitation. However, in many cases, interaction between the mean background winds and moist processes complicates the orographic influence on precipitation (Houze 2012). Moist low-level jets (LLJs) are often cited as one of the primary causes of heavy orographic precipitation (Lin et al. 2001). The precipitation pattern in the vicinity of the orography depends on the Froude number 
Moist processes such as latent heating and evaporative cooling during precipitation can alter the 
The southwest coast of Taiwan is a good example where the mechanisms discussed before are at work. During the East Asian monsoon, the lower-tropospheric southwesterly monsoonal winds flow over the Central Mountain Range (CMR) of Taiwan, which is about 100 km inland. As a result, heavy rainfall events often occur over the southwest coast of Taiwan. This is one of the most studied regions for the orographic influence on precipitation (Chen et al. 1991; Akaeda et al. 1995; Chen and Li 1995; Li et al. 1997; C.-S. Chen et al. 2005; G. T.-J. Chen et al 2005; Zhang et al. 2003; Davis and Lee 2012; Xu et al. 2012). Similarly, copious rainfall over the west coast of India in the summer monsoon season is also a result of an interaction of westerly monsoonal flow with the Western Ghats mountains along the coast (Grossman and Durran 1984; Smith 1985; Ogura and Yoshizaki 1988; Xie et al. 2006). In a two-dimensional modeling study, Ogura and Yoshizaki (1988) showed that the inclusion of ocean surface fluxes in the model localizes the maximum rainfall amount over the coast. Thus, it seems that moist processes are crucial in the localization of rainfall. Over the east coast of peninsular India, the topographical settings are similar to those of the west coast of Taiwan. However, the mean altitude of the Eastern Ghats (
This paper analyzes the orographic influence of the Eastern Ghats on the 1 December 2015 extreme rainfall event over Chennai and its surrounding region. The immediate aim of this case study is not to improve the severe weather forecast over this region. Improvements in numerical weather models and a comprehensive analysis of climatology of extreme rainfall events are necessary for that purpose (Schultz 2010). Individual case studies such as this collectively can lay the foundation for such studies. Moreover, as stated before, the influence of the Eastern Ghats on the impinging easterly flow has not been studied before. This case study presents an analysis of this interaction. A better understanding of the flow dynamics over any mountainous region is essential for improving the weather forecast over that region. Thus, even though the primary aim of this paper is to explain the stated extreme rainfall event over Chennai, understanding gained from this analysis can be useful for improving the general weather forecast over this region. This case study uses data from a reanalysis product, satellites, and the local soundings. A nonhydrostatic numerical model is used for carrying out experiments, which ratify the orographic blocking mechanism. Section 2 gives a detailed description of the datasets used in this study and analyzes the 1 December rainfall event with these datasets. Section 3 presents the numerical model setup and the results of the modeling experiments. Section 4 concludes the study.
2. Event description
a. Data
For the presentation of meteorological fields such as pressure, winds, and the total column water vapor (TCWV), the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011) is used. These fields are available at 6-hourly {at 0000, 0600, 1200, and 1800 UTC [India standard time (IST) = UTC + 0530]} intervals. The vertical structure of the atmosphere over Chennai during the event is studied from the radiosondes released at the local weather station. Soundings were obtained from the University of Wyoming web portal (http://weather.uwyo.edu/upperair/sounding.html). Infrared (IR) images from the geostationary satellite Kalpana-1 (located over 74°E) are used to observe the convective organization of the system. The pixel size of these IR images is about 8 km at subsatellite point, and images are available at half-hourly intervals. These images were obtained from the Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC), Indian Space Research Organization (ISRO). (Images are archived at http://www.mosdac.gov.in/.) Rainfall data from the National Aeronautics and Space Administration (NASA)’s Integrated Multisatellite Retrievals for GPM Core Observatory satellite (IMERG) (Huffman et al. 2015) are used for showing the rainfall accumulation on 1 December 2015. And the 3B42 product of the Tropical Rainfall Measuring Mission (TRMM) (Huffman and Bolvin 2014), also provided by NASA, is used for showing the climatological rainfall over the South Asian region. IMERG 3IMERGHH is available at half-hourly intervals with 0.1° grid spacing, and TRMM 3B42 gives a daily accumulated rainfall with 0.25° grid spacing.
b. Precipitation
Figure 2a shows the climatological rainfall during the winter monsoon months over Southeast Asia from the TRMM 3B42 daily precipitation data. The mean precipitation rate over the east coast of India during the season is around 8–10 mm day−1. Figure 2b shows the IMERG accumulated precipitation between 0000 UTC 1 December and 0000 UTC 2 December 2015. The maximum rainfall accumulation is about 400 mm, and it lies along the coast just to the south of Chennai. Notice that the rainfall accumulation contours run along the coast and the Eastern Ghats orography. The location of maximum rainfall and its north–south organization suggest a significant influence of the local topography. Rainfall amounts decrease as one approaches the orography. Similar features are also seen in the climatological rainfall in Fig. 2a.
(a) TRMM precipitation climatology for October–December 1998–2014; (b) iMERG accumulated precipitation for 0000 UTC 1 Dec–0000 UTC 2 Dec 2015.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Figure 3 shows rainfall time series at the Nungambakkam station in Chennai from 1200 UTC 30 November to 1200 UTC 2 December. It shows that there were some instances (1200 and 1500 UTC) at which significant rainfall was recorded on 30 November. At 0100 UTC 1 December, rainfall picked up and lasted until about 0000 UTC 2 December. There was no break in the rainfall during this period. Total rainfall accumulation from 0000 UTC 1 December to 0000 UTC 2 December is 310 mm at this station.
Rain gauge time series from the Nungambakkam station in Chennai.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
c. Synoptic–dynamics analysis
Before moving into the synoptic analysis, the planetary-scale atmospheric conditions will be reviewed briefly. The Northern Hemisphere winter of 2015 was under the influence of strong ENSO conditions. During the ENSO phase, there are anomalous easterly winds over the BoB; this leads to anomalous moisture convergence over the Indian peninsula. Thus, positive ENSO phases give surplus winter monsoon rainfall accumulations over this region (Zubair and Ropelewski 2006; Kumar et al. 2007). According to the Bureau of Meteorology, Australia (http://www.bom.gov.au/climate/mjo/), the Madden–Julian oscillation (MJO) activity during 27 November–1 December 2015 was significantly strong and was situated over the Maritime Continent, that is, phase 4 (Wheeler and Hendon 2004). During the passage of MJO, the enhanced convection over the eastern equatorial Indian Ocean often forms LPSs that propagate northwestward, making landfall over the east coast of India. Thus, both factors ENSO and MJO were favorable for the enhancement of convective activity over southern India and the surrounding ocean during this extreme precipitation event. A preliminary dynamic analysis of the synoptic weather pattern with respect to the 1 December extreme rainfall event is presented in this subsection. ERA-Interim data are used for this purpose. A thermodynamic analysis from the local sounding data will be presented in next subsection (section 2d).
Figure 4 shows 850-hPa winds, geopotential contours, and moisture flux in shading from the 1° ERA-Interim data. On 28 November, an easterly wind surge carrying moisture flux was observed over the eastern BoB. This low-level easterly jet (LLEJ) entered the bay from the South China Sea (SCS). On subsequent days, the LLEJ moved westward and prevailed over the east coast of India on 29–30 November. This jet supplied moisture to the east coast of peninsular India. Simultaneously, an LPS development was underway near the southeast coast of Sri Lanka on 28–29 November. A well-organized LPS was located over that region on 30 November. Thereafter, like most of the LPSs that develop over this region, it started moving northwestward. On 1 December, the LPS was seen over Sri Lanka. Although the LPS became better organized after the wind surge event, it did not transform into a depression [India Meteorological Department (IMD) classifies an LPS as a “depression” when the system has two or three closed isobars at 2-hPa interval and wind speed from 17 to 27 kt (about 9–14 m s−1) at sea level]. Intensification and propagation of preexisting quasi-stationary synoptic disturbances after northeasterly wind surge events over the SCS are reported in Chang et al. (1979) and Chang and Lau (1980). A dynamic analysis of the interaction between the easterly wind surge event and the LPS in this case is out of the scope of this study. Rather, the focus of this study is on the mesoscale dynamics of the stationary precipitating system formed over the coast on 1 December. It is hypothesized that the stationary system was a result of an interaction between the enhanced low-level moist winds and the Eastern Ghats orography.
ERA-Interim winds, geopotential contours, and moisture flux in shading (g kg−1 m s−1) at 850-hPa level with 1° grid spacing. (a) 0000 UTC 28 Nov, (b) 0000 UTC 29 Nov, (c) 0000 UTC 30 Nov, and (d) 0000 UTC 1 Dec 2015.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Development of convection during this period is depicted in Fig. 5. IR brightness temperature from the geostationary satellite Kalpana-1 is taken as a proxy for the convection. Convection developed over the BoB at the location of wind surge. It moved westward along with the wind surge. On 30 November, some convective activity was seen along the east coast of India, but the main convective envelope was still far off the coast. By 1 December, the main convective system was over the coast, where it remained stationary for around 24 h. As the day progressed, the convection acquired a linear north–south organization along the coast (Fig. 5d). On 2 December, the convection was over a coastal region well south (about 11°N) of Chennai, and then moved eastward away from the coast.
Kalpana-1 satellite IR images: (a) 0000 UTC 29 Dec, (b) 0600 UTC 30 Nov, (c) 0000 UTC 1 Dec, (d) 1300 UTC 1 Dec, (e) 1200 UTC 2 Dec, and (f) 0000 UTC 3 Dec 2015. A cutoff of IR brightness temperature 
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
To analyze the interaction of winds with the orography, the low-level winds (LLWs) at 950 hPa are considered. Figure 6 shows 950-hPa winds and TCWV from 0.75° ERA-Interim data from 28 November to 1 December. At 950 hPa, LLWs from the BoB were deflected southward over the coast during this period. Winds over the coast resembled a barrier jet formed due to the orographic blocking (Parish 1982; Chen and Smith 1987). The direction of deflection is likely a result of the semigeostrophic balance within the barrier layer (Pierrehumbert and Wyman 1985). Thus, it is hypothesized that the deflection of LLWs along the coast was due to the Eastern Ghats orography. On 29 November, the TCWV increased to about 60 kg m−2 with the advection of moisture flux from the bay, as seen in Fig. 4b. The effect of this large-scale moistening on the local thermodynamic processes is assessed in section 2d. Thereafter, conditions for the orographic blocking are discussed in section 2e.
ERA-Interim 950-hPa winds and TCWV in shading (both with 0.75° grid spacing). (a) 0000 UTC 28 Nov, (b) 0000 UTC 29 Nov, (c) 0000 UTC 30 Nov, and (d) 0000 UTC 1 Dec 2015. LLWs were blocked by the Eastern Ghats orography.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
In Figs. 4d and 6d, cyclonic circulation of the LPS on 1 December is seen over Sri Lanka. The LPS remained quasi stationary on 2–3 December north of it, and then went around the peninsula by moving southward (Fig. 7). Blocking and southward deflection of TCs by the topography have been reported along the CMR in Taiwan (Lin et al. 2005) and Sierra Madre in Mexico (Zehnder 1993). Because of the planetary β effect, a general tendency of a cyclonic vortex embedded in the weak mean flow is to move northwestward. When the lower-level peripheral winds of vortex are blocked by the orography on the western side, the topographic β effect (Carnevale et al. 1991; Zehnder 1993; Kuo et al. 2001) dominates over the planetary β effect, and the vortex travels a clockwise path around the orography. Diabatic processes strengthen the vertical coupling in the cyclonic vortices, and the motion of the cyclone is linked to the lower-level circulation (Hsu 1987). Thus, obstruction at the orographic levels is also felt above that. This is probably the first study that shows the topographic blocking of cyclonic vortex over India.
ERA-Interim 850-hPa winds and geopotential contours (both with 1° grid spacing). (a) 0000 UTC 3 Dec, (b) 0000 UTC 4 Dec, (c) 0000 UTC 5 Dec, and (d) 0000 UTC 6 Dec 2015. Gray dots roughly show the location of the center of the LPS. Clockwise movement of the LPS around the peninsula is in consensus with the topographic β-effect theory.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
d. Thermodynamics
Figure 8 shows the atmospheric soundings from the radiosondes released from Chennai on 30 November (Figs. 8a,b) and 1 December 2015 (Figs. 8c,d). The most striking feature of these profiles is that the entire depth of the atmosphere is moistened near the saturation. Moistening of the mid- and upper troposphere is regarded as one of the dominant precursors of deep convective outbreak in the tropics (Brown and Zhang 1997; Sherwood 1999). When the atmosphere has near-saturation vertical profile, dilution of buoyancy and cloud liquid water in convective cores due to entrainment is less; thus, the rainfall efficiency and probability of extreme precipitation in such conditions is very high (Doswell et al. 1996). As far as the orographic blocking is concerned, the cold pool produced in such a moist subcloud layer is relatively weak. It is swept downstream by the background winds. In the presence of downstream orography, a piling up of cold pool and stationary convective systems is most likely in such situations. In case of drier profiles, strong cold pools can drive the precipitation systems in the upwind direction (Miglietta and Rotunno 2010). Near-saturation atmospheric profiles prevailed from 29 November (not shown) due to the moisture transport by the enhanced easterly winds (Figs. 4b, 6b). Precipitable water (PWAT) on 29 November increased to 63 kg m−2 from 32 kg m−2 on an earlier day. Observations show that beyond a critical value of PWAT, rainfall increases sharply (Bretherton et al. 2004; Peters and Neelin 2006; Holloway and Neelin 2009). Throughout the event, PWAT was more than 60 kg m−2.
Soundings from Chennai: (a) 0000 UTC and (b) 1200 UTC 30 Nov 2015; and (c) 0000 UTC and (d) 1200 UTC 1 Dec 2015. Solid line shows the dry-bulb temperature, dashed–dotted line shows the dewpoint temperature, and dashed line shows the pseudoadiabat.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
On 1 December (Figs. 8c,d), southward deflection of easterly LLW is clearly seen—LLWs within the lowest 1 km become northeasterly from the easterlies above that level (reanalysis wind fields agree with this). At 1200 UTC 30 November, such blocking was not seen (Fig. 8b).
Note that at 0000 UTC 1 December, the subcloud layer was very moist but was still subsaturated at all levels. There was a scope for the evaporation of raindrops during precipitation. At 1200 UTC, there were deep clouds and rainfall over the sounding station; as a result, the sounding shows saturated profile below 600 hPa right to the surface. This saturation is likely a result of evaporation of cloud and raindrops. As said before, moist subcloud layer gives a weak cold pool, as evaporative cooling is weak. Therefore, a strong signature of cold pool is not seen in the soundings at the coast. Prior to the commencement of 1 December rainfall, moderate convective instability (CAPE 
e. Orographic blocking
Prior to the arrival of the convective system, an estimate of the 
A dynamic increase in the N values (and hence, lowering of 
For the 1200 UTC 1 December sounding, U 
In the next section, the mechanism of orographic blocking is discussed in detail. The role played by moist processes in localizing the convection is also analyzed. Experiments with a nonhydrostatic numerical model are performed for these objectives.
3. Numerical model experiments
a. Model description
The Advanced Research (ARW) version of the Weather Research and Forecasting (WRF) Model (WRF-ARW; Skamarock et al. 2008) was used for the experiments. The model was run in a nonhydrostatic mode. The simulation done with a single domain having 10-km horizontal grid spacing could not produce the observed intensity of rainfall. Thus, two nested domains (Fig. 9) were used. The outer domain has 10 km, while the inner domain has 3.33-km horizontal grid spacing. The nesting was a two-way nesting; that is, the fine grid domain feedback affected the coarse grid domain solution. With this setup, the simulated rainfall was closer to the observed rainfall. The aim of the study is to prove the orographic blocking of winds and the resulting cloud stagnation. Model output with 10-km grid spacing is sufficient for this purpose. Thus, only output of 10-km domain was used for the analysis (since two-way nesting was employed, the coarse structure of the rainfall in the two domains was similar). Terrain following 30 vertical sigma levels in the atmosphere were used in the simulation. However, for comparing the model output with the observed fields, the model output was reproduced on pressure levels. Initial and 6-hourly boundary conditions for the simulation were provided from the National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analyses data. The horizontal grid spacing of FNL data is 1° × 1°. These data are provided at the surface and at 26 pressure levels from 1000 to 10 hPa. The following parameterization schemes were used: Betts–Miller–Janjić (BMJ) scheme (Janjić 1994, 2000) for cumulus convection, WDM5 microphysics scheme (Lim and Hong 2010) for cloud microphysics, and Yonsei University (YSU) scheme (Hong et al. 2006) for boundary layer processes. These schemes were used in both domains. The simulation begins at 0000 UTC 30 November and ends at 0000 UTC 3 December 2015.
Nested domains for WRF-ARW simulations. The dashed box shows the region from which the orography was removed in the NOTOPO run.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
The grid spacing of the finer domain lies in the “gray zone” (3–8 km) of cumulus parameterization (CP) (Gerard et al. 2009). At this grid spacing, the motion of convective cells is not fully resolved. On the other hand, some of the assumptions made for parameterizing convection are valid only at grids coarser than 10 km. Thus, using CP schemes in the gray zone is dubious. Nevertheless, there are several cases in the literature where usage of some form of CP has improved the model performance in the gray zone (Deng and Stauffer 2006; DuVivier et al. 2017; Lean et al. 2008; Roberts and Lean 2008; Sun et al. 2014). Mahoney (2016) validated several CP schemes in the gray zone for simulating an extreme rain event over Colorado. This study showed that the BMJ scheme simulated the extreme rainfall locations and intensities satisfactorily. In the present case, explicit convection in the 3.33-km domain did not simulate the observed rainfall correctly—the rainfall intensity was weaker, and it was placed over ocean. However, testing the model for its predictability is not the subject of this paper. The reasons for this model behavior are not investigated here. CP is used in the finer domain for all model experiments.
To substantiate the role of the orography in the flow blocking, the WRF-ARW model was run with an identical setup for the following cases: 1) control run with the actual orography (CTL) and 2) without the peninsular orography (NOTOPO). The region from which the orography was removed for the NOTOPO run is shown in Fig. 9 by the dashed box. For this experiment, the land elevation over the dashed box was made zero for both domains. The differences between the two simulations are presented in this section first. The role of evaporative cooling and the cold pools is relatively subtle. To explain this role, another model experiment, 3) with the actual orography but with no evaporative cooling of raindrops (NOEVAP), is done. Results of this experiment are presented at the end of the section.
b. Precipitation
Figure 10 shows rainfall accumulation during 0000 UTC 1 December–0000 UTC 2 December for the CTL (Fig. 10a) and the NOTOPO (Fig. 10b) runs. Compared to the rainfall accumulation observed by the Core Observatory satellite (Fig. 2), the CTL run simulates the location of maximum rainfall accumulation and the intensity correctly, but underestimates its areal spread. In the NOTOPO run, the rainfall accumulation has reduced substantively. The maximum rainfall accumulation in the NOTOPO case is about 200–250 mm, compared to 400 mm in the CTL run. Further, note that the localization of rainfall along the coast is broken down in the NOTOPO case. This suggests that the orography has played a vital role in localizing the convection during the event.
Accumulated rainfall of WRF-ARW simulation during 0000 UTC 1 Dec–0000 UTC 2 Dec 2015 for (a) CTL and (b) NOTOPO runs. Rectangular box around precipitation area shows the region over which precipitation is averaged for producing the time series in Fig. 11.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Figure 11 shows the time series of accumulated precipitation averaged over the boxes in Fig. 10. On 1 December, precipitation picks up sharply in the IMERG observation. Until 0000 UTC 1 December, the mean rainfall accumulation is just 50 mm, which rises to 350 mm at 0000 UTC 2 December. In the CTL run, precipitation also picks up on the same day. Nevertheless, the pickup is not as sharp as in the IMERG observation. In the NOTOPO run, such sharp pickup on 1 December is absent. Hence, the record heavy precipitation on 1 December was assisted by the orography. On 30 November, the CTL and the NOTOPO precipitation accumulation is nearly equal. In section 2c, it was mentioned that the convective system was still over ocean on 30 November. The western edge of the system was near the coast, and it gave some rainfall over the coast. It is likely that the orographic assistance for the 30 November rainfall was minimal. Note that the mesoscale numerical model takes an initial few hours (6–12 h) for the spinup. Dynamical fields after 12 h are considered for the analysis in this study. Any dynamical analysis of 30 November rainfall is not intended.
Time series of the accumulated precipitation spatially averaged over a rectangular box shown in Fig. 10.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
c. Cloud systems
To show the propagation of the cloud systems in the model simulations, cloud liquid water path (LWP) is plotted. LWP is the total weight of the liquid water droplets in the atmospheric column above a unit surface area. Figure 12 shows the LWP in the CTL and NOTOPO runs. The evolution of cloud systems in the CTL run (Figs. 12a,b) is similar to the observations in Fig. 5. At 0000 UTC 1 December, the cloud system is anchored over the coast at the same location as seen in Fig. 5c. The cloud system in the CTL run remained stationary over the coast throughout the day. Note that maximum values of LWP are located right over the coast. In the NOTOPO run, at 0000 UTC 1 December, the location of the cloud system is similar to that in the CTL run (Fig. 12c). But the LWP values are nearly half of those in the CTL run. The subsequent evolution of cloud systems is entirely different—cloud systems moved inland in the NOTOPO run. By 1200 UTC 1 December (Fig. 12d), clouds formed far inland. Clouds covered a large inland area from the east coast. Maximum LWP values are produced far inland, whereas LWP values are reduced substantively over the coast.
Cloud LWP on 1 Dec 2015 in CTL run at (a) 0000 UTC and (b) 1200 UTC, and in NOTOPO run at (c) 0000 UTC and (d) 1200 UTC.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
d. Dynamics
Figure 13 shows 950-hPa winds and the virtual potential temperature 
Winds and virtual potential temperature 
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Figure 14 shows the evolution of rainfall along the coast and the perturbation potential temperature (
Evolution of (a) rainfall and (b) perturbation virtual potential temperature 
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Figure 15 shows the evolution of N and 
Evolution of (a) Brunt–Väisälä frequency N, (b) zonal winds U, and (c) Froude number (
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Orography-induced pressure perturbations slow down the flow well ahead of the orography. Determining the true kinetic energy of the flow impinging on the mountain, hence, is not a straightforward task. Therefore, calculation of 
On 1 December, when the value of N is high around 0.013 s−1, the U at the coast varies from 6 to 8 m s−1, whereas U over BB is around 10–12 m s−1. The bottom panel of Fig. 15 shows values of 
Figure 16 shows the evolution of 
Evolution of (a) rainfall and (b) perturbation virtual potential temperature 
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Zonal winds (shading and dashed contours), perturbation virtual potential temperature 
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Figure 18 shows 850-hPa winds and geopotential during the model experiments. Because of the orographic blocking in the CTL run, the LPS along the east coast remains stationary throughout the run (Figs. 18a,b), whereas in the NOTOPO run, the LPS moves westward, crossing the flat peninsula (Fig. 18c), and on 0000 UTC 3 December, it is seen over the west coast of India (Fig. 18d). Most of the intense LPSs that approach the Indian east coast move over the moderate peninsular orography. However, the LPS in this particular case had comparatively weaker winds that were blocked by the orography. Hence, the westward movement of this LPS was obstructed by the orography.
Winds and geopotential contours (in m) at 850 hPa (a),(c) at 0000 UTC 2 Dec and (b),(d) at 0000 UTC 3 Dec 2015 in (a),(b) CTL and (c),(d) NOTOPO runs. The LPS is blocked by the orography in the CTL run.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
e. Evaporative cooling
It was speculated in the earlier experiment that the evaporatively cooled cold pool presents a denser front to the flow ahead of the orography and lowers the 
Figure 19 shows the differences between the soundings at the coast (Chennai) and at an inland location [shown in Fig. 20b by an asterisk (*)] in the CTL and NOEVAP experiments during the event. Simulated sounding over Chennai in the CTL run (Fig. 19a) at 1200 UTC 1 December is similar to the observed sounding in Fig. 8d. The same sounding in the NOEVAP experiment shows that the lowest layer is warmer by about 3°–4°C. The winds at the lower levels are not deflected southward. A sounding from the CTL run at the same time but from a location near the foothills shows a relatively drier lower troposphere and a cold layer within the lowest 1 km (Fig. 19c). When we see the same sounding from the NOEVAP run (Fig. 19d), again there is a warming at the lowest layer by 3°–4°C. Therefore, it is clear that the cooling in the subcloud layer in the CTL run was due to the evaporative cooling of raindrops. The saturated profile above 850 hPa in Fig. 19d is due to the presence of cloud above that location. Figure 20 shows the evolution of clouds in NOEVAP run. The clouds cross the orography on 1 December 2015. This proves that the surface cold pool, formed due to the evaporation of raindrops, was essential for the coastal cloud stagnation. Low-level flow along the orography in the NOEVAP run is analyzed next.
Simulated soundings at 1200 UTC 1 Dec 2015 in (a),(c) CTL and (b),(d) NOEVAP runs at (a),(b) the coast (13°N, 80°E) and (c),(d) the foothills (13°N, 79.2°E; location is shown in Fig. 20a).
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Cloud LWP in the NOEVAP run. (a) 0000 UTC, (b) 1200 UTC, and (c) 1800 UTC 1 Dec; and (d) 0000 UTC 2 Dec 2015. Asterisk (*) in (a) shows the location for inland sounding shown in Fig. 19.
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Similar to Fig. 13, Fig. 21 shows 950-hPa winds and 
Winds and virtual potential temperature 
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Evolution of (a) Brunt–Väisälä frequency N, (b) zonal winds U, (c) Froude number (
Citation: Monthly Weather Review 146, 4; 10.1175/MWR-D-16-0473.1
Remember that similar diurnal variation in the stratification of the subcloud layer was seen in the CTL run on 30 November (Fig. 15)—an increase in N from 1200 UTC 30 November to 0000 UTC 1 December. However, the values of N on 1 December remain high throughout the day. The NOEVAP experiment proves that this was due to the formation of the cold pool, which is absent in the NOEVAP run. Therefore, stratification and deceleration of the winds by the surface cold pool ahead of the orography is the reason that values of 
4. Summary and conclusions
On 1 December 2015, Chennai and its surrounding region received a record heavy rainfall. The precipitation system during the event was stationary over the coast. This study analyzes how the Eastern Ghats orography and cold pool localized the precipitation system. An orographic blocking mechanism was proposed by looking at the data from ERA-Interim, satellite observations, and local soundings. The nonhydrostatic WRF-ARW numerical model was used for further analysis and to prove the hypothesis. The following are the major conclusions drawn from this study:
Moist LLEJs prevailed over the adjacent ocean during the event.
Winds at the low level were blocked by the Eastern Ghats and a barrier jet formed along the coast.
A detailed analysis of the blocking mechanism with the WRF-ARW model reveals that the cold pools produced by the evaporative cooling on 1 December were essential for the flow blocking. The cold pool stratified the subcloud layer and decelerated the winds ahead of the orography. Thus, the flow entered a blocked regime.
The cold pool piled up ahead of the orography. It was stronger inland (
As a result of orographic blocking, the northwestward propagation of the LPS was seized. Later, the LPS moved in a southward direction along the peninsula. This is in consensus with the topographic β-effect theory.
Acknowledgments
The author is grateful to Prof. G. S. Bhat (CAOS, IISc) for the fruitful discussions and encouragement. Arijit Chanda and Gaurav Govardhan (both from CAOS, IISc) helped with the WRF Model simulation. Simulations were carried out on a high-performance computer (HPC) system facility at CAOS, IISc, funded by the Department of Science and Technology under Fund for Improvement of Science and Technology Infrastructure in Universities and Higher Educational Institutions (FIST) scheme and Divecha Center for Climate Change (DCCC). Station rainfall data were provided by Prof. Balaji Narasimhan (IIT, Madras). I would also like to thank NASA and ISRO for providing the satellite data and ECMWF for providing ERA-Interim data used in this study. The three unknown reviewers helped in improving the original manuscript. I would like to thank them for their comments and suggestions.
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