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Asian Journal of Atmospheric Environment - Vol. 15 , No. 3

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 15, No. 3
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Sep 2021
Received 17 Nov 2020 Revised 03 Mar 2021 Accepted 05 Jul 2021

Prediction of Heatwave 2013 over Andhra Pradesh and Telangana, India using WRF Model
N. Naveena1) ; G. Ch. Satyanarayana1), 4), * ; A. Dharma Raju2) ; N. Umakanth1) ; D. Srinivas3) ; K. Sivasankar Rao4) ; M. Suman5)
1)Center for Atmospheric Science, Koneru Lakshmaiah Education Foundation, Guntur, India
2)India Meteorological Department, (IMD), Ministry of Earth Sciences, Government of India
3)National Center for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, India
4)Andhra Pradesh State Development Planning Society, Andhra Pradesh, India
5)Department of ECE, Koneru Lakshmaiah Education Foundation, Guntur, India

Correspondence to : * Tel: +918367675195 E-mail:

Copyright © 2021 by Asian Association for Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Funding Information ▼


Heatwaves are acknowledged to be the major meteorological disaster, causing a noticeable impact on humans and animals’ lives during the last few decades. The number, frequency, duration, intensity, and areal extent of the heatwaves are on the rise during recent years. The Maximum temperature data of 2013 is analyzed to assess the synoptic nature, intensity, frequency, and various significant facets of the heatwave over the south peninsular states of Andhra Pradesh and Telangana. Indian subcontinent experienced a major heatwave during 2013, which claimed 1216 human lives. Even though the highest intensity of maximum temperatures is observed in May over major areas of India, the increasing (incidence, duration, number of spells, and the sweltering temperatures) number of heatwaves are observed over many parts of the country. The northwest and southeast coastal regions are the two heat wave prone regions. The advection of heat from the northwest with the aid of north-westerly winds causes heatwaves over northwest India to sweep or move towards India’s southeast and east coast. The heatwave record over south-eastern India, i.e, Andhra Pradesh and the adjoining Telangana state during May 22-24, 2013 were described in this study. Maximum temperatures above 40°C are observed with a sudden rise by 6 to 7°C over the study region. An attempt is made to predict the maximum temperatures 72 hours before the existence of a heatwave at 3 km horizontal resolution using the Advanced core of the Weather Research and Forecasting (WRF) model. Model predicted temperature values match with observations and the statistical metrics show a high index of the agreement, lower values for root-mean-square error and mean absolute error. Atmospheric circulation patterns associated with this heatwave are also presented. The arrest of sea breeze, the hovering of diabatic heat because of subsidence is the factor that abetted the heatwave blockade over the south-eastern part of the country. The WRF model forecasts could present the occurance of the heat wave over AP and Telangana region with 72 hour lead time with high accuracy.

Keywords: Maximum temperature, Weather research and forecasting model, WRF, Heatwave, Statistical metrics


With the burgeoning demand for shelter to the growing population, rapid industrialization, and growing urbanization, there has been a drastic reduction in land use and land cover quality. These are major anthropogenic causes of climate change (Fischer et al., 2015). The resultant maximum temperatures aided by the relative humidity during the summer season produce heatwaves that cause sunstrokes, heat stress, other physiological effects, culminating in fatalities. Because of the abnormal rise in temperatures, the human body experiences uneven physiological stress as the optimum temperature of the human body is 37°C, and there is an apparent correlation between heat stress and mortality rate. The escalated temperatures may lead to the loss of human lives and cause financial deprivation that are the major concerns of the disaster managers of every nation, including India. Heatwave episodes are treated as natural disasters in this modern era because of their connection to human health (De Bono et al., 2004) and the environment (Ciais et al., 2005).

Intergovernmental Panel on Climate Change (IPCC, 2007), in its fourth assessment report (AR4) reported that the mean surface air temperature has been increased by 0.7°C during 1961-2000. The changes in the temperature play an essential role in global warming, which is not similar worldwide. India, for instance, experienced multiple severe heat waves associated with increased mortality during the years 1971, 1987, 1997, 2001, 2002, 2013, and 2015 and of late, recorded maximum death rate in recent years (2001-2016) due to heat waves (Mazdiyasni et al., 2017). Pramanik & Jagannathan studied trends in maximum and minimum temperature (1954) by using 30 stations in India for 1880-1950. The number, frequency, duration, intensity, and extent of the heatwaves increased worldwide over three times since the 1960s (Min et al., 2011). Prediction of these heat waves with ample lead time is essential to overcome the impact of heatwaves. The formation of heat waves leans upon some specific maximum temperature threshold values over a particular region, and the value varies from one area to another. For example, India’s maximum temperature threshold is 45°C while it is 40°C for Australia; 32°C for the northwestern United States, and 28°C for Denmark. These values depend on atmosphere variables such as humidity and topography (Das and Smith, 2012). Numerous studies of heatwave episodes were carried out all over the world for example, in North America (Lau and Nath, 2012; Gershunov et al., 2009), in Europe (e.g., Dasari et al., 2014; Black et al., 2004; Fink et al., 2004; Schar et al., 2004). In India also many studies were carried out to understand the characteristics of heatwaves (Pai et al., 2017, 2013, 2004; Ratnam et al., 2016; Ray et al., 2013; Mohan and Bhati, 2011) and the synoptic features connected to heatwaves were identified (Desai, 1999; Subbaramayya and SuryanarayanaRao, 1976; Raghavan, 1966). The heatwave definition is different for different regions, and it is generally related to continuous abnormal temperatures (Meehl and Tebaldi, 2004). Several cases of heatwaves were observed all around the world. During the 2003 heatwave in Europe, the loss of human lives is about 70,000 (Robine et al., 2008), and nearly 11,000 in Russia. In Japan, nearly 1718 (Matsuyama and Sato, 2011) lost their lives during 2010, and in Chicago, about 500 died during 1995 (Dematte et al., 1998). In India, the highest intensity of heatwaves occurred during 2015, which led to nearly 2500 deaths, out of which 2320 deaths were registered only in Andhra Pradesh (AP) and Telangana states (Charan Singh and Kumar, 2018; Dodla et al., 2017). The highest maximum temperatures were recorded over southeast coastal states of India, especially over AP and Telangana, and also mortality rate is high over these regions (Naveena et al., 2021a, 2021c, 2020a; Satyanarayana and Dodla, 2020) and atmospheric wind pattern plays a major role for the highest recorded death rate over these regions (Satyanarayana et al., 2021, 2018; Venkata et al., 2017). The frequency and intensity of heatwaves increases significantly for future scenario of heatwaves over the Indian subcontinent up to 21st century (Naveena et al., 2021b, 2020b). Convection and instability in the atmosphere are also high over these regions during the pre-monsoon season (Umakanth et al., 2021, 2020a, 2020b, 2020c, 2020d). According to the World Meteorological Organization, the death rate due to heatwaves will double in the next 20 years (Larsen, 2006).

Heatwave characteristics are different for different regions. It may last from a few days to a few months also. For example, heatwaves that occurred over Europe during 2003 continued for three months, i.e., from June to August (Fink et al., 2004; Schar and Jendritzky, 2004), and the heatwave that occurred over the U.S. during 1995 did last for only a few days in July (Palecki et al., 2001; Kunkel et al., 1996). Generally, heatwaves last for few days only (Khaliq et al., 2005).

Temperatures during summers are increasing, and the intensity and frequency of heatwaves are also rising due to climate change. According to Intergovernmental Panel on Climate Change (third Assessment Report), the increase in temperatures started since last 50 years due to anthropogenic activities and the average temperature of the globe, i.e., both sea surface and land temperature increased 0.6±0.2°C during the 20th century. A detailed analysis of Northern Hemispheric data showed that temperatures have increased at a higher rate during 20th century when compared to the last 10 centuries (Houghton et al., 2001).

It will be useful to analyze the trends of long-term daily maximum temperature to understand the phenomena of heatwaves over a particular geographical location (Li et al., 2015). Several vulnerable zones are at higher risk to heatwaves where huge populations reside. Efforts are, therefore, required to develop the prediction skill of Numerical Weather Prediction (NWP) models to aid in disaster management (De et al., 2005). Many researchers have extensively used WRF, a mesoscale model, in simulating extreme temperature regions, particularly over Europe and North America (Giannaros et al., 2013; Giovannini et al., 2013; Salamanca et al., 2012; Bohnenstengel et al., 2011). Using the downscaled value of maximum temperature and comparing it with North American regional reanalysis (NARR) data, a generalized extreme value is presented and used in the study (Wang et al., 2016). For the progression of a heatwave through regional and global effects, land-atmosphere coupling plays a significant role. While numerical models are extensively used to model extreme events such as heatwaves, land surface schemes (LSSs) are used for weather simulations in some studies. These schemes are also used in the WRF model. This kind of exploration is imperative as the present WRF sensitivities to various physical parameterizations are mainly concentrated on convective precipitation (Fan, 2009). However, the impact on heatwaves in terms of frequency, intensity, and spatial distribution of temperature, is different for different LSSs. One extreme weather heatwave event over East China, LSS induced negative feedback between low-level atmospheric circulation and surface temperature, is sensitive to LSS (Zeng et al., 2011). WRF model simulations over India were carried out for Andhra Pradesh (Dodla et al., 2017), Odisha (Gouda et al., 2017), Delhi (Mohan and Bhati, 2011).

In the present paper, an attempt is made to predict heatwaves 72 hours before their occurrence over Andhra Pradesh and Telangana, with the aid of the WRF model at 3 km horizontal resolution. Forecast of heatwaves with a prior lead time gives essential information and direction to the people to protect themselves from the deadly effects of heatwaves. It helps disaster managers to arrange the contingency measures.

2. 1 Data

a. Daily maximum temperature data from 9 Automatic Weather Stations are taken from India Meteorological Department (IMD) for May 2013 in both the south-eastern coastal states of India, i.e., Andhra Pradesh and Telangana, to identify heatwaves and also to assess the duration of a heatwave over this region.

b. Gridded daily maximum temperature data are obtained from IMD from 1951 to 2019 at one-degree spatial resolution to generate climate means (Srivastava et al., 2008). The data are used for to identify the area of coverage and span of heatwave conditions over Andhra Pradesh and Telangana regions for May 2013. Anomalies or deviations from climate mean normal values are calculated at each grid point.

c. Reanalysis global dataset of European Centre for Medium-Range Weather Forecasts (ECMWF) at 0.125° resolution for May 2013 are used for the most probable time of occurrence of the maximum temperature of the day at 0900 UTC for the region between 12.5-20oN; 76.5-85oE at different pressure levels from 1000 to 700 hPa. Relative humidity, vertical velocity, and wind flow are analyzed to summarize the dynamics linked with the 2013 heatwave (

d. National Centre for Medium Range Weather Forecasting (NCMRWF) generated Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis data ( available at 12 km horizontal resolution is also used to generate the dynamics of the heatwave of 2013.

e. The initial, and time varying boundary conditions for the model are provided from the National Centers for Environmental Prediction Global Forecast System (NCEP GFS) forecasts available at 0.25° spatial resolution and at 3-hour interval (http://www.ftp.ncep.noaa.Gov/data/nccf/com/gfs/prod/).

2. 2 Methodology

The basis for characterizing “heatwave” in the present analysis is similar to the definition given by IMD. IMD proclaims a region to be under heatwave when the anomaly of maximum temperature is greater than 4-5°C (5-6°C) where the normal maximum temperature is ≥40°C (<40°C) and proclaims it to be a region of a severe heatwave when anomalies of maximum temperature are ≥6°C (≥7°C). If the normal maximum temperature is ≥40°C (<40°C) and if the temperature is greater than 45°C heatwave is declared without considering temperature anomaly (IMD, 2015).

Statistical analysis is carried out for correlation coefficient (CC), BIAS, RMSE (root-mean-square error). IOA (Index of agreement) calculated to estimate the model’s performance in predicting the temperatures for the present study period. The equations enlisted herein are adapted from Wilks (2006).

Correlation Coffecient CC=i=1nMi-M¯oi-o¯i=1nMi-M¯2i=1nOi-O¯2(3) 
Mean Absolute ErrorMAE=1ni=1nMi-Oi(4) 
Root Mean Square ErrorRMSE=i=1nMi-Oi2n(5) 
Index of AgreementIOA=1.0-i=1nMi-Oi2i=1nMi-O¯+Oi-O¯2(6) 

Where Oi and Mi correspond to observations and model values; Xi is the dataset values; n is the number of samples; Σ denotes the summation formula.

2. 3 Model Description and Experimentation

In the present study, Advanced Research Weather Research and Forecasting model version 3.6.1 was used. The model was sourced and developed from National Center for Atmospheric Research (NCAR). It is appropriate for predicting real-time weather applications and also for a wide range of atmospheric simulations. In this model, there is flexibility to opt domain of interest, horizontal resolution, and nested domains, and a number of options are available to select different parameterization schemes for the planetary boundary layer, radiation, explicit moisture, and soil processes. A concise explanation of the model was given by Skamarock et al. (2008).

In the present study, two interactive nested domains, the outer domain with 9 km resolution and inner domain with 3 km resolution are chosen. The inner domain covers AP and Telangana states, and the neighbouring Bay of Bengal region to some extent. The model is utilized to predict the weather conditions with a prior lead time of 72 hours for each day for May 22-24, 2013. The model domain region represented in Fig. 1, and a details are given in Table 1. Outputs from the model are stored at 3 hr interval. 0900 UTC, equivalent to 1430 IST, is the time of maximum temperature to examine the heatwave. The boundary conditions and the initial conditions for the model were provided from NCEP GFS forecasting files at a time interval of 3 hour and at a horizontal resolution of 0.25°. The model forecasted surface temperatures (2 m) were used to estimate the model accuracy with a prior time of 24, 48, and 72 hours. During May 22-24, 2013, the model forecasted surface temperature, and wind (10 m) is utilized to observe warm air accumulation.

Fig. 1. 
(a) Model Domains, (b) Spatial map of Andhra Pradesh and Telangana (numbers indicate districts).

Table 1. 
Model Configuration.
Model WRF (ARW Core)
Version 3.6.1
Dynamics Primitive equation, non-hydrostatic
Vertical resolution 42 levels
Domains Domain1
8-23°N; 73.5-89°E
12.5-20°N; 76.5-85°E
Horizontal resolution 9 km 3 km
Radiation Dudhia scheme for short wave
RRTM scheme for long wave
Initial and boundary conditions NCEP GFS Global Forecast
Cumulus convection Grell-Freitas old Simplied
Planetary boundary layer Mellor-Yamada-Janjic TKE scheme


The distinct characteristics of May 2013 heatwave event are represented in this section. Mean maximum temperature, the number of heatwave days above 42°C, and mean +4°C days are calculated for May 2013 and forecast of this heatwave event using WRF model with a good lead time of 24, 48 and 72 hours, dynamical characteristics accompanied with this heatwave are also underscored.

3. 1 Maximum Temperature over Andhra Pradesh and Telangana during May 2013

The spatial distribution of mean maximum temperature from IMD gridded data for May 2013 is shown in Fig. 2a. The highest temperatures of 42°C to 44°C were recorded for Adilabad, Karimnagar, Warangal, and Nizamabad. North coastal AP exhibited colder temperatures of the order of 36°C to 38°C over Srikakulam, Vizianagaram, Visakhapatnam, and East Godavari (Fig. 2a).

Fig. 2. 
(a) Mean Maximum Temperature during May, 2013, (b) Maximum Temperature days above 42°C, (c) Heat Wave days Mean+4°C (37°C) during May, 2013.

The number of days with maximum temperatures more than 42°C are also high for northern Telangana to the extent of 20 to 30 days during May 2013 (Fig. 2b). In Andhra Pradesh, only west Godavari, Krishna, and Guntur recorded the frequency of about 5 to 10 days during May 2013, and all other districts recorded less than 5-day frequency (Fig. 2b). Mean+4°C days show 2 to 4 days for West Godavari and Guntur districts of Andhra Pradesh (Fig. 2c).

3. 2 Heatwave Event

The time series (1-31 May, 2013) of maximum temperature is depicted in Fig. 3, and climatological mean of daily maximum temperature from IMD gridded data for 9 AWS stations is given in Table 2 and depicted in Fig. 3. A time series plot is utilized to spot the occurrence of heatwave conditions.

Fig. 3. 
Time series of daily maximum temperature (°C) at different locations in Andhra Pradesh and Telangana during May 1-31, 2013 (solid line) along with corresponding climatological mean (dotted line).

Table 2. 
Automatic Weather Stations geographical locations in Andhra Pradesh and Telangana.
S. No. Station name Latitude Longitude
1. Gannavaram 16.54°N 80.80°E
2. Visakhapatnam 17.68°N 83.21°E
3. Machilipatnam 16.18°N 81.13°E
4. Kavali 14.91°N 79.99°E
5. Kakinada 16.98°N 82.24°E
6. Bapatla 15.90°N 80.46°E
7. Nellore 14.44°N 79.98°E
8. Ananthpur 14.68°N 77.60°E

In order to comprehend the present heatwave event, spatial distribution plots of maximum temperature and their anomalies from climate mean normal values are plotted for the period 22-24 May, 2013. From the maximum temperature spatial distribution plots, first locales of temperature surpassing 40°C are identified. To find out the regions of heatwaves based on the definition of heatwaves, as mentioned in section 2, maximum temperature anomalies are investigated. Heatwave event over AP and Telangana sustained during May 21-24, 2013. Fig. 3 gives a clear delineation and segregation of the regions where maximum temperatures surpass 40°C. The spatial distribution plots of maximum temperature indicate that a heatwave with temperature around 43°C to 45°C is observed over the Telangana region and from 40°C to 44°C over the AP region on 21st May. On 22nd May, the heatwave extends over the northern Telangana region, and from 24th May onwards, it starts dissipating (Fig. 4a-d (upper panel)).

Fig. 4. 
Maximum temperature (°C, upper panel), and maximum temperature anomalies (°C, lower panel) valid on (a) 21st May, (b) 22nd May, (c) 23rd May and (d) 24th May, 2013.

From maximum temperature anomalies (deviation from climate mean normal maximum temperature as given in section 2. 1), it is clear that a heatwave occurred over the northern Telangana region with an anomaly of 4-5°C on 21st May. On 22nd May, it extended and slightly moved southwards and spread up to southern and central AP. On 23rd May, it moved towards northern AP and 24th May it moved further northwards, covering northern AP completely (Fig. 4a-d (lower panel)).

3. 3 Model Prediction

As explained in the model in section 2.3, WRF model has been utilized to make 72 hours forecasts for each of the heatwave periods during 22-24 May, 2013. For the domain covering Telangana and AP region, model forecasts are made at a resolution of 3 km. At 0900 UTC (equivalent to 1430 Indian standard time), i.e., the time when maximum temperature occurs, the temperature distribution of the model predicted values with a lead time of 24, 48, and 72 hours using NCEP GFS analysis and forecasts which is available at 25 km resolution, are presented in Figs. 5-7.

Fig. 5. 
Maximum temperature (°C) along with surface winds overlayed for (a) Observation, (b) Day-1, (c) Day-2 and (d) Day-3 forecast valid on 22nd May, 2013.

Fig. 6. 
Maximum temperature (°C) along with surface winds overlayed for (a) Observation, (b) Day-1, (c) Day-2 and (d) Day-3 forecast valid on 23rd May, 2013.

Fig. 7. 
Maximum temperature (°C) along with surface winds overlayed for (a) Observation, (b) Day-1, (c) Day-2 and (d) Day-3 forecast valid on 24th May, 2013.

These plots exhibit the existence of heatwave conditions over Telangana and AP with maximum temperature surpassing 44°C. Prediction of maximum temperature with a very good lead time of 72 hours is made using NCEP-GFS forecasts. Firstly, the model predictions with a lead time of 24 h, 48 h, and 72 h are computed by comparing the values of the grid domain with NCEP final analysis and then with the observational data of the IMD.

Statistical metrics such as RMSE, MAE, BIAS, CC, and IOA have been calculated and given in Table 3. NCEP analysis is compared at each grid point over 3 km resolution to calculate statistical metrics over the study domain (12.5-20.5°N; 76.5-85°E). Many data points are considered, and these statistical metrics show a remarkable similarity between the analysis and model prediction for all the lead times. From Table 3, it is quite evident that the values of MAE, RMSE, and BIAS are very less and CC and IOA are high.

Table 3. 
Statistical metrics of the evaluation of model predictions temperatures over Andhra Pradesh and Telangana during 22-24 May, 2013.
a) Andhra Pradesh and Telangana domain region
Day 1 0.49°C 0.80°C 0.95 -0.11°C 0.97
Day 2 0.47°C 1.27°C 0.90 -0.20°C 0.93
Day 3 0.59°C 1.44°C 0.85 -0.18°C 0.90
b) Mean statistics from station observations
Day 1 0.52°C 1.46°C 0.95 0.09°C 0.97
Day 2 0.44°C 1.94°C 0.90 0.16°C 0.94
Day 3 0.52°C 2.24°C 0.85 -0.02°C 0.92

During day-1, the predicted temperature with a lead time of 24 hours statistical metrics denote that MAE is 0.49°C, RMSE is 0.80°C, CC is 0.95, BIAS is -0.11°C, and IOA is 0.97 (Table 3a). For day-2, with a lead time of 48 hours, MAE is 0.47°C, RMSE is 1.27°C, CC is 0.90, Bias is -0.20°C, and IOA is 0.93. For day-3, with a lead time of 72 hours, MAE is 0.59°C, RMSE is 1.44°C, CC is 0.85, BIAS is -0.18°C, and IOA is 0.90 (Table 3a). Again, statistical metrics are calculated between the model predicted temperature data point and the nearest station location point (9 locations) during the heatwave event from 22nd to 24th May, 2013, as given in Table 3b.

The time series of observed temperatures at the six AWS stations (Ganavaram, Nellore, Ongole, Bapatla, Kakinada, and Hyderabad) and the corresponding WRF model day-1 (24 hr), day-2 (48 hr), and day-3 (72 hr) temperature forecasts at 3-hour interval for the period from 00 UTC of 22 May to 21 UTC of 24 May, 2013 are depicted in Fig. 8. It is clear from Fig. 8 that WRF model forecasted the temperature with 24 hours (day-1), 48 hours (day-2), and 72 hours (day-3) lead times at all these six AWS stations. The model forecasts show the pattern very well agreeing with the observations, but day-3 slighlty underestimes the temperature (Fig. 8). This displays an excellent correlation of about 0.95 for day-1, with very less bias, although RMSE is slightly higher for day-3, i.e. 2.24°C. It gives clear evidence that temperatures can be predicted with a lead time of up to 72 hours, and the WRF model forecasted the heatwave events with greater accuracy. The main reason for the rise of Maximum Temperature over coastal AP is the absence or deflection of sea breeze during day time over south eastern coastal regions, resulting in the accumulation of heat over the regions of AP and Telangana (Dodla et al., 2017).

Fig. 8. 
The time series of temperature (°C) at 3-hour interval from the AWS stations, and corresponding WRF model Day-1, Day-2 and Day-3 forecasts during the period from 00 UTC of 22 May to 21 UTC of 24 May, 2013.

3. 4 Dynamics Associated with a Heatwave

To know the dynamics associated with heatwaves, horizontal wind fields, and relative humidity are plotted for an area-averaged domain between 17 to 17.5°N latitude and 80 to 80.5°E longitude where heatwave event is maximum, as is clear from both the ECMWF and the NCMWF data sets (Fig. 9). The wind fields show that north-westerly flow and westerlies strengthen from surface to 700 hPa pressure level during the heatwave event of 22nd-24th May, 2013. And, low humidity is observed for the heatwave event up to 750 hPa, and advection of heat is seen at the lower levels showing heat accumulation. The timeheight section of the vertical velocity (*10 Pa/sec) and wind speed (m/sec) are also plotted to know the dynamics associated with this heatwave for the domain mentioned above (from surface to 700 hPa). During the heatwave event, the vertical velocity is -0.5 from 850 hPa to 700 hPa, showing the rising motion leading to heatwaves (Fig. 10).

Fig. 9. 
Time-height sections of a relative humidity (%, shaded) and horizontal wind (m/s, vectors) representing for a region (17.0-17.5°N, 80.0-80.5°E) from May 1-31, 2013 by using ECMWF daily data (left panel), and NCMWRF daily data (right panel).

Fig. 10. 
Time-height sections of a vertical velocity (*10 Pa/sec, shaded) and wind speed (m/s, contours) representing the region of 17.0 to 17.5°N, 80.0 to 80.5°E from May 1 to 31, 2013 by using ECMWF daily Data.


A detailed analysis of the heatwave over AP and Telangana during 21-24 May, 2013 is carried out in this present study. This heatwave could be predicted with a lead time of 24, 48, and 72 hours using WRF with a resolution of 3 km. The features of the present heatwave are studied. Maximum temperatures of 43°C to 45°C are observed over Telangana and 40°C to 44°C over AP region during the starting day of the heatwave.

These results indicate that the model is accurate in predicting the heatwave. However, in calculating the statistical metrics, the model showed minor errors in predicting the surface temperature (2 m) as root-mean-square error and mean absolute error are smaller values, and with higher values for the index of agreement and correlation coefficient values. Heatwaves are one of the major calamities which claim human lives, and predicting these heatwaves with a good lead time will be very useful for managing their impact on humans. The model results were used further to realize the thermodynamical reasons for the heatwave. Analysis of temperature advection has brought out an interesting and important dynamic reason for the coastal region vulnerability for warm air advection. The coastal regions are cooler than inland stations because of the vicinity to the sea and sea breeze effect during daytime. Still, the same sea breeze effect has contributed to the observed heatwave over the coastal AP region during the latter fortnight of May, 2013.


The authors acknowledge free access to GFS data from National Centers for Environmental Prediction (NCEP); National Center for Atmospheric Research NCAR; ERA data from the European Centre for Medium-Range Weather Forecasts (ECMWF), U.K.; AWS data from MOSDAC, Government of India, NCMRWF for providing IMDAA data. This research is supported It supports this research by Early Career Research Award, Science and Engineering Research Board, Government of India through financial support under grant No. ECR/2016/001295.

1. Black, E., Blackburn, M., Harrison, G., Hoskins, B., Methven, J. (2004) Factors contributing to the summer 2003 heat wave. Weather, 59, 217-223.
2. Bohnenstengel, S.-I., Evans, S., Clark, P.-A., Belcher, S.-E. (2011) Simulations of the London urban heat island. Quarterly Journal of the Royal Meteorological Society, 137, 1625-1640.
3. Charan, S., Kumar, S.-V.-J. (2018) Meteorological conditions for development of heat wave over Coastal Andhra Pradesh and Telangana. Journal of Indian Geophysical Union, 22(3), 349-358.
4. Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V., Aubinet, M., Buchmann, N., Bernhofer, Chr., Carrara, A., Chevallier, F., De Noblet, N., Friend, A.D., Friedlingstein, P., Grünwald, T., Heinesch, B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci, G., Miglietta, F., Ourcival, J.M., Papale, D., Pilegaard, K., Rambal, S., Seufert, G., Soussana, J.F., Sanz, M.J., Schulze, E.D., Vesala, T., Valentini, R. (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529-533.
5. Das, S., Smith, S.-C. (2012) Awareness as an adaptation strategy for reducing mortality from heat waves: Evidence from a Disaster risk management program in India. Climate Change Economics, 03, 1250010.
6. Dasari, H.-P., Salgado, R., Perdigao, J., Challa, V.-S. (2014) A regional climate simulation study using WRF-ARW model over Europe and evaluation for extreme temperature weather events. International Journal of Atmospheric Sciences, 22, 704079.
7. De, U.-S., Dube, R.-K., Prakasa Rao, G.-S. (2005) Extreme weather events over India in the last 100 years. Journal of Indian Geophysical Union, 9(3), 173-187.
8. De Bono, A., Giuliani, G., Kluser, S., Peduzzi, P. (2004) Impacts on summer 2003 H.W. in Europe. UNEP/DEWA/GRID Europe Environment Alert Bulletin, 2, 1-4.
9. Dematte, J.E., O’Mara, K., Buescher, J., Whitney, C.G., Forsythe, S., McNamee, T., Adiga, R.B., Ndukwu, I.M. (1998) Nearfatal heat stroke during the 1995 H.W. in Chicago. Annals of Internal Medicine, 129, 173-181.
10. Desai, D.-S. (1999) H.W. conditions during March to June for the year 1972, 1979 and 1987 and their comparison with year 1990-1995. Mausam, 50(2), 211-218.
11. Dodla, V.-B., Satyanarayana, G.-C., Desamsetti, S. (2017) Analysis and prediction of a catastrophic Indian coastal H.W. of 2015. Natural Hazards, 87, 395-414.
12. Dodla, V.-B., Satyanarayana, G.-C., Desamsetti, S. (2017) Atmospheric dispersion of PM2.5 precursor gases from two major thermal plants in Andhra Pradesh, India. Aerosol and Air Quality Research, 17, 381-393.
13. Fan, X. (2009) Impacts of soil heating condition on precipitation simulations in the Weather Research and Forecasting Model. Monthly Weather Review, 137, 2263-2285. https://
14. Fink, A.-H., Brucher, T., Kruger, A., Leckebusch, G.-C., Pinto, J.-G., Ulbrich, U. (2004) The 2003 European summer heatwaves and drought-synoptic diagnosis and impacts. Weather, 59, 209-216.
15. Fischer, E.-M., Knutti, R. (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560-564.
16. Gershunov, A., Cayan, D.-R., Iacobellis, S.-F. (2009) The great 2006 H.W. over California and Nevada: Signal of an Increasing Trend. Journal of Climate, 22, 6181-6203.
17. Giannaros, T.-M., Melas, D., Daglis, I.-A., Keramitsoglou, I., Kourtidis, K. (2013) Numerical study of the urban heat island over Athens (Greece) with the WRF model. Atmospheric Environment, 73, 103-111.
18. Giovannini, L., Zardi, D., de Franceschi, M., Chen, F. (2013) Numerical simulations of boundary-layer processes and urban-induced alterations in an Alpine valley. International Journal of Climatology, 34, 1111-1131.
19. Gouda, K.-C., Sahoo, S., Samantray, P., Himesh, S. (2017) Simulation of extreme temperature over Odisha during May 2015. Weather and Climate Extremes, 17, 17728.
20. Houghton, J.-T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Maskell, K., Johnson, C.A. (2001) Climate change 2001: the scientific basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, Cambridge University Press.
21. IMD (India Meteorological Department) (2015) Recommendation regarding the revised criteria for declaring heat wave/ cold wave, DDGM (W.F.). UOI. No. W-969/1304 to 1365 dated February 2002 and DDGM (W.F.) Forecasting Circular No. 5/2015 (3.7), India Meteorological Department.
22. IPCC (Intergovernmental Panel on Climate Change) (2007) Impacts, adaptation and vulnerability. In Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change (Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE eds), Cambridge University Press, Cambridge, p. 976.
23. Khaliq, M.-N., St-Hilaire, A., Ouarda, T.-B.-M.-J., Bobee, B. (2005) Frequency analysis and temporal pattern of occurrences of southern Quebec heatwaves. International Journal of Climatology, 25, 485-504.
24. Kunkel, K.-E., Changnon, S.-S., Reike, B., Arritt, R.-W. (1996) The July 1995 H.W. in the midwest: a climatic perspective and critical weather factors. Bulletin of the American Meteorological Society, 77, 1507-1518.
25. Larsen, J. (2006) Setting the record straight: more than 52,000 Europeans died from heat in summer 2003. Earth Policy Institute.
26. Lau, N.-C., Nath, M.-J. (2012) A model study of H.W.s over North America Meteorological aspects and projections for the twenty-first century. Journal of Climate, 25, 4761-4784.
27. Li, D., Sun, T., Liu, M., Yang, L., Wang, L., Gao, Z. (2015) Contrasting responses of urban and rural surface energy budgets to heat waves explain synergies between urban heat islands and heat waves. Environment Research Letter, 10, 054009.
28. Matsuyama, K., Sato, S. (2011) Heatstroke deaths quadruple as Japan shuns air conditioners to save power, Bloomberg L.P. Retrieved 1st June 2012, Last year, when a record of 1,718 people died of heatstroke as the summer heat broke records.
29. Mazdiyasni, O., AghaKouchak, A., Davis, S.J., Madadgar, S., Mehran, A., Ragno, E., Sadegh, M., Sengupta, A., Ghosh, S., Dhanya, C.T., Niknejad, M. (2017) Increasing probability of mortality during Indian heat waves. Science Advances, 3, e1700066.
30. Meehl, G.-A., Tebaldi, C. (2004) More intense, more frequent and longer lasting H.W.s in the 21st century. Science, 305, 994-997.
31. Min, S.-K., Zhang, X., Zwiers, F.-W., Hegerl, G.-C. (2011) Human contribution to more- intense precipitation extremes. Nature, 470, 378-381.
32. Mohan, M., Shweta, B. (2011) Analysis of WRF Model Performance over Subtropical Region of Delhi, India. Advances in Meteorology, 2011, 621235.
33. Naveena, N., Satyanarayana, G.-C., Rao, D.-V.-B., Srinivas, D. (2020a) An accentuated “hot blob” over Vidarbha, India, during the pre-monsoon season. Natural Hazards, 105, 1359-1373.
34. Naveena, N., Satyanarayana, G.-C., Umakanth, N., Avinash, B., Jaswanth, J., Sainath Reddy, M.-S. (2020b) Statistical downscaling of CMIP models maximum temperature data using BCSD method, AIP Conference Proceedings (Accepted).
35. Naveena, N., Satyanarayana, G.-C., Dharma Raju, A., Rao, K.-S., Umakanth, N. (2021a) Spatial and statistical characteristics of heat waves impacting India. AIMS Environmental Science, 8, 117-134.
36. Naveena, N., Satyanarayana, G.-C., Rao, K.-S., Umakanth, N., Dharma Raju, A. (2021b) Centuries of Heat waves over India during 20th and 219 Century, Applied Environmental Research (Accepted).
37. Naveena, N., Satyanarayana, G.-C., Rao, K.-S., Umakanth, N., Srinivas, D. (2021c) Heat wave Characteristics over India during ENSO events. Journal of Earth System Science (Accepted).
38. Pai, D.-S., Thapliyal, V., Kokate, P.-D. (2004) Decadal variation in the heat and cold waves over India during 1971-2000. Mausam, 55, 281-292.
39. Pai, D.-S., Smitha, A., Ramanathan, A.-N. (2013) Long term climatology and trends of H.W.s over India during the recent 50 years (1961-2010). Mausam, 64(4), 585-604.
40. Pai, D.-S., Srivasatava, A.-K., Smitha, A. (2017) Heat and Cold Waves over India, Chapter 4 in Observed Climate Variability and Change over the Indian Region. In Rajeevan, M., Nayak, S. (Eds.), Springer Geology.
41. Palecki, M.-A., Changnon, S.-A., Kunkel, K.-E. (2001) The nature and impacts of the July 1999 H.W. in the midwestern United States: learning from the lessons of 1995. Bulletin of the American Meteorological Society, 82, 1353-1367.
42. Pramanik, S.-K., Jagannathan, P. (1954) Climatic change in India: II. Temperature. Indian Journal of Meteorology And Geophysics, 5, 1-19.
43. Raghavan, K. (1966) A climatological study of severe H.W.s in India. Indian Journal of Meteorology and Geophysics, 17, 581-588.
44. Ratnam, J.-V., Behera, S.-K., Ratna, S.-B., Rajeevan, M., Yamagata, T. (2016), Anatomy of Indian heatwaves. Scientific Reports, 6, 24395.
45. Ray, K., Chincholikar, J.-R., Mohanty, M. (2013) Analysis of extreme high temperature conditions over Gujarat. Mausam, 64(3), 467-474.
46. Robine, J.-M., Cheung, S.-L.-K., Le Roy, S., Van Oyen, H., Griffiths, C., Michel, J.-P., Herrmann, F.-R. (2008) Death toll exceeded 70,000 in Europe during the summer of 2003. Comptesrendus Biologies, 331, 171-178.
47. Salamanca, F., Martilli, A., Yagüe, C. (2012) A numerical study of the Urban Heat Island over Madrid during the DESIREX (2008) campaign with WRF and an evaluation of simple mitigation strategies. International Journal of Climatology, 32, 2372-2386.
48. Satyanarayana, G.-Ch., Bhaskar Rao, D.-V. (2020) Phenology of heat waves over India. Atmospheric Research, 245, 105078.
49. Satyanarayana, G.-Ch., Bhaskar Rao, D.-V., Desamsetti, S. (2021) Assessment of wind energy potential over India using high-resolution global reanalysis data. Journal of Earth System Science, 130, 64.
50. Satyanarayana, G.-Ch., Lucy Supriya, R.-H., Bhaskar Rao, D.-V. (2019) Wind Energy Assessment over Andhra Pradesh and Telangana Regions. Meteorological Applications, 26(1), 14-29.
51. Schar, C., Jendritzky, G. (2004) Hot news from summer 2003. Nature, 432, 559-560.
52. Skamarock, W.-C., Klemp, J.-B., Dudhia, J., Gill, D.-O., Barker, D.-M., Duda, M.-G., Huang, X.-Y., Wang, W., Powers, J-G. (2008) A description of the advanced research WRF version 3, NCAR Technical Note 475.
53. Srivastava, A.-K., Rajeevan, M., Kshirsagar, S.-R. (2009) Development of a high resolution daily gridded temperature data set (1969-2005) for the Indian Region. Atmospheric Science Letters, 10(4), 249-254.
54. Subbaramayya, I., Surya Rao, D.-A. (1976) Heat wave and cold wave days in different states in India. Indian Journal of Meteorology, Hydrology & Geophysics, 27(4), 436-440.
55. Umakanth, N., Satyanarayana, G.-Ch., Naveena, N. Srinivas, D., Bhaskar Rao, D.V. (2021) Statistical and dynamical based thunderstorm prediction over southeast India. Journal of Earth System Science, 130, 71.
56. Umakanth, N., Satyanarayana, G.-Ch., Simon, B., Kumar, P., Rao, M.C. (2020a) Impact of convection and stability parameters on lightning activity over Andhra Pradesh, India. Acta Geophysica, 68, 1845-1866.
57. Umakanth, N., Satyanarayana, G.-Ch., Simon, B., Rao, M.C., Ranga Babu, N. (2020b) Long-term analysis of thunderstorm-related parameters over Visakhapatnam and Machilipatnam, India. Acta Geophysica, 68, 921-932.
58. Umakanth, N., Satyanarayana, G.-Ch., Simon, B., Rao, M.C. (2020c) Satellite Based Interpretation of Stability Parameters on Convective Systems over India and Srilanka. Asian Journal of Atmospheric Environment, 14(2), 119-132.
59. Umakanth, N., Satyanarayana, G.-Ch., Simon, B., Rao, M.C., Tanooj Kumar, M., Ranga Babu, N. (2020d) Analysis of various thermodynamic instability parameters and their association with the rainfall during thunderstorm events over Anakapalle (Visakhapatnam district), India. Acta Geophys, 68, 1549-1564.
60. Wang, J., Han, Y., Stein, M.-L., Kotamarthi, V.-R., Huang, W.-K. (2016) Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model. Climate Dynamics, 47, 2833-2849.
61. Wilks, D.-S. (2006) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press, London.
62. Zeng, X.-M., Wu, Z., Xiong, S., Song, S., Zheng, Y., Liu, H. (2011) Sensitivity of simulated short-range high-temperature weather to land surface schemes by WRF. Science China Earth Sciences, 54, 581-590.