IMD Meteorological Monograph:
Northeast Monsoon of South Asia
GOVERNMENT OF INDIAMINISTRY OF EARTH SCIENCESINDIA METEOROLOGICAL DEPARTMENT

No: MoES/IMD/Synoptic Met/02(2022)/27

Northeast Monsoon of South Asia

M. Rajeevan, M. Mohapatra, C. K. Unnikrishnan,
B. Geetha, S. Balachandran, O. P. Sreejith,
P. Mukhopadhyay, D. R. Pattanaik, P. Guhathakurta,
P. Kumar, K. N. Kumar, J. Bhate, P. Rohini,
M. Sharma, R. Ashrit, A. Mitra and K. Sagar

2022

India Meteorological Department
Ministry of Earth Sciences
Government of India

Northeast Monsoon of South Asia
M. Rajeevan1, M. Mohapatra2, C. K. Unnikrishnan3, B. Geetha4,
S. Balachandran4, O. P. Sreejith5, P. Mukhopadhyay6, D. R. Pattanaik2,
Pulak Guhathakurta5, Pankaj Kumar9 , K. Niranjan Kumar7, Jyoti Bhate8,
P. Rohini5, Monica Sharma2, Raghu Ashrit7, Ashim Mitra2, Karuna Sagar10
1. Ministry of Earth Sciences, NCESS, Thiruvananthapuram
2. India Meteorological Department, New Delhi
3. National Centre for Earth Science Studies, Trivandrum
4. India Meteorological Department, Chennai
5. Indian Institute of Tropical Meteorology, Pune
6. India Meteorological Department, Pune
7. National Centre for Medium Range Weather Forecasting, Noida
8. National Atmospheric Research Laboratory, Tirupati
9. Indian Institute of Science Education and Research (IISER), Bhopal
10. India Meteorological Department, Amravati

Rajeevan et al., 2022, Northeast Monsoon of South Asia, Meteorological Monograph,
MoES/IMD/Synoptic Met/02(2022)/27, 1-216

Copyright© 2022 by India Meteorological Department
All Rights Reserved

Disclaimer and Limitations
IMD is not responsible for any errors and omissions. The geographical boundaries
shown in the publication do not necessarily correspond to the political boundaries.

Published in India
By
India Meteorological Department, Ministry of Earth Sciences,
Lodi Road, New Delhi – 110 003 (India)
Phone: 91-11-43824298
Email: mausampublication@gmail.com

Executive Summary
1

Document title

Northeast Monsoon of South Asia

2

Document type

Meteorological Monograph3

Issue No.

MoES/IMD/Synoptic Met/02(2022)/27

4

Issue date

09.12.2022

5

Security Classification

Unclassified

6

Control Status

Unclassified

7

No. of Pages

224

8

No. of Figures

140

9No. of reference

135

10

Annexure

Nil

11

Distribution

Unrestricted

12

Language

English

13

Editors /Authors

M Rajeevan, M. Mohapatra, C. K. Unnikrishnan, B. Geetha,
S. Balachandran, O. P. Sreejith, P. Mukhopadhyay, D. R. Pattanaik,
Pulak Guhathakurta, Pankaj Kumar , K. Niranjan Kumar, Jyoti Bhate,
P. Rohini, Monica Sharma, Raghu Ashrit, Ashim Mitra and Karuna Sagar

14

Originating Division/Group

Dr. M. Rajeevan, Distinguished Scientist, Ministry of Earth Sciences

15

Reviewing and Approving
Authority

Director General of Meteorology, India Meteorological Department

16

End users

Operational Forecaster, Modeler, Researcher, Government, Academics etc.

17

Abstract

18

Key Words

Understanding on diurnal, intra-seasonal, seasonal variability, monsoon
onset, synoptic weather systems and capability of making more accurate
forecasts using the state-of-the-art dynamical models during the NE
monsoon season have improved substantially in recent years. The presentmonograph has incorporated more recent research results from studies
using long term and updated climate data sets which would be very usefulfor forecasters as reference material and also students, young researchers
venturing into NE monsoon research. The details of synoptic systems
affecting south peninsula and Sri Lanka and forecasting aspects of theseweather systems are also discussed. A brief summary of the verification of
forecasts of NE monsoon rainfall over India using two different weather
prediction systems is included as reference material for forecasters.
Southwest Monsoon, Indian Ocean Dipole, Multi-model Ensemble, Forecast
Verification, Heavy rainfall, Monsoon and Agriculture

Contents
Foreword

1

Acknowledgements

2

Acronyms

3-4

Chapter-1: Introduction

5-9

Chapter-2: Climatological Features

10-36

Chapter-3: Mean Rainfall Distribution

37-51

Chapter-4: Synoptic Systems

52-98

Chapter-5: Onset and Withdrawal

99-116

Chapter-6: NE Monsoon Variability

117-154

Chapter-7: NE monsoon over Sri Lanka

155-173

Chapter-8: Numerical Weather Prediction Guidance

174-190

References191-203

Bibliography204-218

1

Acknowledgements

The Meteorological Monograph on NE Monsoon of South Asia wasprepared with the kind support of the Ministry of Earth Sciences (MoES) and India
Meteorological Department (IMD). We thank Dr. M. Ravichandran, Secretary,
MoES and Dr. Gopal Iyengar, Advisor for their kind support and encouragement tocomplete this monograph. We also would like to thank the Director, NCESS and
Administration of NCESS for providing all the required facilities for completing thiswork.
We are also thankful to Dr. R. R. Kelkar, Dr. N. Jayanthi, Dr. Y. E. A. Raj,
Dr. G. Srinivasan, Dr. E. N. Rajagopal, Dr. Arindam Chakraborty and Dr. Venkat
Ratnam for their critical and elaborate comments on the earlier versions of thedraft, which helped us to improve the quality of this Meteorological Monograph.

2

Acronym
AMO: Atlantic Multi-decadal Oscillation
AO: Arctic Oscillation
APCC: Asia-Pacific Economic Cooperation (APEC) Climate Center
CAP: Coastal Andhra Pradesh
CFS: Coupled Forecasting System
CS: Cyclonic Storms
CSI: Critical Success Index
CTCZ: Continental tropical convergence zone
CV: Coefficient of Variation
DCC: Deep convective clouds
FAR: False Alarm Rate
FIM: First Inter-Monsoon
GEFS: Global Ensemble Forecasting System
GFS: Global Forecasting System
IMD: India Meteorological Department
IOD: Indian Ocean Dipole
IPCC: Intergovernmental Panel on Climate ChangeITCZ: Inter Tropical Convergence Zone
LOPAR: Low Pressure Area NAO: North Atlantic Oscillation
MJO: Madden Julian Oscillation
MME: Multi-Model Ensemble
MoES: Ministry of Earth Sciences
MSLP: Mean Sea Level Pressure (MSLP)
NCEP/NCAR: National Centres for Environmental Prediction/National Centre for
Atmospheric Research
3

NCMRWF: National Centre for Medium Range Weather Forecasting
NCUM: NCMRWF Unified Model
NEM: North East Monsoon
NEMR: NE monsoon rainfall
NOAA: National Oceanographic and Atmospheric Administration
NWP: Numerical Weather Prediction
OLR: Outgoing Long Wave Radiation
PDO: Pacific Decadal Oscillation
POD: Probability of Detection
PSS: Pierce’s Skill Score
PWC: Precipitable Water Content
SCS: Severe cyclonic storms
SEDI: Symmetric Extreme Dependency Index
SHET: Southern Hemispheric Equatorial TroughSIM: Second Inter-Monsoon
SOI: Southern Oscillation Index
SPI: Southern Peninsular India
SST: Sea Surface Temperature
SWM: South West Monsoon
TEJ: Tropical Easterly Jet
UTC: Universal Coordinated Time

4

Chapter-1
Introduction
South Asia experiences two monsoons, Southwest monsoon or summer monsoon
during June to September and the Northeast (NE) Monsoon or winter monsoon during
October to December. While the boreal summer monsoon (June to Sept) is responsible
for a major portion of annual rainfall over India, rainfall received during the NE monsoon
season (Oct-Dec) is also important, especially for the south peninsula, Sri Lanka andMaldives. During the southwest monsoon season, the southeastern parts of India
remain in the rain-shadow region and receive moderate rains.
NE monsoon is associated with the seasonal reversal of surface and lower
tropospheric winds, which starts by October. During the southwest monsoon season,
surface pressure gradient is directed from the Indian ocean to land with southwesterly
monsoon winds in the lower troposphere. During the NE monsoon season, the pressure
gradient reverses (from land to the Indian ocean) resulting in northeasterly trade winds,
which is the basic state of the system. This change in surface pressure gradient andlower tropospheric winds is associated with the southward movement of thecontinental tropical convergence zone (CTCZ) and the subtropical anticyclone in theupper troposphere. Therefore, the NE monsoon is often described as the retreatingphase of the southwest monsoon.
In a broader perspective, it is also associated with the northern hemisphericwinter circulation dominated by a strong surface high pressure region over Siberia, aprimary low over eastern equatorial Pacific region and secondary shallow lows over thenorth Indian ocean (Geetha, 2011, Raj 2012). A salient feature of the seasonal
circulation during the winter monsoon is the low-level northeasterly flow over the
Indian subcontinent and southeast Asia. There are similarities in circulation and rainfall
patterns of southeastern peninsular India and other South / Southeast Asian regions andalso the established roles of Siberian High, sub-tropical westerly jet stream and the5teleconnection influences of global parameters such as ENSO on the NE Monsoon
rainfall (Geetha 2011). There is an asynhronicity in the establishment of northeasterlies
over the Bay of Bengal and the South China Sea; the northeasterlies appear over the Bayof Bengal in late October, a month later than over the South China Sea (Sengupta andNigam, 2019).
NE monsoon is relatively dry, stable and has less vertical extend compared to thesouthwest monsoon. The India Meteorological Department (IMD) refers to the October
to December period as the northeast monsoon, which is a part of the northeast trades(Dhar et al., 1982, Dhar and Rakhecha, 1983). Dhar and Rakhecha (1983) studiedmonthly rainfall data for a 100-yr period (1877-1976) to investigate the association
between the northeast and southwest monsoons over Tamil Nadu, and concluded thatsummer rainfall is negatively correlated with NEM rainfall.
NE monsoon rainfall exhibits variability from diurnal to synoptic, intra-seasonaland inter-annual time scales. The inter-annual variability of the NE monsoon rainfall
influences agricultural production and water resources over south peninsular India. The
NEMR affects the productivity of rice and maize in the provinces of Tamil Nadu andAndhra Pradesh (Krishna Kumar et al., 2004). During the years when the NE monsoon isdeficient it has been noted that there is a considerable decrease in agricultural
production over the region. The NE monsoon rainfall is highly variable both spatially andtemporally. The coefficient of variation (inter-annual) of the NEMR averaged over thefive subdivisions is 25%, which is more than that of the southwest monsoon rainfall
averaged over the country as a whole (10%). South peninsula is well known for large
year to year variability. In 2015, the city of Chennai experienced deluge in 2015, which
resulted significant loss of life and property. Four years later, in 2019, Chennai city
experienced severe water shortage and crisis. During the NE monsoon season, south
Asia also experiences many extreme weather events like heavy precipitation, strong

6

winds and floods. Some of these extreme events are associated with the passage oftropical cyclones forming over the Bay of Bengal.
In the literature, there is a controversy about using the term “Northeast”
monsoon (IMD, 1973). As per the classifications given by the India Meteorological
Department (IMD), the October-December period constitutes the “Post-monsoon
season”. It is also known as “the Retreating Southwest Monsoon Season”, as the
monsoon convergence zone shifts to lower latitudes during this season. Mr Blanford,
who was the first Director General of the India Meteorological Department (IMD) in his
Memoirs on “Rainfall of India” raised the question about the term “Northeast Monsoon
Rains” (IMD, 1973). IMD discussed this aspect on many occasions and the IMD decided
to retain the term. IMD recommends that “the term Northeast Monsoon should
continue to be used for rainfall season over south peninsula during October toDecember”. Subbaramayya (1976) had examined meridional sea-level pressure profiles
over India, the Atlantic and Pacific and of winter rainfall over India and Sri Lanka andsuggested that that the so-called north-east monsoon over south-east India differs little,
either in wind in weather, from the generality of the northern-hemisphere trade winds.
Compared to the southwest monsoon, adequate attention was not given toaddress various aspects of the NE monsoon. There are only a few research papers
addressing NE monsoon over south Asia. After the classic work by Srinivasan andRamamurthy (IMD 1973), a few papers have been published on different aspects of NE
Monsoon. The review article by Raj (2012) in the Monsoon Monograph edited by Ajit
Tyagi et al. is an excellent documentation of various aspects of NE Monsoon season overIndia. The papers by Sreekala et al. (2012) and Rajeevan et al. (2012) also are good
reviews on the variability of NE monsoon rainfall and its prediction aspects. Amudha(2016) using modern meteorological data analyzed the characteristics of Indian
Northeast monsoon and cyclonic storms of the North Indian Ocean. The study revealed7many interesting aspects of the movement of clouds and other rainfall regimes
associated with the NE monsoon.
These publications contain all relevant references of research papers on NE
monsoon published till 2012. Since then, there is growing research interest on NE
Monsoon with much more papers are getting published in the recent years(Satyanarayana et al., (2020), Raj and Amudha (2022), Raj and Geetha (2008),
Balachandran et al., (2006), Nair et al., (2013), Dash et al., (2019), Naidu et al., (2012),
Maharana et al., (2022), Prasanna et al., (2019), Mondal and Choudhari (2022),
Suneetha et al., (2018), Prasanna et al., (2021), Geetha and Balachandran (2021),
Saikranthi and Chiranjeevi (2022), Mishra and Nagaraju (2021), Geetha and Raj (2014),
Amudha et al., (2016), Prasanna et al., (2020), Prakash and Gairola (2013), Kiran Kumar
and Singh (2021), Pattanaik and Mohapatra (2017), Parvathi et al. (2017), Sengupta andNigam (2019), Mishra and Bharadwaj (2019)). The paleoclimate aspects of NE monsoon
are recently discussed in Achyuthan (2021) and the references therein.
In spite of the growing research interest, there is a lot of scope for an exhaustive
compilation of research results based on recent data products and forecasting tools on
different aspects of NE monsoon over south Asia. During the last one decade, ourunderstanding on diurnal, intra-seasonal, seasonal variability, monsoon onset, synoptic
weather systems during the NE monsoon season has improved substantially. At thesame time, our capability to make more accurate forecasts also has improved by using
the state-of-the-art dynamical models or Numerical Weather Prediction (NWP) models.
The present monograph is designed accordingly to include more recent research results,
which have resulted in better understanding of NE monsoon and its variability in all timescales and their prediction aspects. Such a compilation would be very useful forforecasters as reference material and also students, young researchers venturing into
NE monsoon research.

8

In this monograph, the climatological features of NE monsoon are discussed using
long term and updated climate data sets. Discussions on mean temperatures, sea levelpressure, upper air circulation features, monthly and seasonal rainfall, heavy rainfall,
extreme winds, characteristics of monsoon clouds, thunderstorm and lightning are
included. The variability of NE monsoon from diurnal, synoptic, intra-seasonal andseasonal time scales is also discussed in detail with a few case studies. The details ofsynoptic systems affecting south peninsula and Sri Lanka during the season andforecasting aspects of these weather systems are also discussed. A brief summary of theverification of forecasts of NE monsoon rainfall over India using two different weather
prediction systems is included as reference material for forecasters. A separate sectionof NE monsoon season over Sri Lanka is also provided to widen the scope of this book.
The grey areas where more research is required are identified and discussed.

9Chapter-2
Climatological Features
NE monsoon predominantly influences south peninsula, consisting of fivemeteorological sub-divisions (Coastal Andhra Pradesh, Rayalaseema, Tamil Nadu, SouthInterior Karnataka and Kerala) as shown in Fig. 2.1. In addition, NE monsoon contributes
significant amount of rainfall over other south Asian countries like Sri Lanka andMaldives (Fig 2.2 a).
In Sri Lanka, the southwest monsoon season lasts during May to September. The
period October to November is termed as second inter-monsoon season. The second
Inter-monsoon period of October - November is the period with the most evenly
balanced distribution of rainfall over Sri Lanka. Almost the entire island receives inexcess of 400 mm of rain during this season, with the Southwestern slops receiving
higher rainfall in the range 750mm to 1200 mm. NE monsoon is experienced during theperiod, December to February. During this period, the highest rainfall figures arerecorded in the north, eastern slopes of the hill country and the eastern slopes of the
Knuckles/Rangala range (http://www.meteo.gov.lk/index.php?option=com_content&
view=article&id=94&Itemid=310&lang=en).
In India, the NE monsoon season (Oct-Dec) contributes about 11% of its annual
rainfall over the country as a whole. South peninsula receives much more rainfall
compared to northern parts of the country. Many districts over the south peninsula
receive 30–60% of the annual rainfall during this season. This season is also termed theretreating monsoon season or the post-monsoon season in which the zone of maximum
rainfall migrates to southern parts of India, Sri Lanka and the neighboring sea. With thewithdrawal of the Southwest monsoon from the northern parts of India, the mean sea
level pressure and upper tropospheric wind circulation patterns over India change
rapidly from the summer monsoon type to winter type. By October, the Inter-tropical

10

Convergence Zone (ITCZ) or the monsoon convergence zone which is positioned overnorthern parts of India starts shifting southwards.
Fig.2.2 b shows the annual cycle of precipitation averaged over the longitudes
700-850, calculated using GPCP monthly rainfall data during the period 1979-2021. It
clearly shows the northward movement of monsoon convergence and rainfall towards
the northern parts starting from May and the retreat during October-December. NE
monsoon season coincides with this retreating phase of the Inter Tropical Convergence
Zone (ITCZ). There are also associated rapid changes in upper air circulation and sea
level pressure patterns. During the October-December season, there is a clear evidenceof rainfall maximum in the Southern Hemisphere (SH), coinciding with the presence ofSH equatorial trough (SHET).

Fig. 2.1. Five Sub-divisions over South Peninsula under the NE monsoon region
11Fig. 2.2 a. NE monsoon rainfall (Oct-Dec) in mm/day over South Asia

Fig. 2.2 b. Hovmuller Diagram showing annual cycle of precipitation (mm/day) over the
Indian region, averaged over the longitudes between 700E- 900E. This plot is preparedusing GPCP monthly rainfall data (1979-2021).

12

In the next sections, climatological aspects of sea level pressure and upper air
circulation features are discussed.
2.1. Sea Level Pressure
The mean sea level pressure chart for the month of July is shown in Fig 2.3. The
pattern shows a heat low over the northwestern parts of India and adjoining Pakistanwith a south-north pressure gradient. The pressure gradient between the equator and100N is about 12 hPa. However, by September, the pressure gradient generally weakens
and by October the pressure gradient completely reverses with higher pressure over thenorthern parts of India.
The surface sea level pressure charts for Oct to Dec are given in Fig.2.4, 2.5 and2.6 respectively. By October, a low pressure area gets established over the Central andSouth Bay of Bengal and adjoining east coast, which shifts to south Bay in November
and further southwards, close to the equator in December. This low pressure area ismore marked over the southwest Bay in October and November. In the Arabian Sea, thelow pressure area is not well marked during these months. However, an east-westoriented trough of low pressure is observed in nearly the corresponding latitudes as in
the Bay of Bengal, at least in October and November. In October, the pressure gradientis generally weak, which gets strengthened by November. While the low pressure area
gets shifted southwards, the high pressure area associated with the Siberian High also
gets strengthened over the northern parts of the country. The surface pressure gradient(high in the north and low in the south) over the Bay of Bengal also strengthens. During
November and December, the isobars are nearly parallel to the equator, suggestingstronger surface easterlies/northeasterlies over the Bay of Bengal.

13

Fig. 2.3. Mean Sea Level Pattern (hPa) during July (period: 1979-2021). Source: ERA5
reanalysis.

Fig. 2.4. Same as in Fig. 2.3 but for October.

14

Fig. 2.5. Same as Fig 2.3 but for November.

Fig. 2.6. Same as Fig 2.3 but for December.

15

2.2. Upper Air Circulation Features
The upper air circulation features from Oct to Dec are shown in Fig.2.7, 2.8 and2,9 respectively. These maps show the mean wind pattern at 850, 700, 500 and 200 hPa
levels. During October, an east-west trough is seen extending from the lower levels to
700 hPa from South Bay to the South Arabian Sea. This east-west trough is seen shifting
southwards from October to December, consistent with the equatorward shifting of the
ITCZ. By December, the east-west trough is seen close to 50 N. This east-west trough is
the region with positive vorticity and convergence, thus causing abundant rainfall overthis region. This shear zone in the lower troposphere contributes to the genesis of lowpressure systems over the Bay of Bengal and the Arabian Sea.

Fig. 2.7. Mean wind flow pattern during October at a) 850 hPa b) 700 hPa c) 500 hPa
and d) 200 hPa levels. Source: ERA5 reanalysis. The colour shading represents windspeed in m/s. The data period: 1979-2021.

16

Fig. 2.8. Same as Fig 2.7 but for November.

Fig. 2.9. Same as Fig 2.7, but for December.
17

In the middle and upper troposphere (500 hPa and 200 hPa), easterlies prevailover the south Peninsula and adjoining seas. The sub-tropical ridge at these levels also
shifts southwards from October to December. The sub-tropical ridge in the upper levels
is an area of divergence and contributes to the intensification of low pressure systems
over the region. The Tropical Easterly Jet (TEJ), which is one of the semi-permanentsystems during the southwest monsoon totally disintegrates once the southwest
monsoon is withdrawn from the country. From October to December, the sub-tropicalwesterlies over the northern parts of the country also get strengthened and startmoving to lower latitudes. An upper tropospheric ridge gets established over thesouthern parts of India by October and November at the 200 hPa level.
2.3. Mean Precipitable Water Content (PWC)
Spatial distribution of mean precipitable water content provides useful
information on moisture sources and sinks over the region during the NE monsoon
season. Mean precipitable water content is calculated as the weighted average ofmoisture from surface to upper troposphere (normally up to 300 hPa). It provides
information of moisture content in the whole atmospheric layer. Higher values suggest
moisture sources.
The PWC is a measure of available moisture for precipitation in the atmosphere.
It measures the maximum possible precipitation of water, which may precipitate out
from a given atmospheric column, if nothing else (e.g., surface evaporation, moistureadvection) happens over a given time span. Of course, this is rather an oversimplified
picture, because it is hard to imagine that all PWC would condensate within a given
column under any conceivable process. Nevertheless, it would be good enough tointerpret PWC as an upper bound for a possible precipitation at a given moment with agiven atmospheric column.
Fig. 2.10 a, b and c show the spatial distribution of precipitable water content
during October, November and December respectively. During October, the maximum

18

PWC is observed over the Southeast Bay of Bengal (east of Andamans) with values of 50
kg/m2. The PWC reduces sharply westwards. Over the Bay of Bengal, PWAT values are
more than 45 kg/m2. Over south Peninsula, PWC varies between 40 to 45 kg/m2. During
November, the maximum zone of PWC slightly shifts southwards towards the equator.
Over the south Peninsula, however, the PWC values are between 40 to 45 kg/m2. During
December, PWC maximum again shifts southwards and lies close to the equator. Overthe south Peninsula, PWC values sharply reduce, ranging from 34 to 40 kg/m2. During
November and December, PWC decreases from south to north and isolines are almost
parallel to the equator. Over the south Peninsula, PWC values are higher over the east
coast as it reduces sharply towards interior parts.

Fig. 2.10 a. Mean precipitable water content (mm) during October (1979-2021). Data
source: NCEP/NCAR reanalysis.

19

Fig. 2.10 b. Same as Fig 2.10 a but for November.

Fig. 2.10 c. Same as Fig 2.10 a but for December.

20

These are monthly mean patterns, calculated using long term climatological data.
However, on a typical day, PWC values can either increase or decrease, depending upon
the prevailing synoptic conditions. For example, low pressure weather systems
transport more moisture and PWC values can increase sharply in a day or two. To see
the variation of moisture content in the atmospheric column, it is better to monitor the
PWC changes in addition to low-level humidity. PWC can be easily calculated using
radiosonde profiles of moisture.
2.4. Mean Air Temperatures
Seasonal(October-December)meanminimum,

maximumandmean

temperatures over South Peninsula are shown in Fig. 2.11 a, b and c respectively. This
analysis was made using the IMD’s 1 × 1 degree gridded daily temperature data
(Srivastava et al., 2009). Seasonal mean minimum temperatures are the highest along
the coasts and they decrease towards the interior parts. Over the east coast, seasonalmean minimum temperatures are of the order of 22-230C. Over the eastern parts of thesouth peninsula, the isotherms run parallel to the coast. Over the interior parts,
minimum temperatures are below 200C. Seasonal maximum temperature over thesouth peninsula varies between 280 – 310C, except over the interior parts of Karnataka
where maximum temperatures are below 28oC. Mean temperatures (Fig. 2.11 c) overthe coastal Andhra Pradesh are more than 26oC, while over the extreme south peninsula(Tamil Nadu and Kerala), the mean temperatures exceed 270C.
2.5. Sea Surface Temperatures (SST)
During the NE monsoon season, synoptic systems like tropical cyclones, lows anddepressions and easterly waves, the presence of east-west trough contribute to rainfall
over the South Peninsula. As these weather systems form over the Bay of Bengal andthe Arabian Sea, oceanic conditions like Sea Surface Temperature (SST) play animportant role. However, higher SSTs lead to more convection only if atmospheric21

conditions also are favourable. Therefore, it is important to monitor closely the oceanic
conditions like SST and Ocean heat content regularly during the NE monsoon season.

Fig. 2.11 a. Mean Minimum Temperatures (0C) (b) Mean Maximum Temperatures during
October to December (1972-2021) Source: IMD gridded temperature data.

Fig. 2.11 c. Mean Temperatures during October to December (1972-2021) Source: IMD
gridded data

22

Fig. 2.12 a, b and c show the spatial distribution of SSTs during October,
November and December respectively. During October, SSTs are of the order of 28.5 0C
over the central Bay of Bengal. Over the eastern parts of the Arabian Sea, SSTs are of theorder of 28.50C. But it sharply reduces westwards and over the western Arabian Sea,
SSTs are below 27.50C. During November, the SST maximum slightly shifts southwards
and is located near the east central Bay of Bengal with values exceeding 28.50C. Another
SST maximum is observed over the east central Arabian sea off the Kerala and Karnataka
coasts. By December, both the Bay of Bengal and the Arabian Sea cools off quickly. The
28.50C isotherm is now seen near the equator. SSTs over the west central Bay of Bengal
are below 28.00C and sharply decreases towards the north Bay of Bengal.
The next section discusses the relationship between SST and convection during
the NE monsoon season.

Fig. 2.12 a. Mean Sea Surface Temperature (SST) (degree C) during October, Period:
1972-2021. Source : HadISST, Met Office Hadley Centre.
23

Fig. 2.12 b. Same as Fig 2.12 a but for November.

Fig. 2.12 c. Same as Fig 2.12 a, but for December.

24

2.6. Relationship between SST and Convection during the NE monsoon
There is a strong relationship between SST and atmospheric convection over thetropical ocean basins. Previous studies (Gadgil et al., 1984, Sabin et al., 2012) haveshown that during the southwest monsoon season, convection over the Bay of Bengal isinitiated when SSTs are between 270-280C. A similar analysis was made for the NE
monsoon season (Oct-Dec) using long term data of SST and Outgoing Long Wave
Radiation (OLR) data over the Indian Ocean. OLR is a proxy for atmospheric convection.
Fig. 2.12 d shows the scatter plot between SST and OLR over the a) the ArabianSea (0-200 N, 60-750 E) and b) the Bay of Bengal (0-200 N, 80-1000 E). The data during
1982-2021 have been used for these plots. Over the Bay of Bengal, convection starts
abruptly and increases sharply once the SST threshold crosses 28.0 0 C. At 28.50C, the
OLR in the Bay of Bengal is 20 Wm-2 lower than in the Arabian Sea due to much deeper
clouds over the Bay of Bengal. The mode with the highest probability for SST above thethreshold of 280C over the Bay of Bengal has low OLR (about 210 Wm-2) corresponding
to deep convection whereas that for the Arabian sea it is around 250 Wm-2.
It is interesting to note that even above the SST threshold of 28 0C, there arepoints with OLR values more than 240 Wm-2 suggesting severe convection is not present
at these locations. It may be worthwhile to note that only SST threshold will not decide
whether convection will occur or not. Initiation of convection also depends on
atmospheric circulation, which should be conducive for low level convergence andascending motion (Lau et al., 1997). This aspect is not further examined here and should
be taken up as a separate study.
2.7. Spatial distribution of cloud properties
A further analysis was made on the spatial structure of some vital cloudproperties during November over the region using the International Satellite Cloud
Climatology Product (ISCCP) data (Rossow et al., 1991) for the period 1998-2019.
Fig. 2.13 a, b and c show the spatial pattern of Deep convective clouds (DCC) (%), Cloud

25

Optical Thickness and Cloud Water Path (cm) respectively. Cloud optical thickness is thevertical optical thickness between the top and bottom of a cloud. The optical thicknessof a cloud is the degree to which the cloud prevents light from passing through it. It
depends directly on the cloud thickness, the liquid or ice water content and the size
distribution of the water droplets or ice crystals.

Fig. 2.12 d. The scatter plot between SST and OLR over a) Arabian Sea (0-200 N, 60-750
E) and b) Bay of Bengal (0-200 N, 80-1000 E). The data during 1982-2021 have been used
for these plots.

Deep convective clouds are clouds with cloud top crossing above 400 hPa. The
spatial distribution of DCC suggests maximum percent DCC over the east equatorial
Indian Ocean (12-14%) coinciding with warmer SSTs (Figs 2.13 a, b and c). Over thesouthwest Bay of Bengal and off the Tamil Nadu coast, DCC is around 8%.
Comparatively, DCC distribution over the Arabian sea is much smaller, suggesting the
Bay of Bengal has much deeper convective clouds.
26

Fig. 2.13 a. Spatial distribution of Deep Convective Clouds during the NE monsoon
season (October- December) based on the ISCCP data. Period: 1998-2019.

Fig. 2.13 b. Spatial distribution of Cloud Optical thickness during the NE monsoon season(October- December) based on the ISCCP data. Period: 1998-2019.
27

Fig. 2.13 c. Spatial distribution of Cloud water path (cm) during the NE monsoon season(October- December) based on the ISCCP data. Period: 1998-2019.

The spatial distribution of cloud optical thickness suggests maximum distribution
over the east equatorial Indian Ocean coinciding with warmer SSTs and a large amountof deep convective clouds. Another maximum of cloud optical thickness is observed
over the southeast Peninsula and adjoining Bay of Bengal. The spatial distribution ofcloud liquid path (cm) shows that maximum is observed over the east equatorial Indian
Ocean. Another maximum of cloud liquid path with values more than 120 cm isobserved over the southwest Bay of Bengal off Tamil Nadu Coast. Therefore, the easternparts of south peninsula and adjoining Bay of Bengal have deeper convective clouds
with larger optical depth and cloud liquid water path. The spatial distribution shown
here is based on long term climatology. On a day-to day basis, there could be significant
variations in the distributions of cloud properties and they should be monitored using
INSAT and similar satellite data products.

28

Characteristics of raindrop size distribution (DSD) are studied by Rao et al. (2009)
during the southwest (SW) and northeast (NE) monsoon seasons using 4 1/2 years ofDroplet Size Distribution (DSD) measurements made at Gadanki (13.5°N, 79.2°E) by an
impact-type disdrometer. The observed DSD is found to be distinctly different in the NE
monsoon from that of the SW monsoon. The stratified DSD (based on rain rate) shows
more small drops and fewer bigger drops in the NE monsoon compared to the SW
monsoon, particularly in the low rain rate regimes.
2.8. Thunderstorms/ Lightning
During the NE monsoon season, the south Peninsula experiences large scale
thunderstorm activity. A good review of thunderstorm activity over India using station
data is given in a recent paper by Omvir Singh and Bharadwaj (2017). A detailed study
on lightning activity over the Indian region was published by Ranalkar and Chaudhari(2009) using the TRMM Lightning flash data. The average number of thunderstorm days
during October, November and December are given in Fig. 2.14 a, b and c respectively.
These plots are taken from the IMD Climate Hazards and Vulnerability Atlas of India(2022) (https://imdpune.gov.in/hazardatlas/index.html). The data from 1981-2010 havebeen used to prepare these spatial maps.
During October, some districts in coastal Andhra Pradesh, Tamil Nadu and Kerala
experience more than 4 thunderstorm days. Over south interior Karnataka andRayalaseema, average number of thunderstorm days is between 2 and 3. During
November, thunderstorm activity is reduced in Coastal Andhra Pradesh. However, Tamil
Nadu and Kerala still experience thunderstorms with average number varying between
3 and 7 days. Over the coastal Andhra Pradesh, south interior Karnataka andRayalaseema, thunderstorm activity is slightly reduced. By December, thunderstorm
activity is further reduced. During December, maximum thunderstorm days areobserved over Kerala and southern parts of Tamil Nadu.

29

Fig. 2.14 a. Average number of Thunderstorm Days during October, 1981-2010. (Source:
IMD Climate Hazards and Vulnerability Atlas of India, 2022).

Fig. 2.14 b. Same as Fig 2.14 a, but for November.
30

Fig. 2.14 c. Same as Fig 2.14 a, but for December.

Fig. 2.15 a, b and c show the spatial pattern of the average number of lightning
flashes per sq km per day during October, November and December respectively. Theseplots are also taken from the IMD Climate Hazards and Vulnerability Atlas of India(2022). During October, maximum lightning activity is observed over the interior parts ofTamil Nadu and the northern parts of Kerala. During November, lightning flash activity ismostly confined to Kerala and adjoining south Tamil Nadu. During December, thelightning activity is mostly confined to Kerala. Thus, during the NE monsoon season, thestate of Kerala witnesses maximum lightning activity. This could be related to proximity
to the Arabian Sea, thus abundant moisture transport and orography along the west
coast supporting deeper convective activity.

31

Fig. 2.15 a. Average number of Lightning flashes per sq km per day during October.
(Source: IMD Climate Hazards and Vulnerability Atlas of India, 2022).

Fig. 2.15 b. Same as Fig. 2.15 a, but for November.

32

Fig. 2.15 c. Same as Fig 2.15 a, but for December.

2.9. Extreme Wind Speed
For providing wind speed forecasts for specific purposes to users, it is important
to know the climatology of extreme wind speed during the NE Monsoon season.
Fig. 2.16 a, b and c shows the spatial distribution of Extreme surface wind speed (inm/sec) in October, November and December respectively. The surface wind speed isnormally measured at a height of 10.0 m. These maps are also derived from the IMD
Climate Hazards and Vulnerability Atlas of India, 2022.
During October, extreme surface wind speeds exceeding 18 m/sec are observed
over the north coastal Andhra Pradesh and interior parts of Tamil Nadu. Over otherparts of the south Peninsula, extreme surface wind speed varies between 12 and18 m/sec. During November, east coast of Tamil Nadu and coastal Andhra Pradesh has
maximum risks due to extreme surface wind speeds. This region experiences tropical
weather systems like depressions and cyclonic storms every year. Over this region,33

extreme surface wind speed varies between 22 and 57 m/sec. Thus, this region is very
prone to extreme surface wind speed.

Fig. 2.16 a. Extreme Wind Speed (m/sec) during October (Source: IMD Climate Hazards
and Vulnerability Atlas of India, 2022).

Extreme surface wind speed decreases sharply towards interior parts of south
Peninsula. By December, northern parts of Tamil Nadu and adjoining parts ofRayalaseema experience the highest extreme wind speed. Over this region extreme
surface wind speed varies between 41 and 59 m/sec. Over the southeast parts of Tamil
Nadu, extreme surface wind speed varies between 31.0 and 41.0 m/sec. Over otherparts of south Peninsula, extreme surface wind speed varies between 12.6 and31.5 m/sec.

34

Fig. 2.16 b. Same as Fig 2.16 a, but for November.

Fig. 2.16 c. Same as Fig 2.16 a, but for December.

35

Extreme surface wind speed observed over the eastern parts of south peninsula
could be attributed to tropical cyclones making landfall over this region during theseason. A detailed description of tropical cyclones including their tracks during the NE
monsoon season is included in Chapter-4.

36Chapter-3
Mean Rainfall Distribution
In this chapter, various features of mean rainfall during the NE monsoon are
discussed.
3.1. Mean Rainfall over Met Sub-divisions
The mean monthly rainfall in the various meteorological sub-divisions in thesouth peninsula during the period October to December is given in Table 3.1. Thesemean values are calculated using the rainfall data of 1971-2020.

TABLE 3.1
Mean monthly, seasonal and annual rainfall (cm)
Based on the IMD sub-divisional data for the period 1971-2020
Subdivision name

OCT NOV DEC

Season's
total

ANNUAL

32

104

COASTAL ANDHRA PRADESH & YANAM
Rainfall(cm)

18

11

3

Rainfall as % of seasonal total

56

34

9

Season's total as % of annual total

31

RAYALSEEMA
Rainfall(cm)

13

8

3

Rainfall as % of seasonal total

54

33

13

Season's total as % of annual total

24

73

33

TAMIL NADU, PUDUCHERRY & KARIAKAL
Rainfall(cm)

17

18

9

Rainfall as % of seasonal total

39

41

20

Season's total as % of annual total

44

48

37

92

SOUTH INTERIOR KARNATAKA
Rainfall(cm)

14

5

1

Rainfall as % of seasonal total

70

25

5

Season's total as % of annual total

20

103

19

KERALA & MAHE
Rainfall(cm)

31

15

3

Rainfall as % of seasonal total

63

31

6

Season's total as % of annual total

49

289

17

Table 3.1 shows that for the South Peninsula as a whole, October is the rainiest
month. However, in Tamil Nadu, November gets as much as rains in October. By
December, the rainy season is practically confined over extreme south Peninsula
including Tamil Nadu. The south peninsula receives rainfall, when the seasonal eastwest convergence zone (ITCZ) and associated weather systems are present over theregion. During December when the ITCZ moves further south, rainfall over the south
peninsula sharply reduces. When the east-west trough is present, synoptic systems likelows and depressions form over this trough zone and move towards the south peninsula
contributing to widespread rainfall activity.
Coastal Andhra Pradesh (CAP) gets rainfall during both the southwest andnortheast monsoon seasons. During the NE Monsoon season, CAP receives about 32 cm
of seasonal rainfall, with October contributing 56% of seasonal rainfall. About 34% of
seasonal rainfall occurs during November. The NE monsoon season contributes about
31% of annual rainfall. Over Rayalaseema, the NE monsoon season contributes about
24 cm, which is 33% of the annual total. October is the rainiest month during the season
and rainfall activity sharply reduces in November and December.
Tamil Nadu receives more rainfall during the NE monsoon season (OctoberDecember) compared to that during the Southwest monsoon season. There is a38

considerable increase in rainfall activity from September to October and November. In
December, rainfall activity is confined only to the coastal districts. The seasonal rainfall
during the NE monsoon season is around 44 cm contributing to 48% of its annual
rainfall. Over South-interior Karnataka (SIK) seasonal rainfall is around 20 cm, which isabout 19% of the annual total. Over SIK, October contributes maximum rainfall during
the season, while December hardly contributes to the seasonal total.
Over Kerala, the NE monsoon season contributes about 49 cm, which is about
17% of the annual total. During the NE monsoon season, Kerala receives maximum
rainfall, even slightly more than Tamil Nadu. October contributes maximum rainfall over
Kerala, which reduces in November and December.
3.2. Mean Spatial Distribution of Rainfall
Fig. 3.1 a, b and c show the monthly rainfall climatology during the months ofOctober, November and December. The mean values are calculated using the IMD
gridded data (0.25 X 0.25 degree) from 1972-2021. During October, maximum rainfall
exceeding 200 mm is observed over the coastal Andhra Pradesh, coastal Tamil Nadu andKerala. Over central parts of Kerala, monthly rainfall exceeds 300 mm. However, rainfall
reduces sharply towards the interior parts of south peninsula, where monthly rainfall isless than 150 mm. During November, monthly rainfall sharply increases over thenorthern parts of coastal Tamil Nadu and southern parts of coastal Andhra Pradesh,
where monthly rainfall is more than 300 mm. Over Tamil Nadu, monthly rainfall sharply
reduces towards the interior parts. Southern parts of Kerala experiences monthly
rainfall exceeding 200 mm. Over the rest of Tamil Nadu and Kerala, rainfall is between
100 and 150 mm. Over the South interior Karnataka and Rayalaseema, monthly rainfall
is less than 100 mm. During December, rainfall sharply reduces everywhere in the south
peninsula. Maximum rainfall exceeding 200 mm is observed over coastal Tamil Nadu
and reduces sharply towards interior parts. Over the rest of the south peninsula,monthly rainfall is less than 75 mm.

39

The spatial distribution of mean seasonal rainfall (October to December) is shownin Fig. 3.1 d. The spatial distribution suggests a rainfall maximum along the east coast ofnorth Tamil Nadu and south coastal Andhra Pradesh with seasonal average exceeding
800 mm. Seasonal rainfall reduces sharply towards interior parts of South Peninsula.
Another rainfall maximum is observed over Kerala. Over south peninsula, isohyets (lines
of equal rainfall) run parallel to the east coast with maximum over the east coast andreducing towards the interior parts. During the NE monsoon season, the KariakalVedarnyam belt receives the highest rainfall in the range 900-1000 mm with Vedarnyam
receiving 103 cm. The decrease of rainfall south of Vedarnyam is probably due tosheltered nature of the coast (due to Sri Lanka land mass on the east). Tuticorin
registers only 40-45 cm of normal rainfall.

Fig. 3.1 a. IMD Observed Rainfall Climatology in mm during October (1972-2021).
Source: IMD gridded data.

40

Fig. 3.1 b. Same as 3.1 a, but for November.

Fig. 3.1 c. Same as Fig 3.1 a, but for December.

41

Fig. 3.1 d. Seasonal Rainfall Climatology (in mm) during the northeast monsoon season(October- December), 1972-2021. Source: IMD gridded rainfall data.

Fig. 3.2 shows the time variation of mean rainfall averaged over the south
peninsula from 01 Sept to 31 Dec. The mean rainfall is calculated using the data of 19792021. The plot shows that October and December months contribute maximum rainfall
over the region. From the first week of December, rainfall activity over the region is
sharply reduced. This is due to the fact that by first week of December, the ITCZ shifts
towards much south and rainfall activity is mostly confined south of 100N.
The Coefficient of Variation (CV) of seasonal (October to December) monsoon
rainfall over the southern peninsula is given in Fig. 3.3. The CV of NE monsoon seasonalrainfall is generally higher compared to southwest monsoon season. CV varies from 30
to 50% over the south peninsula with the east coast experiencing smaller CVs compared
to interior.

42

Fig. 3.2. Daily climatological rainfall (in mm/day) over NE India from 1 Sep to 31 Dec
averaged over the period 1979-2021. The averaging was done over the area 70-850 E,
8-160 N.

Fig. 3.3. Coefficient of variation of seasonal Rainfall (in %) during the northeast monsoon
season (October- December), 1972-2021. Source: IMD gridded rainfall data.
43

During the NE monsoon season, a rainy day is assumed to be a day with rainfall of
2.5 mm or more. Fig. 3.4 shows the spatial distribution of the mean number of rainydays during the NE monsoon season. Over the north Tamil Nadu coast and parts of
Kerala and south Karnataka, mean rainy days are more than 30 during the season. Overthe remaining parts of Tamil Nadu, coastal Andhra Pradesh and remaining parts of
Kerala, number of rainy days varies between 20 and 30 days. Statistical trend analysis
suggests (Fig. 3.5), the mean number of rainy days over the interior parts of south
Peninsula is increasing. It is important to note that this pertains to long term
climatology. In a particular year, there could be an increase or decrease in the numberof rainy days over a particular station.

Fig. 3.4. Number of rainy days (> 2.5mm) during October to December (1972-2021).
Source: IMD gridded Data

44

Fig. 3.5. Trend in rainfall days during October to December (1972-2021).

3.3. Heavy Rainfall events during the NE monsoon season
It is important to know the spatial pattern of climatology of heavy rainfall over
the region. This will provide information on the climatological probability of heavy
rainfall occurrence over the region during the season.

The spatial pattern of heavy

rainfall events was prepared using the IMD 0.25 × 0.25 degree daily rainfall data (Paiet al. (1972-2020). It may be noted that the IMD’s definition of heavy, very heavy andextreme heavy rainfall is based on rainfall station data. Since the analysis given below isbased on the IMD’s gridded data and somewhat smoothed data, IMD’s definition
cannot be strictly used for defining heavy, very heavy and extreme rainfall. However,even with the gridded data, we could get a reliable understanding of the spatial
distribution of such heavy rainfall events.

45

Fig. 3.6 shows the spatial pattern of the number of days with heavy rainfall
between 65 mm-124 mm. It suggests the maximum number of days with heavy rainfall
is confined to the east coast of Tamil Nadu and the south coastal Andhra Pradesh,
where on average we can expect more than 2 days of heavy rainfall. Another area of
maximum heavy rainfall days is observed over south Kerala. Number of days with heavy
rainfall sharply reduces towards the interior parts of the south Peninsula.
Fig. 3.7 shows the spatial distribution of frequency (number of days) of rainfallwith 125 mm or more during the NE monsoon season. It suggests maximum frequency isfound over the coastal parts of north Tamil Nadu and Andhra Pradesh. It suggests thatthese very heavy rainfall spells are associated with the landfall of tropical
cyclones/depressions and lows along the east coast. On an average, we can expect
about one day of such an event over this region during the NE monsoon season.

Fig. 3.6. Mean number of days during October-December with 65 mm-124mm. Periodof the data 1972-2021.
46

Fig. 3.7. Mean in rainfall days with more than 125 mm/day (Period: 1972-2021).

3.5. Maximum Probable frequency of heavy rainfall
Fig. 3.8 shows the maximum probable frequency of heavy rainfall (between 65
and 124 mm) during October to December calculated using data from 1951-2019. Thismap was taken from the IMD Climate Hazards and Vulnerability Atlas of India, 2022. A
maximum probability of more than 7 days can be expected over the east-coast of Tamil
Nadu and Southern parts of Kerala and Tamil Nadu. Over the interior parts of the south
Peninsula, the maximum probability lies between 3-4 days.
Fig. 3.9 shows the maximum probable frequency of heavy, very heavy andextreme heavy rainfall (number of days) during the NE monsoon season (Oct-Dec). Overthe east coast of north Tamil Nadu, South Coastal Andhra Pradesh and southern most
districts of Tamil Nadu and Kerala, the maximum probable frequency is more than 15
days. Over other parts of coastal Pradesh, Tamil Nadu and south Kerala, the maximum
probable frequency is between 10-14. Over interior parts of the south Peninsula, thisnumber varies between 5-10 days.
47

Fig. 3.10. shows the maximum probable frequency of very heavy and extreme
heavy rainfall (number of days) during the season. Over the eastern coast of north Tamil
Nadu, south coastal Andhra Pradesh and southern most parts of Tamil Nadu and Kerala,
the maximum probable frequency of very heavy and extreme rainfall is between 5 and
11 days. Over the interior parts of the south Peninsula, this number varies between 1
and 4.
Thus, the eastern coast of north Tamil Nadu and south coastal Andhra Pradesh is
the region, with maximum probable frequency of heavy rainfall days during the NE
monsoon season. Southern parts of Tamil Nadu and Kerala also have similar maximum
probable frequency.

Fig. 3.8. Maximum Probable frequency of heavy rainfall events (Number of days):
October-December, 1951-2019. (Source: IMD Climate Hazards and Vulnerability Atlas ofIndia, 2022).

48

Fig. 3.9. Maximum Probable frequency of heavy, very heavy and extremely heavy
rainfall events (Number of days): October-December, 1951-2019. (Source: IMD Climate
Hazards and Vulnerability Atlas of India, 2022).

Fig. 3.10. Maximum Probable frequency of very heavy and extremely heavy rainfall
events (Number of days): October-December, 1951-2019. (Source: IMD Climate Hazards
and Vulnerability Atlas of India, 2022).

49

The city of Chennai experienced three phases of heavy rainfall that resulted indevastating floods during November and early December, 2015. Chakraborty (2015)
examined synoptic aspects of this flood event in detail. The study revealed that
propagating convective systems from the west Pacific Ocean intensified further over thewarm Indian Ocean before moving north towards the Indian land region. This northward
propagation was guided by two highs of mid-troposphere to the east and west of the
Indian region. While the high to the east was typical of an El Nino year, that the west
was associated with global phase shift of upper tropospheric Rossby wave. The study
revealed that similar highs to the west were present during other years of heavy rainfall
along the east coast of Peninsular India.
Fig. 3.11 shows Spatial variation of correlation coefficient between monthly mean
rainfall over the Indian landmass and area averaged 500 hPa geopotential height over
30-600E, 20-450N (Middle-east) in November for the period 1948-2014. It suggests that ahigh GPH over the Middle East is positively correlated with high amount of rainfall overcentral and south India land regions. The highest positive correlation is found along theeast coast of the Indian peninsula over Tamil Nadu and Andhra Pradesh. The study
concluded that an anomalous mid-tropospheric high to the west of the Indian region
can induce anomalous northerly to the north that does not allows propagating systems
to mover further north and west. This results in dry conditions in the northwest Indian
region and wet conditions to the southeast Indian peninsula. It suggests that an
anomalous high over Middle east in November can help increase the northeast
monsoon rainfall over the south peninsula.

50Fig. 3.11. Spatial variation of correlation coefficient between monthly mean rainfall overthe Indian landmass and area averaged 500 hPa geopotential height over 30-600E,
20-450N in November for the period 1948-2014 (After Chakraborty, 2016).

Nageswara Rao et al., (2019) made an extensive analysis of NE monsoon rainfall
using IMD gridded data set. Their analysis revealed that the seasonal rainfall has
increased over Tamil Nadu, Rayalaseema, as well as South Peninsula because of anincrease in the number of high-intensity rainfall events in the recent period with respect
to the earlier period (1901–1958), while it has decreased over the other sub-divisions.
The percentage contribution of moderate rainfall events to the seasonal rainfall is morecompared to the other events.
The study by Koteswara Rao et al., (2020) revealed there will be an increase inprecipitation in near future (about 5%). The future climate projections also indicate thatboth the intensity and frequency of precipitation extremes in most parts of the Southpeninsular India may increase under the warming scenarios during the northeast
monsoon season.

51

Chapter-4
Synoptic Systems
In the previous chapters, the salient aspects of climatological features ofatmospheric circulation and onset process have been discussed. With the onset of NE
monsoon, the east-west seasonal trough (ITCZ) slowly shifts southwards. During theseason, the mean position of the east-west trough extends from the south Arabian sea
to south Bay of Bengal across the south Peninsula. This seasonal trough, an area of large
scale convergence, can help formation of low level circulations and low pressure
systems, which lead to large scale convection and rainfall over the region.
The main synoptic systems affecting rainfall over the south Peninsula are a) Low
pressure areas b) Depressions, cyclonic storms and above c) troughs in easterlies or
easterly waves and d) upper air troughs or cyclonic circulations, confined to lower levels.
The troughs in easterlies could be observed at sea level or in lower atmosphere. The
upper air trough could be generally oriented in an east-west direction.
A summary of different synoptic systems affecting the south peninsula during the
NE monsoon season during the period 2000-2019 is given in Table 4.1. These details
were obtained from the post-monsoon seasonal reports published by the IMD in
Mausam.
Table 4.1
Number of Synoptic systems affecting South Peninsula
during the NE monsoon season (Oct-Dec)

Year
2000

Low Pressure
areas
2

Depressions
and above4

Upper airtrough/cyclonic
circulation
20

2001

4

3

8

8

2002

1

4

2

14

52

Trough in
Easterlies/
Easterly Wave
3

2003

4

4

7

11

2004

4

4

3

17

2005

7

5

4

2

2006

2

1

10

18

2007

3

3

20

6

2008

2

5

12

9

2009

3

2

10

10

2010

3

5

10

5

2011

0

5

9

9

2012

2

4

8

13

2013

1

6

8

7

2014

1

2

8

13

2015

6

4

14

7

2016

0

5

12

11

2017

3

5

29

3

2018

2

4

37

8

2019

2

6

22

5

2020

3

3

19

4

2021

4

2

28

7

Mean

2.7

3.9

13.6

8.6

Standard
Deviation

1.7

1.3

9.1

4.4

In this table, low pressure areas, depressions and cyclonic storms forming overthe south Arabian Sea and South Bay of Bengal are considered. The upper air troughs
and cyclonic circulations are counted only those forming near the South peninsula
contributing rainfall activity over the region.

53

On an average, around 3 low pressure systems form during the NE monsoon
season, which do not intensify into depressions or above. During the season, four
depressions/cyclonic storms can be expected to affect the south peninsula. On an
average, 13 upper air cyclonic circulations/troughs and 10 troughs in easterlies/easterly
waves also form affecting the south peninsula during the season. While there is little
year to year variation in cyclonic storms (Depressions and above), there is, however,large year to year variation in the frequency of tropical easterly waves and upper air
systems. It may be interesting to understand the contribution of each of these weather
systems on the seasonal rainfall distribution over the region.
A preliminary analysis was made to understand the variability of the frequency ofthese synoptic systems and its linkage to global forcings like El Nino-Southern Oscillation(ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Indian Ocean
Dipole (IOD) etc. The time series of the global indices were taken from the website
https://psl.noaa.gov/gcos_wgsp/Timeseries. The data of the period 2000-2021 (22years) have been used to calculate the correlation coefficients and the results are given
in Table 4.2. The seasonal mean of Oct-Dec period was considered for the analysis.
Table 4.2
Correlations between Global Circulation Indices and frequency of Synoptic systems
GlobalIndex

Lows

Nino 3.4
SOI
NAO
AO
AMO
PDO
IOD

0.134
-0.109-0.084-0.036-0.036-0.181-0.183

Dep/CS

Troughs /Circulations

Easterly Waves-0.2160.249-0.0790.165
0.165-0.013-0.041-0.180
0.290
0.172
0.125
0.125
-0.1460.386

0.489-0.504
0.030
0.008
0.008
0.336
0.170

54

Note: Nino3.4: ENSO Index, SOI: Southern Oscillation Index, NAO: North Atlantic
Oscillation. AO: Arctic Oscillation, AMO: Atlantic Multi-decadal Oscillation, PDO: Pacific
Decadal Oscillation, IOD: Indian Ocean Dipole.
It is interesting to note that the frequency of easterly waves over the Bay of
Bengal during the NE monsoon season has a statistically significant correlation with Nino
3.4 and SOI. This indicates, during the El Nino years, frequency of easterly waves isenhanced, which might lead to more seasonal rainfall over the south peninsula. The
relationship between the ENSO and seasonal rainfall is discussed in detail later in thechapter on the NE monsoon variability. The seasonal rainfall is positively correlated with
the Nino 3.4 index. It is also interesting to note that Pacific Decadal Oscillation (PDO)
plays a positive role in enhancing the frequency of easterly waves. The positive phase of
the Indian Ocean Dipole (IOD) leads to more troughs and upper air circulations oversouth peninsula which also contribute to the seasonal rainfall.
A brief description of various synoptic systems forming during the NE monsoon
season is given below with an example, just for illustration. It may be noted that thecharacteristics of these synoptic systems may show large variability.
4.1. Trough of low over the Southwest Bay of Bengal (30 Oct - 05 Nov 2018)
An example of a trough of low pressure that formed over the Southwest Bay of
Bengal is discussed below.
A low-level cyclonic circulation was prevailing over the southwest BOB andadjoining Sri Lanka during 27-31 Oct and trough of low at mean sea level extending fromthis circulation from southwest - west central BOB off TN / south CAP / Sri Lanka coast
during 30 Oct - 05 Nov. Fig. 4.1. shows the different aspects of the trough of low which
formed over the Southwest Bay, including satellite images and rainfall associated withthe system. The average rainfall is taken from the IMD/NCMRWF merged satellite(GPM)-raingauge dataset. The streamline pattern and mean sea level pressure chart are
taken from the archives of IMD. These plot shows a north-south trough over the55southwest Bay of Bengal and adjoining Tamil Nadu coast. This trough is observed on thesea level also. The presence of this trough had caused extensive rainfall off the coasts ofTamil Nadu and Sri Lanka (Fig. 4.1 c).

(a)

(b)
56

(C)
Fig. 4.1. a) 850 hPa streamline analysis during 26-30 Oct, 2018 b) Isobaric analysis on 31
Oct, 2018 and c) average rainfall (mm/day) during 31 Oct to 05 Nov, 2018.

4.2. Upper air east-west trough
During the NE Monsoon season, occurrence of an east-west trough across thesouth peninsula is very common. This could be associated with the presence of ITCZ
over the region. One good example of the east-west trough is shown in Fig. 4.2 a-c.
During 14-16 Nov 2021, an east-west trough was present passing across the south
peninsula. The east-west trough provides large scale convergence and associated rainfall
activity over the region, as seen in the vertical velocity (omega) shown in Fig. 4.2 b.
Associated with this east-west trough large scale rainfall activity was observed over thesouth Peninsula and the Arabian sea.

57

(a)

(b)
58

(c)
Fig. 4.2. a) 850 hPa winds on 14 Nov showing the east-west trough (shown as black line)
and b) vertical velocity (omega) (Pa/s) at 500 hPa on 14th Nov and c) the cumulative
rainfall during 14-16 Nov 2021.

4.2. Low Pressure Area (LOPAR) : 27 Oct - 04 Nov 2021
A low pressure formed over the central parts of south Bay of Bengal andassociated cyclonic circulation extending up to 5.8 km above mean sea level on 27th Oct
2021. It lay over the southwest Bay of Bengal off Sri Lanka coast and associated cyclonic
circulation extended up to 3.1 km above mean sea level on 28th; over southwest Bay of
Bengal off Sri Lanka and Tamil Nadu coast and associated cyclonic circulation extended
up to 3.1 km on 29th, 30th and 31st. It lay over the Comorin area and adjoining north Sri
Lanka coast extending up to 3.1 km on 01st Nov; over the Comorin area andneighborhood on 02nd and over Lakshadweep area and neighborhood extending up to
4.1 km above mean sea level on 03rd and extending up to 4.5 km on 04th Nov 2021.

59

Various dynamical and thermodynamical features of the LOPAR are shown in
Fig. 4.3. The Low pressure area was observed off Sri Lanka Coast with an east-west
trough of low pressure from the east Arabian sea to the east Bay of Bengal across thelow pressure system. At 925 hPa level, the east-west trough is easily seen. The OLR
spatial pattern suggests large scale convection of deep clouds with low OLR (less than
200 Wm-2) over the south peninsula and adjoining Bay of Bengal. The Chennai DWR
image as well as the Satellite image are also shown below, which suggests large scale
clouding over the region. The cumulative rainfall from 01-04 Nov is shown in Fig. 4.3 c.
The spatial pattern suggests heavy rainfall exceeding 200 mm/day off the Tamil Nadu
coast. Formation of a low pressure area with associated vertical extension over theseasonal east-west trough of low pressure is a regular synoptic system contributing toseasonal rainfall over the south Peninsula. The present day NWP models have capability
in predicting the formation of these low pressure systems over the Bay of Bengal, atleast 2-3 days in advance. Therefore, early warnings for adverse weather due to the lowpressure systems are feasible.

(a)

60

(b)

(c)

Fig. 4.3. Various dynamic and thermodynamic features of the LOPAR a) Mean Sea Level
Pressure b) 925 hPa winds c) vertical velocity (omega) at 500 hPa on 29 Oct 2021

61

(d)

(e)

(f)
Fig. 4.3. d) DWR image on 28th Oct e) Satellite Image on 29th Oct and f) Cumulative
Rainfall during 01-04 Nov 2021.

62

4.3. Cyclonic Storms
In this section, the details of activity of cyclonic storms are discussed using thelong term tropical cyclone track data. Fig 4.4 shows the frequency of Cyclonic Storms(Depressions and above) forming over each 2.5 × 2.5 degree boxes during the period
1961-2020. In each box, top figure shows the frequency of all systems together(Depressions + Cyclonic Storm + Severe Cyclonic Storm), middle one is the frequency of
CS+SCS and bottom one is the frequency of SCS alone. These statistics are derived fromthe IMD Cyclone atlas (http://www.imdchennai.gov.in/). Frequency of cyclonic storms
is maximum over the west central Bay of Bengal (100-17.50N, 800-900E). Therefore,
during the NE monsoon season, north Tamil Nadu and Coastal Andhra Pradesh are mostvulnerable to cyclonic storms.
The tracks of cyclonic storms (depressions and above) during the months October
to December months are shown in Fig. 4.5, 4.6 and 4.7 respectively. In October, northTamil Nadu, Coastal Andhra Pradesh, Odisha and West Bengal experience the landfall ofthese storms. Some storms after moving northwestwards recurve towards northeast,
due to the influence of sub-tropical high. Over south Peninsula, north Tamil Nadu andcoastal Andhra Pradesh experience Tropical cyclonic storms.
In November, most of the storms forming over the Bay of Bengal movenorthwestwards and make landfall over Tamil Nadu and the coastal Andhra Pradesh. In
November, a few storms recurve and make landfall over West Bengal and Bangladesh.
In December, cyclonic storm activity is generally reduced. In December also, whenstorms form, they move northwestwards and make landfall over Tamil Nadu. A few
storms recurve and make landfall over coastal Andhra Pradesh and Bangladesh. There
are a few storms, which form over the Bay of Bengal, cross the south peninsula andemerge in the Arabian Sea, thus making longer lifetime. The systems forming over the
Arabian Sea, either move westwards towards the Arabian sub-continent or towards
Gujarat/Pakistan. The storms forming over the Arabian sea generally do not make63

landfall over the south peninsula. They tend move northwestwards. A few storms
however move north and affect the Gujarat coasts.

Fig. 4.4. Frequency of all Cyclonic disturbances (Depressions and above) during theperiod 1961-2020. In each box top figure shows the frequency of Dep+CS+SCS, middle
one CS+SCS and bottom one is the frequency of SCS. These statistics is derived from IMD
Cyclone atlas.

64

Fig. 4.5. Tracks of cyclonic storms (Depressions and above) during October for theperiod 1961-2020 (Above) and tracks of cyclonic storms (depressions and above) during
October, but which affected the south peninsular region. These tracks are derived fromIMD Cyclone Atlas.

65

Fig. 4.6. Same as Fig. 4.5, but for November.

66

Fig. 4.7. Same as Fig 4.5, but for December.

67

Figs. 4.8 a, b and c show the return periods (year) of cyclonic storms (CS)/ Severe
Cyclonic Storms (SCS) passing within 50 nm of the coastal districts during October,
November and December respectively. The period 1961-2020 was considered for thisanalysis. The maps are derived from the IMD Climate Hazards and Vulnerability Atlas ofIndia, 2022. The same maps for cyclonic storms (CS) and Severe cyclonic storms (SCS)
separately are available in the IMD Climate Hazards and Vulnerability Atlas of India(2022). In October, the districts in coastal Andhra Pradesh and north Tamil Nadu haveshort return periods of 7.5 to 9.0 years. In November, a few coastal districts in south
coastal Andhra Pradesh and north Tamil Nadu have much shorter return period of 3.75.0 days. Some more neighhouring districts have return periods of 5.1-10.0 years. In
December coastal districts of Tamil Nadu show return periods of 7.5-9.0 years.

Fig. 4.8 a. Return period (in years) for Cyclonic Storm (CS)/ Severe Cyclonic Storm
passing within 50 nautical miles of coastal Districts during October for the period 19612020. (Source: IMD Climate Hazards and Vulnerability Atlas of India, 2022).
68

Fig. 4.8 b. Same as Fig 4.5 a, but for November.

Fig. 4.8 c. Same as Fig 4.5 a, but for December.

69

4.4. Track and intensity forecast errors of Tropical Cyclones
In this section, an analysis is presented on the Track and Intensity forecast errorsof Tropical Cyclones over the North Indian Ocean during the NE monsoon (Oct-Dec)
season. Data of the period 2012-2021 have been used to complete this analysis.
Mohapatra et al. (2013) and Mohapatra et al. (2015) have carried out extensive analysisof tropical cyclone track forecast errors over the North Indian Ocean.
The comparative analysis of track and intensity forecast errors for the postmonsoon season (October-December) during 2017-21 and 2012-16 are presented in Fig.
4.9 a and b respectively. The track forecast errors were 82 km, 107 km, 139 km & 272
km during the period 2017-21 for the 24, 48, 72 & 120 hours lead forecasts respectively.
However, during the period 2012-2016, the corresponding errors were 88 km, 133 km,
185 km & 348 km. It indicates an improvement of 10% in track forecast errors for lead
period up to 24 hours and 15-20% for lead period beyond that up to 120 hours (Fig. 4.9a) during the recent years.
The intensity forecast errors were 8.1 knots, 11.7 knots, 14.2 knots & 15.8 knots
during 2017-21 for 24, 48, 72 & 120 hours lead period respectively. The corresponding
errors during the period 2012-2016 were 11.9 knots, 16.9 knots, 17.2 knots & 11.6 knots
respectively. The intensity of a tropical cyclone is measured based on its maximum windwhich is normally quoted in knots. This indicates an improvement of 30% in intensity
forecast errors for lead period up to 48 hours and 15-25% for lead period of 60 to 84
hours (Fig. 4.9 b).
To carry out the analysis of landfall errors, only landfalling cyclones have been
considered. There were 9 cyclones in each period making landfall over different places
within the region during the NE monsoon season. The comparative analysis of landfall
point forecast errors for the NE monsoon season during 2017-21 and 2012-16 arepresented in Fig. 4.9 c. The landfall point forecast errors were 41.9 km, 59.3 km & 51.2
km during 2017-21 against 31.6 km, 85.3 km & 82.7 km during 2012-2016 for 24, 48 &70

72 hours lead period respectively. The comparative analysis indicates that there is asignificant improvement of 30-40% in landfall point forecast errors for lead period of 4896 hours. However, there is no significant change in landfall point forecast errors up to alead period of 36 hours during the two periods. It is needless to mention here that thediameter of the central region (eye) of the TC is about 10-100 km, and an average about
50 km. No significant change in the forecast errors up to 36 hours which vary fromabout 15-60 km can be attributed to the above fact. Further within 36 hours, thelandfalling TCs are monitored with the help of the coastal hourly observations as well as
Doppler weather radar observations, leading to lower detection error. It is very
interesting to note that the landfall forecast errors during the recent epoch are muchless than the previous epoch. This could be mainly attributed to the excellent modelling
strategy used by the IMD; availability of global models with higher resolution withplenty of assimilated data. The global models used for predicting the track and thelandfall point have the spatial resolution of about 12-25 km. Hence, there is scope toreduce the landfall point errors for higher lead periods like 48-120 hours withimprovement in model resolution, data assimilation supported by augmented direct &remotely sensed observational system.
Cangialosi et al. (2020) have discussed the Hurricane forecast errors over the
Atlantic Ocean since 1960s to 2019. It has been well documented that the National
Hurricane Center (NHC) has made significant improvements in Atlantic basin tropical
cyclone (TC) track forecasting during the past half century. In contrast, NHC’s TC
intensity forecast errors changed little from the 1970s to the early 2000s. Recently,
however, there has been a notable decrease in TC intensity forecast error and anincrease in intensity forecast skill. The advancement of NWP models, especially as
supported in the past decade, creation of the consensus aids, development of rapid
intensification guidance, and the ability of the NHC forecasters to add value to the TC
guidance has greatly improved intensity forecast skill over the Atlantic Ocean.

71The study by Cangialosi et al. (2020) revealed that the track forecast errors over
the Atlantic Ocean during the period 2010-2019 are 83 km, 110 km, 185 km and 213 km
respectively for 24 hr, 48 hr, 72 hr and 120 hr forecasts. Similarly for Hurricane Intensity
forecasts the errors are 8 knots, 12 knots, 14 knots and 15 knots respectively for 24 hr,
48 hr, 72 hr and 120 hr forecasts during the same period. This suggests, the tropical
cyclone forecast intensity errors over the North Indian Ocean are larger compared tothe intensity forecasts errors for the Atlantic Ocean. Therefore, more efforts should bemade to reduce track and intensity forecast errors of tropical cyclones over the North
Indian Ocean.

(a)

(b)

72

(c)

Fig. 4.9. Comparative operational (a) track and (b) intensity and (c) Landfall forecast in
the post monsoon season during 2017-21 against 2012-16.

4.5. A case study of Tropical Cyclone Ockhi
In this section, the details of the tropical cyclone (TC-OCKHI) are discussed tobring out various forecasting issues and challenges related to the cyclone during the NE
monsoon season. The TC- Ockhi was responsible for deaths of over 350 people fromsouthern Tamil Nadu and Kerala, between 30 Nov and 3 Dec 2017. There were also
some unidentified fishers from the north-eastern states of India who were lost at sea
while working on-board fishing vessels. The full force of the storm was borne byfishermen at sea, unlike the previous cyclones over the Arabian sea (Manas Roshan2019).
The TC OCKHI formed as a low pressure area over southwest Bay of Bengal (BOB)
and adjoining areas of south Sri Lanka on 28th Nov /0300 UTC and became well marked
on 29th /0000 UTC over the same region. The vertical wind shear of horizontal wind wasmoderate to high (15-30 knots) over the southwest Bay of Bengal and adjoining Sri
Lanka coast. Under the favourable environmental conditions, it concentrated into a
Depression (D) over southwest BOB off southeast Sri Lanka coast on 29 th / 0300 UTC.
73

Moving westwards, it crossed Sri Lanka and emerged into Comorin area by 29 th /1200
UTC. It intensified into a Deep Depression (DD) on 29th/2100 UTC. Moving
northwestwards it intensified into a Cyclonic Storm (CS) on 30th / 0300 UTC over the
Comorin area, into a Severe Cyclonic Storm (SCS) over Lakshadweep area on 01st
December / 0000 UTC and further into a Very Severe Cyclonic Storm over southeast
Arabian Sea (AS) to the west of Lakshadweep by 01st / 0900 UTC.
Generally, for the North Indian Ocean basin, TCs are considered to haveundergone rapid intensification (RI)/ rapid weakening (RW) whenever 30 knots
increase/decrease is noted in maximum sustained surface wind speed (MWS; Vmax) in
24 hrs. It is interesting to note that OCKHI underwent RI between 01 st /0000 UTC to 02nd
0000 UTC and attained its peak intensity of 150-160 kmph gusting to 180 kmph on 2nd/0900 UTC with lowest central pressure of 976 hPa over the Arabian Sea. It thengradually recurved north-northeastwards, maintained its VSCS intensity till 04th / 1200
UTC and then weakened gradually. It crossed the south Gujarat coast as a well-marked
low on 06th Dec/0000 UTC (IMD, 2018). More details of its genesis and tracks areavailable in Geetha and Balachandran (2020) and the IMD (2018) report on TC-Ockhi(https://rsmcnewdelhi.imd.gov.in/uploads/report/26/26_83ec45_ockhi%20pre.pdf).
The system caused extensive damages over extreme south Tamil Nadu and south
Kerala during its developmental stages on 29-30 Nov. The system centre was about 60
km from Kanyakumari, the southern-most tip of peninsular India on 30th Nov/ 0300 UTC
when it intensified from DD to CS stage (as per IMD’s best track data). Even though thecentre of the system did not cross the coast and move inland, Kanyakumari and
Thiruvananthapuram (Kerala) bore the brunt of the fury of the eye-wall region of the TC
during 29th night-30th Nov morning. The IMD’s best track of TC-OCKHi is shown in
Fig. 4.10 and spatial pattern of mean sea level pressure on 29th Nov and 30th Nov isshown in Fig. 4.11. These charts show the movement of TC-OCKHI and quick
intensification by 30th Nov near the Comorin area.

74

Fig. 4.10. Observed track of very severe cyclonic storm OCKHI, 29 Nov to 06 Dec 2017.
Source: India Meteorological Department (IMD).

(a)
75

(b)

(C )
Fig. 4. 11. Mean Sea Level Pressure Chart on a) 29 Nov 2017, 03 UTC b) 29 Nov 2017 12
UTC and c) 30 Nov 2017 03 UTC. Source: India Meteorological Department.

76

Fig. 4.12. 24 hour change in MSLP (on left) and MSLP departure from normal (right) ona) 29th Nov 2017 12 UTC and b) 30th Nov 2017 03 UTC. Source: IMD Meteorological
Department.

77

Fig. 4.12 above shows the Mean Sea Level Pressure (MSLP) changes in 24 hourand MSLP departures from normal on 29th and 30th Nov 2021. These charts show rapid
fall of mean sea level pressure by 30th Nov and large-scale negative departure (of theorder of 8 hPa) off the Kerala coast.
Fig. 4.13 shows low level convergence and 850 hPa vorticity patterns at 03 and 12
UTC on 29th Nov, when the system intensified into a cyclonic storm. The low-level
convergence pattern shows a rapid increase in the low-level convergence over theregion associated with the weather system. The INSAT satellite pictures of TC-Ockhi areshown in Fig. 4.14. These images are provided by the Satellite Directorate of IMD, whichshows the westward movement and then north-northeasterly recurvature towards the
Gujarat coast.

Fig. 4.13. Low-level convergence (10-5/s) (left) and 850 hPa vorticity (10-5/s) (right) at 03
and 12 UTC on 29th Nov 2017. Source: IMD

78a) 29 Nov 0300 UTCb) 30 Nov 0300 UTCc) 01 Dec 0300 UTCd) 02 Dec 0300 UTC

79e) 03 Dec 0300 UTCf) 04 Dec 0300 UTC

Fig. 4.14. INSAT IR image at 0300 UTC showing TC-Ockhi during 29 Nov- 04 Dec 2017.
Source: IMD Satellite Directorate.

Fig. 4.15 shows the spatial distribution of 10 m wind (in knots) from 29 Nov to 4th
Dec showing the intensification and weakening of surface winds associated with TCOckhi. These plots are prepared using the ERA5 reanalysis data with 0.25 degree
resolution.
It caused heavy to very heavy rainfall over Lakshadweep on 01st and 2nd Dec.
There was heavy rainfall over north coastal Maharashtra and adjoining south coastal
Gujarat on 5th Dec. Thiruvananthapuram recorded 62 kmph wind in gustiness at 1300
IST of 30th Nov. Storm surge of height 1m over Lakshadweep Islands was observed on
30th Nov.

80

81

Fig. 4.15. Spatial distribution of 10 m wind in knots from 29 Nov to 4 Dec 2017. Source:
ERA reanalysis data
82

The salient features of TC-Ockhi are given below (IMD, 2018):
 This was the only fourth cyclonic storm developing over the Comorin Sea (southof Kerala and Tamil Nadu and west of Sri Lanka) during the past 120 years.
Previously two cyclones (19-22 Nov and 17-19 Dec) in 1912 and another in 1925
(06-10 Nov) developed over the Comorin Area. All these cyclones affected south
Kerala and south Tamil Nadu.
 TC-Ockhi was thus a rare cyclone with rapid intensification in genesis stage (fromdepression to cyclonic storm within 24 hours). The system intensified rapidly
from 1800 UTC of 30th to 0000 UTC of 2nd Dec (increase in wind speed by 30 kts in
24 hours) (Fig. 4.16).
 The total track length of the cyclone was 2538 km.
 The 12 hourly average translational speed of the cyclone was 15.0 kmph.
However, it moved faster in the genesis stage (29/0830 IST to 30/0830 IST) with
12 hourly average translational speed of 19 kmph.
 The life period of cyclone was 6 days & 18 hours against long period average of
4.7 days for very severe cyclonic storm over north Indian Ocean.
 The peak maximum sustained surface wind speed (MSW) of the cyclone was 150160 kmph gusting to 175 kmph (85 knots) during 0600 UTC of 2 nd to 0000 UTC of
3rd December (Fig. 4.17)
 The lowest estimated central pressure was 976 hPa (from 0300 UTC of 2 nd to0000 UTC of 3rd December) with a pressure drop of 34 hPa (Fig. 4.17).
 The intensification/weakening of TC-Ockhi was largely governed by the Ocean
heat content. However, the rapid weakening on 4th and 5th Dec was facilitated bydry and cold air intrusion and high vertical wind shear under the influence of atrough in mid latitude westerlies.

83Fig. 4.16. 24-hour change in intensity (knots) ending on different date and time. Rapid
intensification and rapid weakening are indicated. Source: IMD 2018 report.

Fig. 4.17. a) Maximum sustained surface wind (kts) and estimated centre pressure (hPa)and b) Maximum sustained surface wind (kts) and pressure drop at centre (hPa). Source:
IMD 2018 report.

Genesis and intensification of tropical cyclones are closely related to oceanic
conditions like sea surface temperatures (SST) and ocean heat content. Fig. 4.18 shows
the spatial distribution of SST during 23-28 Nov, just prior to the formation of TC-Ockhi.
84

This plot clearly shows sea surface temperatures were more than 28.50C over thesouthwest Bay of Bengal (where the TC-Ockhi initially formed), which is much more thanthe SST threshold for formation of tropical cyclones. Over southeast Arabian sea, SSTs
were even more than 29.00C. Many previous studies (for example, Jangir et al., 2021,
Sanap et al., 2020) have documented the relationship between the ocean heat content
and intensification of tropical cyclones. Fig. 4.19 shows the Tropical cyclone heatpotential (TCHP), which clearly suggests the presence of large ocean heat content oversouthwest Bay of Bengal (Comorin area) and southeast Arabian sea (over the observed
track of TC-Ockhi), TCHP was more than 70 KJ/cm2. This large amount of heat potential
was mainly responsible for the rapid intensification of TC-Ockhi.

Fig. 4.18. Spatial distribution of Sea Surface Temperatures (SST) in 0C averaged during
23-28 November 2017, just before the TC-OCKHI formed over southwest Bay of Bengal.
Source: UK Met office, HADISST.

85

Fig. 4.19. Spatial distribution of Tropical Cyclone Heat Potential (TCHP) in KJ/Cm 2 on
25 November 2017, just prior to the formation of TC-OCKHI. Source: NRSC, ISRO.

Singh et al., (2020) studied the role of eastward moving Madden and Julian
Oscillation (MJO) in the genesis of TC-Ockhi. Mohapatra and Adhikary (2011) alsostudied the role of MJO on tropical cyclone genesis over the Indian Ocean region. MJO isan eastward-moving wave in the tropical belt associated with enhanced convective
activity in the regions from which it passes. Previous studies have shown that MJO inphase 4 is conducive for cyclogenesis in the south of Bay of Bengal by ensuing increased
vorticity and anomalous cyclonic circulation on the westward side of MJO-induced
convective activity. As the MJO propagated from phase 3 to phase 4 in November, 2017,it resulted in anomalous westerlies over the entire south Bay of Bengal (near theequator) in response to the shift in the convection over the maritime continent The
anomalous westerlies along with anomalous easterlies over a narrow zone centred near
10°N, caused a strong shear zone and positive vorticity (Singh et al., 2020).

86

Jyothi et al., (2020) investigated the oceanic and atmospheric processes that havecontributed to the Rapid Intensification (RI) and Rapid Weakening (RW) of Cyclone Ockhi
using the HYbrid Coordinate Ocean Model (HYCOM) simulations and Global Forecast
system (GFS) outputs. The environmental conditions prevailed before RI showed thepresence of thick warm and fresh waters, ample supply of mid-tropospheric relative
humidity, and moderate wind shear. The intrusion of dry air, strong vertical wind shear,and unfavourable oceanic conditions annihilated the storm intensity during the RW
stage. Compared to the ocean temperatures, the vertical structure of salinity showedremarkable differences between the RI and RW locations resulting in contrasting upperocean stratification.
NWP Forecast Guidance for TC-Ockhi
India Meteorological Department (IMD) refers to many forecast products for
preparing warnings for tropical cyclones over the north Indian Ocean. In this case of TCOckhi also, IMD had referred to many NWP products from IMD GFS, NCUM, ECMWF and
JMA models. An analysis of inferences drawn from these models suggests that none of
the models could provide an early forecast guidance for the genesis of TC-Ockhi as a
Low/depression and its further intensification. The first model forecast guidance wasavailable from the ECMWF model based on 28 Nov 0000 UTC in which the model
indicated formation of a depression and its intensification into a severe cyclonic storm
over the Arabian sea. The other models started indicating the formation of this system
and its intensification from 29th Nov only. It is very important to note that none of themodels correctly indicated the rapid intensification of TC-Ockhi during the course of its
travel around the Comorin area.
However, the track forecasts by some of the models in predicting track of TCOckhi have been reasonably accurate as shown in Table-4.3 below. Numbers given in
the bracket is number of verified forecasts. It can be seen that the Multi-Model
Ensemble (MME) of IMD has the lowest errors compared to other models up to 48

87

hours lead time. Beyond 48 hours, ECMWF performed better than IMD-MME. The
forecast errors of IMD-HWRF beyond 24 hours was far inferior compared to othermodels. The observed track of TC-Ockhi and IMD-MME predictions are given in Fig 4.20
and that of NCUM model are given in Fig 4.21. The models could indicate therecurvature and weakening of the TC-Ockhi very well.
In summary, the NWP models did not provide enough lead time for the IMD
about its genesis over the Comorin area and its likely intensification into a cyclonicstorm. IMD could get only 36 hours lead time to inform Kerala and Tamil Nadu
government officials about its genesis and likely intensification. However, once the TCOckhi started moving, the forecast track errors were found reasonable and comparablewith the climatological forecast errors. Despite considerable improvements in theprediction of genesis, intensity and track of cyclones in the north Indian Ocean in the
recent decade, most of the models failed to capture the genesis of cyclone Ockhi inadvance and its rapid intensification.

Fig. 4.20. Observed and Predicted track of TC-Ockhi by the IMD-MME prediction system.

88

Fig. 4.21. Observed and Predicted track of TC-Ockhi by the NCUM prediction system.

A detailed study is required to understand why the NWP models could notprovide adequate lead time in predicting the genesis and the further intensification of
TC-Ockhi. One of the reasons could be that its genesis was very close to the equator.
The models might have had problems in predicting its genesis near the equator. It may
be mentioned that the models referred by IMD for forecast guidance are atmosphericmodels and do not treat the oceanic conditions and ocean-atmosphere coupling
explicitly. This leads to an inference that even for short to medium range forecasting of
weather systems like Tropical Cyclones, a coupled modelling strategy may be required.
This may be considered as a special case in which the NWP models had problems
in predicting its genesis and rapid intensification. Otherwise, the recent NWP models are
known for their accurate prediction of tropical cyclones.

89

Table 4.3
TC-OCKHI Average Track Forecast Errors in km by different models(Source: IMD report)
Lead time

12 hrs

24 hrs36 hrs48 hrs

60 hrs72 hrsIMD-GFS

51(11)

68(10)

98(9)117(8)

123(7)

145(6)

IMD-WRF59(11)

115(10)166(9)

226(8)

271(7)

278(6)

JMA

70(11)

70(10)

85(9)103(8)

148(7)

215(6)

NCEP-GFS

60(11)

65(10)

75(9)110(8)

91(7)

104(6)

UKMO

42(11)

63(10)

76(9)122(8)

146(7)

173(6)

ECMWF

58(11)

69(10)

64(9)

62(8)

86(7)

100(6)

IMD-

58(21)

99(20)

130(18)180(16)234(14)251(12)

IMD/MME29(11)

44(10)

58(9)

86(8)

100(7)

112(6)NCUM

82(13)

122(12)129(11)121(10)144(9)

180(8)

NEPS

90(7)

134(6)

178(6)

217(5)

211(5)

208(4)

HWRF

4.7. Easterly Waves/Troughs in Easterlies
From the Table 4.1, it is observed that easterly waves/trough in easterlies form asubstantial portion of synoptic systems affecting south peninsula during the NE
monsoon season. However, there are not adequate studies on easterly waves forming
over the Indian Ocean.
Easterly wave is a wave within the broad easterly current and moves from east to
west, generally more slowly than the current in which it is embedded. Although best
described in terms of its wave like characteristics in the wind field, it also consists of a
weak trough of low pressure. Easterly waves do not extend across the equatorial trough.
To the west of the trough line in an easterly wave, there is generally found divergence, ashallow moist layer, and exceptionally fine weather. The moist layer rises rapidly near90the trough line; in and to the east of the trough line intense convergence, cloudinessand heavy rain showers prevail. Easterly waves occasionally intensify into tropical
cyclones over the Bay of Bengal.
These waves are first identified in the Carribean (Dunn, 1940) and subsequently
studied in detail over the African region using synoptic / satellite observations andnumerical models. Considerable work on the passage of easterly waves over the African
region, their structure, movement and roles in genesis of Atlantic hurricanes have been
carried out and reports available in the literature (Burpee (1974), Jury et al., (1991),
Berry and Thorncroft (2005) and Ross and Krishnamurti (2007)). Similar studies havealso been undertaken for other oceanic regions such as Eastern and Western Pacific (Taiand Ogura, 1987). For the Indian region, Saha et al., (1981) have analysed 24 hour sea
level pressure change charts of July-August of three stations for the 10 year period of
1969-1978 and have identified passage of westward propagating disturbances aspredecessors to formation of monsoon lows and depressions. Using cross-correlationand power spectral analysis, they have determined the period of the wave as about 5
days, speed of about 6 ms-1 and wavelength of 2300 km. There are not adequate studies
on Easterly waves forming over the Indian Ocean. Balachandran et al., (1998) havereported some features of an inverted V-type easterly wave over the Indian seas during
December 1995 and have determined the speed of the wave as 8.2 knots using satellite
imageries.
Conceptually, northerly meridional winds, subsidence, divergence and fairweather are the general atmospheric characteristics ahead of an approaching easterly
wave trough and southerly meridional winds, rising motion, convergence and active
weather are the characteristics behind the wave trough. The southerly meridional winds
approaching the wave trough encounter an upgradient motion (moving from a low inthe south towards a high in the north) and hence slow down leading to velocity
convergence. The northerly winds approaching the wave ridge face a downgradient91

motion as a result of which the wind speeds increase leading to velocity divergence.
Thus, the winds approaching the wave trough are sub-geostrophic and the winds
approaching the wave ridge are super-geostrophic (Hess, 1959). The pictorial
representation of an easterly wave, taken from Riehl (1968, Tropical Meteorology) is
given in Fig. 4.22.

Fig. 4.22. Pictorial Representation of an Easterly Wave observed over the north Atlantic.
After Riehl (Tropical Meteorology).

There are a few studies on examining the role of Easterly Waves for enhancing
rainfall over south Peninsula during the NE Monsoon season. Reba et al. (2022)
examined the years 2015, 2016 and 2017 to understand the variability of synoptic
conditions including the propagation of easterly waves (EWs). Further, it is also seenthat many active rainfall spells during 2015 and 2017 over Tamil Nadu met-subdivisionwere associated with passage of EWs over the southern peninsula along east of 850E,
whereas, the large deficient NE monsoon of 2016 witnessed no such active spell of92

monsoon and the passage of EW was confined to the eastern part of Bay of Bengal andnever entered into the Indian peninsula. Geetha and Balachandran (2014) studied theeasterly wave characteristics during 2010 NE monsoon season by means of synergetic
analysis using synoptic, statistical and numerical methods. The easterly waves during
2010 had time periods of about 4.5 days. The speed of movement, wavelength andamplitude of EWs were 7.28 ms-1, 2800 m, and 6.7 ms-1 respectively. Sanap et al. (2018)
examined the 2015 NE Monsoon season in respect of easterly wave activities. Theirresults indicated that EW activity over the Indian Ocean plays a seminal role inoccurrence of heavy rainfall events during the positive phase of the ENSO (El-Niño),
while it is found to be weak during negative (La-Niña) and neutral phase.
Here, we now discuss on a case study of formation of an easterly wave and its
westward movement across south peninsula during 13-18 November, 2010. As shown in
Fig. 4.23, there was widespread rainfall activity over south peninsula and adjoining Bay
of Bengal associated with the Easterly wave. It is interesting to note that a wave typestructure is observed in precipitation pattern during this period. Fig. 4.24 shows thevector winds at 700 hPa during the period 13-18 November, 2010 and vorticity at 700
hPa. Positive vorticity suggesting large scale ascending motion can be noticed on theeastern side of the trough line of the easterly wave. The easterly wave which formed
over southeast Bay of Bengal moved westwards and crossed south peninsula by 17
November and emerged into the Arabian sea on 18th November. This weather systemcaused widespread rainfall activity over the region. The relevant satellite pictures of thiseasterly wave are shown in Fig. 4.25.
Fig. 4.26 shows the Longitude-Time cross section of a) Meridional wind at
700 hPa b) Outgoing Longwave radiation c) Precipitable water content andd) Precipitation rate at 100N during the period 13-18 November 2010, showing different
aspects of an easterly wave, which travelled from east to west during 10-20 November,2010 and contributed to large scale rainfall activity over south peninsula. These plots93very clearly show the westward movement of easterly wave representing meridional
winds at 700 hPa, OLR, precipitation rate and vertical velocity, omega. The zero-line
separating southerlies and northerlies is shown in Fig. 4.26. It is interesting to note thatlower values of OLR, large precipitation rate and large ascending motion (negative
omega) are observed east of the zero line. This suggests that in and to the east of thetrough line intense convergence, cloudiness and heavy rain showers prevail.

Fig. 4.23. Precipitation rate (mm/day) averaged during 13-18 November 2010 associatedwith the Easterly Wave. Source: NCEP/NCAR reanalysis.

94

95

Fig. 4.24. Spatial distribution of 700 hPa winds and vorticity from 13 -18 November,
2010, showing westward movement of an easterly wave, which caused widespreadrainfall activity over south Peninsula.

96

(a)

(b)

(c )

(d)

Fig. 4.25. Kalpana IR Satellite Pictures showing the presence of easterly wave over the
Bay of Bengal. a) 0300 UTC 15 November b) 1200 UTC 15 November, C) 1200 UTC 16
November and d) 1500 UTC 16 November. Source: IMD Satellite Directorate.

97

(a)

(b)

(c)

(d)

Fig. 4.26. Longitude-Time cross section of a) Meridional wind at 700 hPa b) Outgoing
Longwave Radiation (OLR) c) precipitation rate and d) vertical velocity, Omega averaged
between 5-150N during the period 10-20 November 2010

98

Chapter-5
Onset and Withdrawal
NE monsoon advances into the south Peninsula in October/November after theSouthwest monsoon completely withdraws from the country. India Meteorological
Department (IMD) made the following criteria in August 1988 for declaring NE monsoon
onset, which was further amended in August 2006 (IMD, 2008).
The criteria are:
1) Withdrawal of Southwest monsoon up to Latitude 150N
2) Onset of persistent surface easterlies over Tamil Nadu Coast3) Depth of easterlies up to 850 hPa over Tamil Nadu Coast
4) Fairly widespread rainfall over coastal Tamil Nadu, South Coastal Andhra Pradesh
and adjoining areas.
The withdrawal criteria of SW monsoon up to 150N becomes a constraint in someyears. If due to operational constraints, the SW monsoon is not withdrawn, NEM could
not be declared on real time basis even if conditions 2 to 4 above are satisfied (Raj,2012).
IMD started announcing withdrawal dates of NEM only from the year 1993 andprior to that no such declaration appears to have been made. IMD has declared
cessation of NEM rainfall by considering rainfall and may be a few other parameterssuch as depth of moisture, temperature etc. in a subjective way but no objective criteria
or guidelines to determine NEM have so far been published (Raj 2012).
The IMD dates of NE monsoon onset and withdrawal are given in Table-5.1. The
climatological NE monsoon onset date is 22 October with a standard deviation of about
7 days. The earliest onset date from 1977-2020 is 5 October in 1984. The most delayed
onset date is 02 November which occurred in 1988, 1992 and 2000. The climatologicalwithdrawal date is 04 January with a standard deviation of about 11 days. The earliest
withdrawal date was 14 December 1994, and the most delayed withdrawal is 19 January992021. The mean duration of the NE monsoon season is about 75 days. The smallest
duration of 51 days occurred in 1992 and the highest duration of 98 days occurred in
2005. However, there is no statistical correlation of NE monsoon seasonal rainfall witheither monsoon onset date, withdrawal date or duration. The highest correlation is
between the monsoon onset and NE monsoon seasonal rainfall, which is -0.305,
statistically significant only at the 90% significance level.
Raj (1992, 2003) and Geetha and Raj (2011) extensively studied the onset andwithdrawal of NE monsoon. Geetha and Raj (2011) re-determined the onset andwithdrawal dates of NE monsoon for the 100- years of 1901-2000 based on the daily
rainfall data of 25 rain-gauge stations. Stations of south Coastal Andhra Pradesh andcoastal Tamil Nadu which were located within a distance of 100 Kms from the coast
were considered. The comparison of the dates thus identified are compared with the
IMD dates. There is a negligible difference in respect of onset dates. However, thedifference in respect of withdrawal dates is quite substantial over several years. The
onset and withdrawal dates determined by Geetha and Raj (2011) and Raj (2012) arealso given in Table-5 for comparison with the IMD onset and withdrawal dates. As per
the data of Raj (2012), the mean onset date is 20 October with a standard deviation of
about 8 days and mean withdrawal date is 29 December with a standard deviation of
about 14 days. Mean duration of the NE monsoon season is about 70 days.
We follow the onset and withdrawal dates as determined by IMD for further
analysis and discussions. In the following sections, more analysis with respect to NE
monsoon onset and withdrawal dates is discussed.

100

Table 5.1
NE monsoon onset and withdrawal dates as determined by
IMD and Raj (2012)
Withdrawal
Date
(IMD)

Withdrawal
Date(Raj, 2012)Year
1977-78

Onset Date (IMD)
14-OctOnset Date
(Raj, 2012)
10-Oct1978-79

23-Oct21-Oct1979-80

22-Oct22-Oct1980-81

11-Oct10-Oct1981-82

23-Oct23-Oct1982-83

19-Oct18-Oct1983-84

24-Oct24-Oct1984-85

05-Oct03-Nov

1985-86

25-Oct25-Oct1986-87

27-Oct26-Oct1987-88

30-Oct20-Oct1988-8902-Nov

03-Nov

1989-90

27-Oct29-Oct1990-91

19-Oct18-Oct10-Jan27-Dec1991-92

20-Oct19-Oct24-Dec23-Dec

1992-9302-Nov

02-Nov

22-Dec09-Dec

1993-94

20-Oct13-Oct11-Jan01-Jan

1994-95

18-Oct18-Oct14-Dec24-Dec

1995-96

23-Oct23-Oct22-Dec13-Dec1996-97

11-Oct10-Oct23-Dec19-Dec1997-98

13-Oct13-Oct31-Dec23-Dec101

1998-99

28-Oct28-Oct22-Dec04-Jan1999-00

21-Oct04-Oct28-Dec12-Jan2000-0102-Nov

05-Nov

06-Jan02-Jan2001-02

16-Oct11-Jan

2002-0325-Oct27-Dec2003-04

19-Oct08-Jan2004-05

18-Oct25-Jan2005-06

12-Oct18-Jan2006-07

19-Oct28-Dec2007-08

22-Oct10-Jan2008-09

15-Oct31-Dec2009-1029-Oct18-Jan2010-1129-Oct17-Jan2011-1224-Oct10-Jan2012-13

19-Oct11-Jan2013-14

21-Oct18-Jan2014-15

18-Oct04-Jan2015-16

28-Oct07-Jan2016-1730-Oct04-Jan2017-18

27-Oct15-Jan2018-1901-Nov

02-Jan2019-20

16-Oct10-Jan2020-21

28-Oct19-JanFig. 5.1 shows the super-epoch analysis of rainfall with respect to IMD NE
monsoon onset date. Super epoch analysis is a composite analysis (averaging over many
years) in which the origin is taken as the monsoon onset date and 50 days before and102

after the onset date are considered. Rainfall data in the box 79-810 E, 10-140 N havebeen used for this analysis for the period 1977-2020. The analysis shows a very sharp
rise of rainfall on the monsoon onset date over the coastal areas of Tamil Nadu andcoastal Andhra Pradesh. Before the onset date up to 50 days, the mean daily rainfall
over this region is around 6 mm/day. Just after the monsoon onset, mean rainfall overthis region sharply increases to around 14 mm/day, which persists almost for two
weeks. We can observe strong intra-seasonal activity even in this super-epoch analysis.
For example, after about 20 days after the onset rainfall activity is reduced and it picks
up again around 40 days after the monsoon onset.
Fig. 5.2 shows super epoch analysis of rainfall with respect to IMD NE monsoon
withdrawal date. Rainfall data in the box 79-810 E, 10-140 N have been used for thisanalysis for the period 1977-2020. This super epoch analysis shows a reduction (not very
sharply as we see with respect to monsoon onset) of rainfall activity just after the
monsoon withdrawal. Raj (2012) suggested that the monsoon withdrawal date decided
by IMD is done subjectively and no official objective definition exists. However, there isa sharp decline in rainfall activity associated with the withdrawal date of Raj (2012), as
these dates are determined in hindcast mode using only rainfall criteria.

103Fig. 5.1. Super epoch analysis of rainfall with respect to IMD NE monsoon onset date.
Rainfall data in the box 79.5-80.50 E, 10-140 N have been used for this analysis. Period ofanalysis: 1977-2020.

Fig. 5.2. Super epoch analysis of rainfall with respect to IMD NE monsoon withdrawal
date. Rainfall data in the box 79.5-80.50 E, 10-140 N have been used for this analysis.
Period of analysis: 1977-2020.
1045.1. Composite meteorological features associated with the monsoon onset
To understand the changes in dynamical and thermodynamical parameters
occurring associated with the NE monsoon onset, a composite analysis was made with
Mean Sea Level Pressure (MSLP), winds at 1000, 850 and 700 hPa level, Outgoing
Longwave Radiation (OLR), and precipitable water content. The analysis was made as apentad (5 days) composite with the onset date as the central date. The other dates in
the pentad are two days before the onset and two days after the onset date. Suchcomposites will provide useful information on the dynamical and thermodynamicalchanges occurring in the region associated with the monsoon onset. Also, they will
provide clues for some important parameters to be considered for developing criteria todeclare the monsoon onset.
Fig. 5.3 shows the composite MSLP pattern associated with the NE monsoon
onset. It clearly shows the presence of a low-pressure area over the southwest Bay of
Bengal, off the Tamil Nadu coast, suggesting an area of convergence and associatedrainfall activity. However, the surface pressure gradient over the South Bay of Bengal is
not strongly associated with the monsoon onset. Instead, a trough of low pressure at
the surface extends from the northwest Arabian sea across the southern part of thepeninsula to the southeast Bay of Bengal.
Fig. 5.4, 5.5 and 5.6 show the composite mean wind flow at 1000, 850 and 700
hPa levels associated with the monsoon onset. These maps clearly show the presenceof east-west trough (ITCZ), from south-west Arabian sea to south-east Bay of Bengal
across south Peninsula right from 1000 hPa to 700 hPa. At 1000 hPa, there is a cyclonic
circulation over southwest Bay of Bengal associated with the low-pressure area
observed at the mean sea level (Fig. 5.3). At 850 hPa and 700 hPa levels, the east-west
trough across south peninsula is very prominent. At 850 hPa and 700 hPa levels, theeast-west trough passes across south peninsula around 90N. It may be also noted thatthe strong easterlies north of the trough line, between 12 0N and 200N and strong105

westerlies south of the trough line, between the equator and 50N. Therefore, the eastwest trough zone is an area of strong horizontal wind shear and associatedconvergence.

Fig. 5.3. Composite Mean Sea Level Pressure (MSLP) during the pentad of monsoon
onset date. The onset date is the central date of the pentad. Onset dates of 1977-2020
were considered for the analysis. Data source: NCEP/NCAR reanalysis.

Fig. 5.4. Composite wind flow at 1000 hPa during the pentad of monsoon onset date.
The onset date is the central date of the pentad. Onset dates of 1977-2020 wereconsidered for the analysis. Data source: NCEP/NCAR reanalysis.

106Fig. 5.5. Same as Fig 5.4, but for 850 hPa winds.

Fig. 5.6. Same as Fig 5.4, but for 700 hPa winds.

107Fig. 5.7 shows composite latitude-height distribution of zonal winds averaged
between 800E-900E. There is an easterly wind maximum from 975 hPa to 700 hPa
between 130-170E. At the surface the zero zonal wind line is close to 10 0N, whichsuggests the presence of the east-west trough line. Between the equator and 50N, there
is a maximum of westerly zonal winds. The zonal wind pattern clearly shows a small
southward tilt with height.
Fig. 5.8 shows the pentad composite OLR pattern associated with the NE
monsoon onset. This plot was made using observed OLR data derived from National
Oceanographic and Atmospheric Administration (NOAA) satellites (http://psl.noaa.gov).
The plot shows the southern part of Peninsula, especially the eastern coast is covered
with large scale convection with OLR values less than 220 Wm-2.

Fig. 5.7. Composite Latitude-Height distribution of zonal winds averaged between 8090E during the pentad of monsoon onset date. The onset date is the central date of thepentad. Data source: NCEP/NCAR reanalysis.
108

Fig. 5.8. Composite Outgoing Long Wave Radiation (OLR) during the pentad of monsoon
onset date. The onset date is the central date of the pentad. Onset dates of 1977-2020
were considered for the analysis. Data source: NOAA OLR Data.

Over the eastern coast of Tamil Nadu (up to 140N) the composite OLR value is lessthan 210 Wm-2 suggesting severe convection over the region. Therefore, the NE
monsoon onset is associated with large scale convection (with low OLR) over southernpart of peninsula covering up to 160N or so. The monsoon onset is also associated withthe presence of large amount of precipitable water content (moisture content) over theregion (Fig. 5.9). Over the extreme southern parts of peninsula and east coast of Tamil
Nadu, high amount of PWC values (exceeding 42 kg/m2) is observed. Fig. 5.10 shows thespatial distribution of vertically integrated moisture convergence over the region
associated with the NE monsoon onset. This plot also shows abundance of moisture flux
off- east coast of south peninsula. Therefore, the monsoon onset is associated with thepresence of large amount of moisture flux and large-scale convection over the region.

109

Fig. 5.9. Composite precipitable water content (PWC) (in Kg/m2) during the pentad of
monsoon onset date. The onset date is the central date of the pentad. Onset dates of
1977-2020 were considered for the analysis. Data source: NCEP/NCAR reanalysis.

Fig. 5.10. Mean moisture flux convergence (shaded) along with 850 hPa vector winds
associated with NE Monsoon Onset.

1105.2. A new method for declaring NE Monsoon Onset
In this section, a new method is proposed for objective declaring the NE monsoon
onset. In this objective method, in addition to information on spatial distribution of
rainfall, information of large-scale convection and wind patterns are also considered.
In the previous section, we have noted that the monsoon onset is associated withthe presence of ITCZ as an east-west trough in the lower levels across south peninsula(Fig. 25 b-d) along 100N. Large scale easterly wind flow is observed north of the trough.
The emergence of this trough and easterlies are primary indications of the NE monsoon
onset over south Peninsula. The monsoon onset is also associated with large scaleconvection over the region (Fig. 26 a), in which OLR values over the east coast of Tamil
Nadu are less than 220 Wm-2. Therefore, in the new method of declaration of the NE
monsoon onset, information about easterly zonal winds, OLR and spatial distribution of
rainfall are considered.
For deriving information about these parameters, the following methodology isadopted. For calculating zonal wind, an area of 12-160N, 85-900E is considered to
calculate zonal winds at 850 hPa. For calculating OLR, an area of 09-140N, 79-820E is
considered. For spatial distribution of rainfall, following 25 rain-gauge stations of IMD
are considered:
1) Adiramapatnam 2) Chengalpattu 3) Chidambaram 4) Cuddalore 5) Kodavasal
6)

Madurabthagam

10)

Needamangalam

7)

Minambakkam

11)

8)

Nungambakkam

Muthupet
12)

9)PambanNagapattinam
13)

Ponneri14) Ramanathapuram 15) Srimushnam 16) Sriperumudur 17) Thiruvallur18) Thirvararur 19) Thondi 20) Thoothukudi 21) Thiruchendur 22) Vanur
23) Vedaranniyam 24) Sulurpet and 25) Nellore
After analyzing the data for all the years from 1981-2020, the following objective
criteria are proposed as a new method for identifying/declaring the monsoon onset.

111

1)

The NE monsoon Onset is declared on any date after 10 October, provided theSouthwest monsoon is completely withdrawn from the country.

2)

Easterly trade winds should prevail north of 120N up to 200N (approx) andextending from surface up to at least 700 hPa level. The zonal wind at 850 hPa,
averaged over the box (12-160N, 85-900E) should be moderately negative at least
for three consecutive days. Due to its large variability, a quantitative measure of
the strength of easterlies could not be identified.

3)

There is large scale convection over the south eastern peninsula region as seenin satellite pictures, with OLR value less than 210 Wm-2, averaged over the box(09-140N, 79-820E).

4)At least 50 per cent of the above mentioned 25 IMD rain-gauge stations shouldreport rainfall of 2.5 mm or more. This is equivalent to at least fairly widespreadrainfall activity over the region.

5)

The first day is taken as the monsoon onset date, provided conditions (1) to (4)are satisfied.
With these objective criteria, a new onset date has been fixed for each year andthe new onset dates thus estimated are given in Table-6 below.
The mean onset date of IMD is 22 October with a standard deviation of 6.6 days
and the mean onset date from this new method is 24 October with a standard deviationof 8.5 days. The correlation coefficient between the IMD onset date and the new onset
date is 0.60, which is statistically significant at the 99% significance level.

112

Table 5.2
New Onset Dates along with the IMD Onset Date andOnset Dates identified by Raj (2012).
Year

IMD Onset
Date

Onset Date by
Raj (2012)

New Onset
Date

1981

23-Oct23-Oct23-OctDifference between
the IMD date and new
date
0

1982

19-Oct18-Oct18-Oct1

1983

24-Oct24-Oct25-Oct-1

1984

05-Oct03-Nov

06-Nov-32

1985

25-Oct25-Oct25-Oct0

1986

27-Oct26-Oct26-Oct1

1987

30-Oct20-Oct20-Oct10

1988

02-Nov

03-Nov

03-Nov-1

1989

27-Oct29-Oct29-Oct-2

1990

19-Oct18-Oct19-Oct0

1991

20-Oct19-Oct27-Oct-7

1992

02-Nov

02-Nov

03-Nov-1

1993

20-Oct13-Oct12-Oct8

1994

18-Oct18-Oct18-Oct0

1995

23-Oct23-Oct28-Oct-5

1996

11-Oct10-Oct10-Oct1

1997

13-Oct13-Oct13-Oct0

1998

28-Oct28-Oct28-Oct0

1999

21-Oct04-Oct15-Oct6

2000

02-Nov

05-Nov

19-Nov-17

2001

16-Oct26-Oct

-10

113

2002

25-Oct30-Oct-5

2003

19-Oct19-Oct0

2004

18-Oct26-Oct-8

2005

12-Oct11-Oct1

2006

19-Oct19-Oct0

2007

22-Oct20-Oct2

2008

15-Oct12-Oct3

2009

29-Oct29-Oct0

2010

29-Oct29-Oct0

2011

24-Oct25-Oct-1

2012

19-Oct19-Oct0

2013

21-Oct21-Oct0

2014

18-Oct18-Oct0

2015

28-Oct28-Oct0

201630-Oct01-Nov-2

201727-Oct30-Oct-3

201801-Nov

02-Nov-1

2019

16-Oct20-Oct-4

2020

28-Oct13-Nov-16

Difference-2.0 days

Correlation

0.607

Mean

22-Oct21-Oct24-OctStd. Dev

6.65

8.57

8.65

In general, the new onset dates and IMD onset dates are closer in most of theyears. Both the onset dates are exactly the same in 14 years. However, there are114

differences too. The largest difference for 10 days or more is found in years like 1984,
1987, 2000, 2001, and 2020. The mean difference between IMD onset date and New
Onset date is about 2 days.
The new method of fixing the onset date is further discussed using a case study ofthe 1986 monsoon onset. Fig. 5.11 shows the time series of percent of stations
reporting 2.5 mm or more, Outgoing longwave radiation (OLR) in Wm-2 and the zonal
wind at 850 hPa. As per the new method, the onset date in 1986 is 26 Oct. On this day,
63 percent stations reported 2.5mm or more rainfall, thus indicating fairly widespreadrainfall activity. Enhanced rainfall activity continued for almost 10 days. OLR, which is a
proxy for convection, started reducing a couple of days before the onset date. Thissuggests starting of large-scale convection over South peninsula associated with themonsoon onset. OLR values near the onset were less than 200 Wm-2, which persisted foralmost one week. By 16th itself, easterlies were set in and strong easterly zonal winds
were prevailing during the onset date and subsequent 10 days. But the zonal windcomponent has shown large day to day variations.
The NE monsoon onset of 2020 is now discussed with a similar time series plot.
Fig. 5.12 shows the similar variation of parameters (as in 1986) from 24 Oct to 20 Nov.
The IMD onset date in 2020 was 28 Oct and the new onset date as per the revised
criteria was 13 Nov. On 28th October, only 14% of IMD stations reported rainfall and OLR
values were more than 240 Wm-2, suggesting not enough convection over the region.
Weaker easterly winds had just set in. Thereafter, there was not much rainfall activity
over the region. Rainfall activity again picked up on 12 November. Easterly winds started
strengthening and OLR values started dipping below 210 Wm-2. The new criteria with
the inputs on easterly winds and OLR values were completely satisfied on 13 Nov, whichis the new monsoon onset date. Therefore, the new objective criteria could identify thereal onset date which is associated with large scale convective activity over the region.

115

300

0

250

-2

200

-4

150
100
50

21

63 79 68 74 53 75 50 72

47 42
0

0

5

0

5

0

5

78
33 44

-6
-8

0

U wind m/s

OLR Wm-2

1986 Monsoon Onset-10

05/Nov

04/Nov

03/Nov

02/Nov

01/Nov

31/Oct30/Oct29/Oct28/Oct27/Oct26/Oct25/Oct24/Oct23/Oct22/Oct21/Oct20/Oct19/Oct18/Oct17/Oct16/Oct

Dates
Percent Rainfall

OLR

U windFig. 5.11. Monsoon Onset in 1986. Time series of percent of stations reporting rainfall of
2.5 mm or more (Vertical bars in blue), OLR value (Orange colour) and zonal wind at
850 hPa during 16 Oct to 05 Nov, 1986.

300
250
200

150
100
50

14 5 14 0 14

36 32

0 0 0 0 0

32

59

36

59

82 77 77
36
0 0

32

0

100 95 86
32 18

4
2
0
-2
-4
-6
-8
-10
-12

Zonal Wind m/s

OLR/Percent Rainfall

Monsoon Onset: 2020

Onset Date
Percent rainfall

OLR

Zonal Wind

Fig. 5.12. Monsoon Onset in 2020. Time series of percent of stations reporting rainfallof 2.5 mm or more (Vertical bars in blue), OLR value (Orange colour) and zonal wind at
850 hPa during 24 Oct to 20 Nov 2020. The IMD Onset date, 28 Oct is shown as red
vertical bar and the new Onset date is shown as green vertical bar.

116

Chapter-6
Variability of NE Monsoon Rainfall
NE monsoon exhibits variability in all time scales, from diurnal, synoptic to intraseasonal, inter-annual and decadal time scales. In this chapter, the NE Monsoon
variability in all time scales is discussed.
6.1. Diurnal Cycle of NE Monsoon Rainfall
The diurnal cycle is one of the most important modes of precipitation variability
over the Indian monsoon region (Rajeevan et al. 2012, Bhate et al. 2019, Raj andAmudha 2022). It is a manifestation of the atmosphere-ocean-land-cryosphere response
to incoming solar radiation. A detailed analysis and discussions on diurnal variation
during the southwest monsoon season are given in Sahany et al. (2010).
This section discusses the results from a detailed analysis of 3-hourly rainfall
derived from TRMM satellite data. Harmonic analysis was made to understand thephase and amplitude of diurnal variations. More details of the analysis of diurnal
variation are available in Bhate et al. (2019) and Rajeevan et al. (2012).
Fig. 6.1 shows the 3 hourly variation of climatological rainfall over the Indian
region during the northeast monsoon season as estimated using TRMM satellite data forthe period 1998-2019. It shows distinct types of variations over the Oceans and land.
Over the east coast of India, maximum rainfall is observed in the early morning hours,
but over the interior parts, rainfall peaks in the late evening and early night.
The spatial map of the phase (hours with maximum rainfall) revealed by the
Harmonic Analysis explained is shown in Fig. 6.2. The phase diagram clearly shows thedistinct difference in the rainfall peak over the oceans and land. Over the oceans and off
the east coast of India, rainfall during the NE monsoon season peaks in the morning. On
the other hand, rainfall peaks over the interior parts and the west coast, during the lateevening/early night (between 1830 and 2130 IST). Thus, the northeast Monsoon oversouth peninsular India exhibits significant diurnal variations with large spatial variations.
117

Along the east coast of India, an early morning peak is observed and over the inland
regions and the west coast, an afternoon/evening peaking is observed.
Fig. 6.3 shows hourly average rainfall during the NE monsoon season (Oct-Dec)
showing the diurnal variation of observed rainfall over different stations over the south
Peninsula. These plots are made using hourly station rainfall data, taken from the IMD
archives. These plots clearly suggest that the stations over the east coast experiencemaximum rainfall during the morning hours. Over the interior parts and the west coast,
maximum rainfall occurs during the evening and early night hours. Different physicalmechanisms could be responsible for the observed rainfall diurnal variations over landand oceans. The observed rainfall peak in the late afternoon over the land could be due
to intense surface solar heating and resultant convective instability. Over the east coast,
the presence of a strong sea-land breeze is also noted to be responsible for diurnal
variations (Bhate et al., 2019, Ramesh Reddy et al., 2021).
It is important to make a detailed analysis using Numerical Weather Prediction(NWP) model results, whether the NWP models are capable of predicting theseobserved diurnal variations. An analysis of diurnal variations for the southwest monsoon
revealed that NWP model (WRF model) has constraints in predicting diurnal variations
accurately (Bhat et al., 2019). The model was found successful in simulating the patternof diurnal variation of rainfall, but underestimates its amplitude compared to theobserved one especially over the western Himalayas, northeast India, central India, andthe north Bay of Bengal (BoB). It is important to carry out an extensive analysis to
examine how the NWP models are capable of predicting the diurnal variations of NE
monsoon rainfall accurately.
6.2. Intra-seasonal variation of NE monsoon rainfall
Several studies have shown that during the southwest monsoon season (June to
September) a substantial component of the variability of convection and rainfall overthe Indian region arises from the fluctuations on the intra-seasonal scale between active118

and weak or break spells (Ramamurthy, 1969; Goswami, 2005; Rajeevan et al., 2010).
Long intense breaks are known to have an impact on the seasonal monsoon rainfall overthe country (Webster et al., 1998; Gadgil and Joseph, 2003, Rajeevan et al., 2010). The
dry and wet spells of the active and break conditions represent the sub-seasonal or
intra-seasonal variations of the monsoon with timescales longer than synoptic activity(1–10 days) but shorter than a season.
Recent research results also provided new insights regarding the origin of themonsoon intra-seasonal variations. The ISOs of summer monsoon essentially havetimescales between 10 and 90 days. Within the broad range of 10–90 day periods, two
period ranges with periodicities between 10 and 20 days and 30 and 60 days
respectively are particularly prominent (Goswami, 2005). Active and break spells andintra-seasonal variations of the Indian summer monsoon have been extensively studied.
As far as the northeast monsoon is concerned, not much knowledge is available on theintra-seasonal activity during the NE monsoon season. Therefore, the results of theintra-seasonal activity during the northeast monsoon season are discussed in thissection.
Fig. 6.4 shows daily rainfall (averaged over the NE monsoon region) from 15
September to 15 January during two sessions, 2008-2009 and 2011-2012. These plots
clearly show that rainfall activity over the region is confined to only a few days during
the season, which is interspaced between dry or weak monsoon spells. For example,
during 2008-2009, heavier rainfall activity was observed around 20-30 October. The next
rainfall activity was observed almost after one month, from 20 Nov onwards. Between
30 October to 20 November, rainfall activity over the region was generally subdued.
Similar intra-seasonal variability of rainfall with enhanced (suppressed) activity wasobserved during 2011-12 also.

119Fig. 6.1. Average 3-hourly seasonal (October to December) rainfall (mm/3h) over the
Indian region averaged over the years 1998-2019 derived from the TRMM 3G68 data.

Fig. 6.5 a shows the Hovmuller diagram showing Outgoing Longwave Radiation(OLR) anomalies averaged over 700 -900E plotted with latitude versus time (15 Sept 1998
to 15 January 1999). Fig. 6.5 b shows the similar plot but for the period 15 Sept 2000 to
15 January 2001. OLR is a proxy for atmospheric convection. Low OLR suggests moreconvection. These two plots clearly show strong intra-seasonal activity during the NE120monsoon season. Fig 6.5 a shows clear northward propagation of OLR anomalies fromthe equator around 16 October and the next one around 15 November Anothernorthward propagation is observed around 18 December. However, all the northward
propagation of convections are limited to 150N only. During the southwest monsoon
season, this northward propagation moves to even the foothills of the Himalaya. Fig 6.5
b shows a similar plot, but for zonal wind anomalies at 925 hPa. This plot clearly shows
the strengthening of the shear zone (between westerlies in the south and easterlies in
the north) at particular intervals. This plot also shows the slow southward movement of
the horizontal wind shear zone by December, which represents the southward
movement of ITCZ.

Fig. 6.2. Phase angle (time of maximum rainfall peak) in Indian Standard Time (IST)
based on satellite data for the period using the Harmonic Analysis (1998-2019).
121

East Coast stations

122

Central Region Stations

123

Central Region Stations

124

West Coast Stations

Fig. 6.3. Hourly average rainfall (in mm) during the NE monsoon season (Oct-Dec)
showing diurnal variation of rainfall at different stations over south Peninsula. Period:
1969-2021.

125

Using daily rainfall data from 15 September, 1998 to 15 January 1999, a spectral
analysis was made to see the periodicities of rainfall during the NE monsoon season. The
results are shown in Fig. 6.6. The spectral analysis shows a strong periodicity of rainfall
activity with 30-40 days, which is also statistically significant. Another weaker periodicity
is observed around 20 days. Fig 6.6 c shows the wavelet spectrum which also suggests
the periodicity around 30-40 days. It may be interesting to note that these two
periodicities are predominantly observed during the southwest monsoon season. Morestudies are required to understand the physical mechanisms of these oscillations during
the NE monsoon season. What are the physical mechanisms for these oscillations andwhat is the predictability of this oscillation?
Sreekala et al., (2018) analyzed intra-seasonal rainfall activity during the NE
monsoon season and the combined effect of Madden Julian Oscillation (MJO), NSO and
IOD. The study has revealed that the intra-seasonal variation of daily rainfall over thesouth peninsula during the NEM season is associated with various phases of theeastward propagating MJO life cycle.
A similar study was made using more years of updated data to understand the
ISO activity during the NE monsoon season in terms of different phases of MJO. ERA5
(Hersbach et al., 2020) daily precipitation datasets have been used for analyzing theintra-seasonal variation of NE Monsoon rainfall over the Oceanic region. Total hourly
precipitation data are used in this study. In addition, zonal and meridional winds at 850
hPa from ERA5 data and Outgoing Long-wave Radiation (OLR) data (Liebmann and Smith
1996) from NCEP/NCAR during 1979-2021 are also used in the current study.
The

Real-timeMultivariate

MJO

indices

(RMM1

and

RMM2)

from

http://www.bom.gov.au/ bmrc/clfor/cfstaff/matw/maproom/RMM/ were used for
defining the various Phases of MJO (Wheeler and Hendon 2004). MJO indices were
calculated as the principal component (PC) time series of the two leading empirical126

orthogonal functions (EOFs) of combined daily mean fields for 850 and 200 hPa zonal
winds and OLR averaged over the tropics (15°S–15°N).

Fig. 6.4. Time series of daily rainfall averaged over NE Monsoon region during 15
September to 15 January a) 2008-2009 b) 2011-2012.

Fig. 6.5 a. Hovmuller diagram (Time vs Latitude) showing northward propagation of
convection (OLR anomalies) during 15 September 1998 to 15 January 1999, averaged
over 700-900E.

127

Fig. 6.5 b. Hovmuller diagram (Time vs Latitude) of zonal wind (m/sec) at 925 hPa from15 September 2010 to 15 January 2011.

Fig. 6.6. Daily rainfall (in mm) averaged over south Peninsular India from 15 September
1998 to 15 January 1999, b) Lomb-Scargle periodogram of daily rainfall over south
Peninsular India from 15 September 1998 to 15 January 1999 and c) wavelet power
spectrum of climatological daily rainfall averaged over south peninsular India (19942004).
128

Maps of composite rainfall anomaly (mm) in respect of eight strong phases of
MJO superimposed with composite daily OLR anomalies (W/m-2) and composite surface
wind vector in the eight strong MJO phases were prepared using the data for the period
1979–2021. The results are shown in Fig. 6.7. Positive rainfall anomaly over south
peninsular India and the surrounding Indian Ocean (IO) is observed during the strong
MJO phases 2, 3 and 4; and negative rainfall anomaly during the strong MJO phases 6,7
and 8. Therefore, an understanding of the phase of MJO (which is available on real timeat http://www.bom.gov.au/) is useful for assessing the prospects of NE monsoon rainfall
over the south Peninsula. From Fig. 6.7, it is very clearly seen the northeastward
movement of positive rainfall anomalies from the equatorial Indian Ocean to the west
Pacific and neighborhood.
Shanmugasundaram et al. (2017) employed hidden Markov model tocharacterize the spatio-temporal variations of NE monsoon rainfall at pentad time stepand its probability of occurrence during 1982–2014. The results indicated the dominantpresence of three rainfall states during the season, which were the wet (State-1),
coastal wet (State-2), and dry (State-3) states. Seasonal total NEIMR was significantly
and positively correlated with the frequency of State-1, whereas it was negatively
correlated with that of State-3, indicating a crucial role of the rainfall states indetermining water requirements in the southeastern peninsular India. Wet conditions
were characterized by enhanced cyclonic activities and increased moisture
convergence at 850 hPa over the southeastern peninsular India and its neighbouring
oceanic regions (Bay of Bengal and Indian Ocean).
In contrast, dry conditions were associated with anticyclonic circulation andreduced moisture convergence at 850 hPa. The study by Somenath Dutta et al. (2016)
revealed that the transition from weak phase to strong phase of north-east monsoon isassociated with an enhancement in conversion of zonal potential energy to zonal kinetic

129

energy, implying a strengthening of Hadley circulation, favouring the above transition. It
is also observed that the transition from weak phase to strong phase is associated withenhanced baroclinic energy conversion.

Fig. 6.7. Maps of composite rainfall anomaly (mm) (shaded) in respect of eight strong
phases of MJO superimposed with composite daily OLR anomalies (W/m2) (lines) andcomposite surface wind vectors in the eight strong MJO phases and during wet spellusing the data for the period 1979–2021.
6.2.1. Active and Weak Spells of NE Monsoon
Understanding of the intra-seasonal aspects of the NE monsoon is very importantfor prediction of rainfall. It is well known that the southwest monsoon season exhibits
strong active and break spells during the season, and extensive analyses of monsoon
active and break spells have been made. During southwest monsoon season, there are
active and break spells, with specific characteristics (Ramaswamy 1969, Gadgil andJoseph, 2003 and Rajeevan et al. 2010). It was shown that the total number of active

130

and break spells during the season is statistically well correlated with the monsoon
seasonal rainfall (Rajeevan et al. 2010). Larger number of break spells during the season
could lead to a deficient monsoon. However, similar studies of active and break spells
during the NE monsoon are unavailable. Since the earlier discussions suggest that there
is strong intra-seasonal activity during the NE monsoon season, it is important tounderstand the active and break spells during the NE monsoon season. A similar
analysis of active and weak spells during the NE monsoon season was carried out andthe results are discussed below.
Using the IMD gridded daily rainfall data, an area averaged (8-140 N, 80-850 E)
daily rainfall time series is prepared from 01 Oct to 31 Dec. Using the daily rainfall data,
standardized rainfall is calculated for all these days from 1981-2021. An Active (wet)Spell is considered when the area averaged standardized rainfall is more than 1.0 for
consecutively three days. Similarly, the weak (dry) spell is considered when the area
averaged standardized rainfall is less than -0.8. The asymmetry in the threshold ofstandardized rainfall anomaly was made to ensure active and weak spell days are similar
on a long-term climatological data. Using these criteria, active and weak spells during
Oct-Dec are identified for the period 1981-2021. These criteria are similar to the criteria
for the active and break events adopted for the southwest monsoon season (Rajeevan
et al. 2010).
Table 6.1 shows the active and weak spell days during the period 1981-2021,
identified using the above criteria.
The time series of total number of active and weak days during the season, yearwise is given in Fig. 6.8. The mean number of active (weak) days during the season is
7.07 (6.56) with a standard deviation of 5.89 and 6.08 respectively. The standard
deviation of active and weak spells is very large. There are few years in which there isneither active nor weak spells. In 2003, maximum number of active days (24) was
observed. In 1988 and 2016, maximum number of weak days (20) was observed. As
131observed during the southwest monsoon season, total number of active and weak spells
during the season influence the NE Monsoon seasonal rainfall. The correlation
coefficient between the active and weak days during the season with the seasonalrainfall is 0.447 and -0.603 respectively, which are statistically significant at 95%
significance level. Total number of weak days is more correlated with the seasonalrainfall, compared to the total number of active days.
Table 6.1
Active and Weak Spells during the NE Monsoon Season (Oct-Dec).
Period 1981-2021
Year

Active Days

1981
1982
1983
1984
1985
1986
1987

25-28 Oct, 02-04 Nov
26-28 Oct, 03-05 Nov
21-27 Dec
NIL
01-03 Oct, 05-11 Oct
05-07Nov,12-14Dec
16-20 Oct1988

22-25Dec

1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001

Weak Days
11-17 Oct, 21-23 Nov
01-13Oct11-13Nov
16-20 Oct, 31 Oct-4Nov
19-23 Oct20-25 Oct

11-18 Oct, 20-23 Oct,27-31 Oct, 21-23Nov
NIL
03-05 Oct, 14-16 Oct
25-28Oct, 31Oct-2Nov,15-17Dec, 28-31Dec 7-9 Nov
29-31Oct,15-18Nov,24-26Dec
NIL
13-18 Nov,
20-31 Oct
8-12Nov, 4-7 Dec
NIL
04-06 Oct,7-9 Dec ,27-29 Nov
NIL
NIL
NIL
02-05 Oct,18-20 Oct,9-11 Dec, 13-17Dec
2-6 Nov
8-10Nov, 24-30 Nov, 4-10 Dec
07-9 Oct
11-18Oct, 7-10 Nov,8-12 Dec
NIL
5-8Oct, 21-23 Nov
1-3Nov
NIL
25-27 Oct,30Oct-9Nov
NIL
NIL

132

2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021

13-16 Oct
1-8 Oct,21-25 Oct
6-8 Oct,21-24 Oct,1-17 Dec
11-13 Oct,15-17oct,2-6Nov
3-5 Oct
3-5 Nov
12-15 Oct,22-25 Nov
6-8 Oct
26-30oct,4-6dec
12-14 Oct19-21 Dec
9-13 Oct24-30 Nov,19-21 Dec
1-3 Oct, 29 Oct-7 Nov
1-5 Oct, 5-12 Nov, 14-16 Nov
10-12 Oct ,14-28 Oct
31Oct-2 Nov ,17-19 Nov,21-23 Nov, 7-9 Dec 11-14 Oct
1-3 Oct, 5-9 Oct,18-21 Oct,
NIL
21-23 Nov
1-3 Nov
8-10 Oct,26-28 Oct
22-26 Oct
9-11 Nov
26-28 Oct
2-5 Oct
30 Nov-3 Dec
16-18 Oct,21-24 Oct
15-20 Oct,22-29 Oct,
NIL
6-8 Nov,11-13Nov
NIL
17-19 Oct,23-26 Oct
NIL
26-31 Oct,6-8Nov
19-22 Oct, 24-26 Oct, 30Nov-3Dec, 13-15
NIL
Dec
11-15 Oct,16-18 Nov, 3-5 Dec,7-9 Dec,
30 Oct-2 Nov
17-19 Oct,12-15 Nov,18-21 Nov
NIL

To understand better the active and weak spells during the NE monsoon season,an analysis is made to make the composites of circulation and SST anomalies for theactive and weak spells using the days of active and weak spells mentioned in Table 6.1.
The results are discussed below.
Fig. 6.9 shows the composite rainfall anomalies during the active and weak spelldays, calculated using the rainfall data of ERA5. Fig. 6.9 shows large positive (negative)
anomalies over the south peninsula during the active (weak) spell days. Over theequatorial south Indian Ocean and the west Pacific, there are sharp differences between
the active and weak spell days. An active (weak) spell is also associated with suppressed(enhanced) rainfall activity over the west Pacific and the equatorial Indian Ocean.
133

Total Number of Active Days
No of Days30

r= 0.447

25
20
15
10
5
0

Year

Total number of Weak Days
No of Days25

r= -0.603

20
15
10
5
0

Year

Fig. 6.8. Total number of active (above) and weak (below) days during the OND season
for different years, 1981-2021. The correlation coefficient between the total number ofactive and weak days with the seasonal rainfall is shown in the plots.

134

Fig. 6.9. Composite rainfall anomalies (mm) during the active spell days (above) andweak spell days (below) for the period 1981-2021.
Fig. 6.10 shows the composite SST anomalies during the active and weak spelldays. The largest differences between the active and weak spells are observed over theequatorial Pacific and the north Indian Ocean. Over the equatorial Pacific Ocean, the SST
anomalies are positive (negative) during the active (weak) spells. This indicates thatenhanced rainfall activity (more active days) is observed during the El Nino years.
Similarly, during the La Nina years, the weak spell days are more. During the active(weak) spell days, SST anomalies over the equatorial Bay of Bengal and Arabian Sea are135

large negative (positive). However, over the north Bay of Bengal, positive (negative)
anomalies are observed during the active (weak) spell days.
Fig. 6.11 shows the similar composite plots, but for the OLR anomalies. The
results are consistent with the rainfall anomaly plots. During the active (weak) spelldays, OLR anomalies are large negative (positive) over the Indian sub-continent
suggesting enhanced (suppressed) rainfall activity. The drastic difference between thesetwo cases is observd over the west Pacific Ocean and China and adjoining area. During
the active (weak) phase of the monsoon, convection over the west pacific is suppressed(enmhanced). The enhanced convection over the west Pacific could cause anomalousdescending motion over the Indian region and thus reduce the NE monsoon activity.
Fig. 6.12 shows the 850 hPa wind anomalies during the active and weak spells of
NE monsoon. The most striking feature of the wind anomalies is observed over the
Indian region. The active (weak) phase of the NE monsoon is associated with cyclonic(anti-cyclonic) circulation anomalies over the Indian region, which is consistent with theobserved rainfall anomalies. The other significant anomalies are observed over the west
Pacific and adjoining eastern parts of China. During the active (weak) phase, ananomalous anticyclonic (cyclonic) circulation is observed over the region, suggesting
below (above) normal convection over the region. This is consistent with the OLR
anomalies discussed above.
6.3. Interannual variation of NE monsoon rainfall (NEMR)
In this section, the inter-annual variability of NE monsoon rainfall is discussed.
There are not adequate studies examining the inter-annual variability of NE monsoon
rainfall except the studies by De and Mukhopadhyay, 1999; Kripalani and Kumar, 2004;
Raj and Geetha, 2008; Zubair and Ropelewski, 2006; Kumar et al., 2007; Sreekala et al.,2012, Rajeevan et al., 2012.

136

Fig. 6.10. Composite Sea Surface Temperature (SST) (°C) anomalies during the active
spell days (above) and weak spell days (below) for the period 1981-2021.

137

Fig. 6.11. Composite OLR anomalies (Wm-2) during the active spell days (above) andweak spell days (below) during the period 1981-2021.

138Fig. 6.12. Composite OLR anomalies (Wm-2) during the active spell days (above) andweak spell days (below) during the period 1981-2021.

The inter-annual variability of the NEMR is calculated using the sub-divisional
rainfall data for the period 1901–2021. During the northeast monsoon season, south
peninsular India receives a mean rainfall of 338.4 mm with a coefficient of variation of
about 25%. It may be noted that the coefficient of variation during the NE monsoon
season is much more than that of SW monsoon rainfall (June to September) for thewhole country which is around 10%.
Fig. 6.13 a shows the interannual variation of the NEMR as expressed as percent
departure of the seasonal rainfall. No long-term trend in the NEMR is noticed, but there
139are years with large rainfall departures, even exceeding 40%. The years with more (less)than 1 Standard Deviation (25%) are termed as excess (deficient) years. If the departureis more than -25% but less than 25%, then those years are termed as normal years. Outof 121 years (1901-2021), there were 78 normal years, 23 excess years and 20 deficient
years. Among these years, the 2021 monsoon season has the highest positive departure(73%). The year 2016 was the worst deficient year with a deficiency of 65%. The othertwo notable excess years are 2010 and 2015. The other two notable deficient years are
1938 and 1988. Recently, three consecutive years 2016-2018 experienced below normal
rainfall with large negative rainfall departures. However, the subsequent three years
witnessed above normal monsoon rainfall with positive departures. Fig. 6.13 b shows
the 21-year moving average of the NEMR during the period 1901–2021. It clearly shows
the multi-decadal variations of the NEMR with epochs of above normal and below
normal rainfall. An increasing trend in the NEMR during the recent years is observed.

Fig. 6.13 a. Time series of NE monsoon seasonal rainfall as % Departure from 1901-2020.
El Nino year is shown as red and La Nina year is shown as blue lines.

140

370.021 year moving

360.0
350.0
340.0
330.0
320.0
310.0
300.0
290.0

1901
1904
1907
1910
1913
1916
1919
1922
1925
1928
1931
1934
1937
1940
1943
1946
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000

280.0

Fig. 6.13 b. The 21 year moving mean of NE monsoon seasonal rainfall (Oct-Dec) in mm,
showing multi-decadal variations of NE monsoon rainfall.

Fig. 6.14 a shows the periodogram of NE Monsoon seasonal rainfall for the period
1901-2021. Even though many periodicities of shorter duration are seen, the periodicity
of about 16 years is close to the significant (90% significance) level. Fig. 6.14 b shows the
wavelet spectrum of NE monsoon seasonal rainfall for the same period 1901-2021. It
clearly shows the periodicity of about 16 years, which is statistically significant. This
periodicity, however, was not uniformly active during the whole period. It was active till
about 1950. Then, it became active from 1980s till date. Raj (2012) also suggested theperiodicity of similar periods for NE monsoon rainfall. This periodicity is also apparent in
the 21-year running mean shown in Fig. 6.13 b. The periodicity of 2-3 years (quasi
biennial) is observed during 1920s to 1970s. Kripalani and Kumar (2004) also suggested
the NE monsoon rainfall undergoes different epochs of above and below normal rainfall.
These epochs last about a decade or two. More studies are required to understand thedecadal variations of NE monsoon rainfall using long term data.

141

Fig. 6.14 a. Periodogram of NE Monsoon seasonal rainfall (1901-2021). The peak around16 years is close to significance level at 90%.

Fig. 6.14 b. Wavelet power spectrum (1901-2021) of NE monsoon seasonal rainfall. The
periodicity close to 16 years is statistically significant.

142

The inter-annual variability of the NEMR is linked to the El Nino/Southern
Oscillation (ENSO), the Indian Ocean Dipole and the EQUINOO (De and Mukhopadhyay,
1999; Kripalani and Kumar, 2004; Raj and Geetha, 2008; Jayanthi and Govindachari
1999; Zubair and Ropelewski, 2006; Kumar et al. 2007; Sreekala et al. 2012, Rajeevan etal. 2012). ENSO is an irregular periodic variation in Sea surface temperatures and winds
over the equatorial Pacific Ocean. ENSO influences the climate of much of the tropics
and subtropics. ENSO is a coupled process in which the equatorial Pacific andatmosphere interact. The warming phase of the sea surface temperature is known as El
Nino and the cooling phase as La Nina. The Southern Oscillation is the accompanying
atmospheric component, coupled with the changes in SST.
The Indian Ocean Dipole (IOD) is defined by the difference in sea surface
temperature between two areas – a western pole in the Arabian Sea (western Indian
Ocean) and an eastern pole in the eastern Indian Ocean south of Indonesia. The IOD
affects the climate of Australia and other countries surrounding the Indian Ocean Basin,
and is a significant contributor to rainfall variability in this region. Positive Dipole eventsare characterized by positive (negative) SST anomalies over the west (east) equatorial
Indian Ocean. Conversely, the negative phase is characterized by negative (positive) SST
anomalies over the west (east) equatorial Indian Ocean.
Fig. 6.15 a shows the spatial pattern of correlations between Oct-Dec SST and NE
monsoon rainfall during two 30-year periods, 1961-1990 and 1991-2020. The plot forthe period 1961-1990 clearly shows that positive SST anomalies over the equatorial
Pacific Ocean (El Nino) are associated with normal or above normal rainfall. However,during the recent 30-year period, the positive correlations over the equatorial Pacifichave weakened. Another interesting area of strong correlation is over the North
Atlantic. During the period 1961-1990, strong positive correlations are observed, which
are replaced by weak negative correlations during the period 1991-2020.

143

Raj and Geetha (2008) analyzed the relationship between Southern Oscillation
Index (SOI) and NE monsoon rainfall in antecedent and concurrent mode and foundthere is a negative relationship. The relationship in antecedent mode is stronger.
Sengupta and Nigam (2019) studied the aspects of ENSO impact on NE monsoon rainfall.
Their study suggested stronger NE monsoon rainfall over south peninsula and Sri Lanka
during El Nino events. The impact varies sub-seasonally, being weak in October andstrong in November. The positive anomalies over the south peninsula are generated byanomalous anticyclonic flow centered over the Bay of Bengal, which is forced by an El
Nino-related reduction in deep convection over the Maritime continent.
In fact, the correlation between NE monsoon rainfall and Nino 3.4 changes its
sign by middle of October. Till middle of October, the correlation is negative and itchanges to positive correlation by November. Fig. 6.15 b clearly suggests this shift in thesign of correlation between Nino 3.4 and NE monsoon rainfall by October end.
However, the relationship between NE monsoon and ENSO is not very stable. It
was weakened during the recent epoch (1991-2020) (Fig. 6.15 c). Another interesting
aspect to be noticed is the positive correlation of SSTs over the north Bay of Bengal with
the NE monsoon rainfall, suggesting a warmer Bay of Bengal could be related to better
performance of NE monsoon rainfall. It may be interesting to examine long term data tounderstand why the relationship between ENSO and NE Monsoon rainfall undergoes
secular variations.

144

Fig. 6.15 a. Spatial Pattern pf correlation between Sea Surface Temperature (SST) andNE Monsoon seasonal rainfall during the period 1961-1990 (above) and 1991-2020(below).

145

Fig. 6.15 b. Correlation between pentad rainfall averaged over south peninsula and Nino
3.4 SST index. The period 1948-2021 is considered for the analysis.

0.6

21 yr Correlations between Nino 3.4

0.5
0.4

0.3
0.2
0.1

0
-0.1
-0.2

1901
1904
1907
1910
1913
1916
1919
1922
1925
1928
1931
1934
1937
1940
1943
1946
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000

-0.3

Fig. 6.15 c. The 21 year moving correlations between Nino 3.4 index during OND and NE
Monsoon seasonal rainfall (OND) suggesting multi-decadal variation of relationshipbetween NE monsoon rainfall and El Nino.

146

Table 6.2 below shows El Nino/La Nina's relationship with NE monsoon rainfall.
Table 6.2
Relationship between ENSO and NE Monsoon Rainfall
Period: 1940-2021
El Nino Years (39)

La Nina Year (42)

ExcessNormalDeficient

ExcessNormalDeficient>25%-25% to 25%< -25%>25%-25% to 25%< -25%

11

22

6

6

23

13

28%

57%

15%

14%

55%

31%

The analysis shows that the probability of an El Nino year being an excess NEMR
year is higher (28%) compared to the year being a deficient (15%) monsoon year.
However, the probability of a La Nina year being an excess monsoon year (14%) issmaller compared to an excess (31%) monsoon year. Therefore, more confidence inexcess or deficient monsoons may be obtained with the additional information on IOD.
However, compared to the Southwest monsoon, the NE monsoon is not very strongly
related to ENSO or IOD.
An analysis was carried out by Prasanna et al. (2019) of two successive La Niña
years, referred to as the first and second year during the period 1900–2010 to see theimpact on NE monsoon rainfall. Observations show that despite noticeable weakening in
the equatorial Pacific cooling from the first year to the second year, strong La Niña
teleconnections and the rainfall deficiency over the region remains the same in most of
the multiyear-La Niña events (70%).
Even though, there is a high probability for NE monsoon to be on lower side of
the normal during the La Nina years, there have been three major exceptions to be
examined in detail. These years occurred recently, 2010 and 2021, when the ENSO-NE147

monsoon rainfall relationship was weaker. The NE monsoon rainfall percent departureduring these three seasons was 49% and 73% respectively. The year 2021 was truly an
exception.
Kripalani and Kumar (2004) documented the NEMR and IOD relationship. Theysuggested that the NEMR variability is enhanced during the decades when the IOD
exhibits its active phase, and is suppressed during the decades when the IOD is inactive.
This relationship suggests that the positive (negative) phase enhances (suppresses) thenortheast monsoon activity. During the positive phase, the anomalous flow patternshows winds converging and suggesting moisture transport from the southeast Indian
Ocean and the Bay of Bengal towards south peninsular India. In contrast, the negative
phase reveals winds diverging and transporting moisture away from the south Indian
region.

These results show the direct influence of the IOD phenomenon on theinterannual NE monsoon rainfall variability over south India.
Balachandran et al. (2006) examined the local and teleconnective association
between Northeast Monsoon Rainfall over Tamil Nadu and global surface temperature
anomalies (STA) using the monthly gridded STA data for the period 1901–2004. It isobserved that the meridional gradient in surface air temperature anomalies between
Europe and north Africa, in the month of September is directed from the subtropics(higher latitudes) to higher latitudes (subtropics). It is also observed that North Atlantic
Oscillation (NAO) during September influences the surface air temperature distribution
over north Africa and Europe. Also, the NAO index in January shows significant inverse
relationship with the NE monsoon rainfall since recent times. The central and easternequatorial Pacific oceanic regions have significant and consistent positive correlation
with NE monsoon rainfall while the western equatorial region has significant negative
correlation with Northeast monsoon rainfall. A zonal temperature anomaly gradientindex (ZTAGI) defined between eastern equatorial Pacific and western equatorial Pacificshows stable significant inverse relationship with Northeast monsoon rainfall.

148

The unusual excess year of the 2021 NE monsoon season is discussed below.
6.4. The unusual NE monsoon during the year 2021
During the year 2021, the southwest monsoon withdrew from the Indian region
on 25th October. Simultaneously, the Northeast monsoon (NEM) of 2021 commenced
over the southeastern parts of peninsular India on 25th October against the normal dateof 20th October. Excepting Coastal Andhra Pradesh (CAP), which received normal
rainfall during the season, the other four sub-divisions [Tamil Nadu (TN (including
Puducherry & Karaikal), Kerala (KER), Rayalaseema (RYS) and South Interior Karnataka(SIK)] benefitted from the NE monsoon. These sub-divisions received excess to large
excess rainfall during the NEM season (October-December) with KER, SIK, RYS recording
more than 100% excess (large excess) rainfall. During the season, there were 30 days ofactive to vigorous monsoon conditions over Tamil Nadu and Kerala. There were 65 daysof isolated heavy rainfall activity with 33 days of isolated very heavy rain, including 09
days of isolated extremely heavy rainfall activity over Tamil Nadu. Two Depressions
formed over the North Indian Ocean during November contributed significantly to NEM
rainfall over the peninsular India. Cyclonic Storm (CS) Jawad over the Bay of Bengal(BOB) during 02-06 December tracked northwards towards West Bengal- Bangladesh
coasts and did not contribute towards NEM seasonal rainfall. However, two days ofextremely heavy rainfall occurred over Chennai (i) 06th November night & (ii) 30th
December 2021. Recurrent heavy rainfall over the coastal and adjoining districts fromthe last week of October to November led to the filling up of water bodies, and inland
and riverine flooding occurred over several areas of Tamil Nadu and Rayalaseema. As aresult, NE monsoon 2021 was extended into January 2022 and cessation of NEM 2021
rainfall over peninsular India was declared on 22nd January 2022 (Geetha et al., 2022). A
more detailed report on 2021 NE monsoon is available for reference (Geetha et al.,2022).
The Table 6.3 presents the frequency of active and vigorous monsoon days andheavy rainfall days during the 2021 NE monsoon season (after Geetha et al., 2022).

149

Table 6.3
Subdivision
Activity
Vigorous
ActiveNumber of Days
Heavy Rainfall
Extremely Very Heavy
Heavy
9
33

Heavy

Tamil Nadu

8

22

65

CoastalAndhraPradeshRayalaseema

2

7

0

8

28

8

12

1

4

22

Kerala

11

19

2

18

40

South Interior
5
10
0
9
28
Karnataka
Note: Heavy Rainfall > 6.5 cm/day Very Heavy rainfall> 12 cm/day and Extremely Heavy
rainfall >21 cm/day

Active: Failrly widespread to widespread sub-divisional rainfall with rainfall morethan 1.5 to 4 times the normal with at least two stations reporting more than or equal 5
cm in coastal Tamil Nadu and south coastal Andhra Pradesh and 3 cm elsewhere in the
NEM region.
Vigourous: Fairly widespread to widespread sub-divisional rainfall with rainfall
more than 4 times the normal with at least two stations reporting more than or equal to
5 cm in the coastal Tamil Nadu and the south coastal Andhra Pradesh and 3 cm
elsewhere in the NEM region.
Fig. 6.16 a shows the seasonal (Oct-Dec) rainfall over the south peninsula andneighborhood (in mm/day) during the 2021 monsoon season. It shows widespreadabundant rains over the south Peninsula and the adjoining southwest Bay, suggesting an
excess monsoon year (73% above its long period average).

150

Fig. 6.16 a. Precipitation anomaly (mm/day) during OND 2021. Source: ERA5 reanalysis.

The 850 hPa wind pattern (Fig. 6.16 b) shows an extended east-west troughextending from the east central Arabian sea to the west equatorial Pacific Ocean acrossthe south peninsula and south Bay of Bengal. This suggests the presence of active ITCZ
over the region during the NE monsoon season. This convergence zone must be thecause of the genesis of several weather systems over the south Bay of Bengal and theirwestward movement towards the south Peninsula. The SST anomaly during Oct-Dec
2021 (Fig. 6.16 c) shows the presence of cold SST anomalies over the equatorial Pacific,
suggesting La Nina conditions. It may be interesting to note the presence of above
normal SSTs over the Bay of Bengal. Some recent studies like Singh et al. (2017)
suggested that local air-sea interaction plays a crucial role in modulating or driving
extremes over South Peninsula associated with ENSO. More studies are required tounderstand the physical mechanisms of relationship of Bay of Bengal SSTs and the NE
monsoon rainfall.

151

Fig. 6.16 b. 850 hPa wind anomalies during OND 2021. Source: ERA5 reanalysis.

Fig. 6.16 c. SST anomalies during OND 2021. Source: NOAA OI SST data.

6.5. Seasonal Forecasting of NE Monsoon
India Meteorological Department (IMD) has been attempting to prepare longrange forecasting of NE Monsoon rainfall (Oct-Dec) using indigenously developed
statistical models based on principal component analysis (PCA). The parameters used forthe statistical models are shown in Fig. 6.17. Overall, five predictors are used for longrange prediction of NE monsoon rainfall. Out of these five predictors, four predictors are152derived from the Indian and West Pacific regions, while the fifth parameter is derived
from the Atlantic Ocean. Since the inter-annual variability of NE monsoon rainfall isquite large (25%), developing a skillful statistical prediction scheme could bechallenging. IMD, however, shares the long-range forecasts confidentially with theconcerned state governments every year. Therefore, more research studies are required
to understand the inter-annual variability of NE monsoon rainfall better and develop
skillful long-range forecast models.
Rajeevan et al. (2012) suggested that present-day dynamical models have serious
problems in properly simulating mean monsoon rainfall and its teleconnections.
Coupled climate models do not correctly simulate the sign of the ENSO-NE Monsoon
rainfall relationship. Furthermore, there is absolutely no skill with the present dynamical
models in predicting inter-annual variability of NE monsoon.

Fig. 6.17. The parameters used in the statistical model for the long-range forecasting of
NE Monsoon rainfall by the India Meteorological Department.

153

The study by Acharya et al. (2011) suggested that the general circulation models
considered in the study are not able to simulate the observed interannual variability ofrainfall. They attributed this to inaccurate response of the models to sea surface
temperatures. They found that the multi-model ensemble scheme improved theaccuracy of simulations. The study by Sengupta and Nigam (2019) revealed that thehistorical twentieth-century climate simulations informing the Intergovernmental Panelon Climate Change’s Fifth Assessment (IPCC-AR5) showed varied deficiencies in the NEM
rainfall distribution and a markedly weaker (and often unrealistic) ENSO–NEM rainfall
relationship.
Prasanna et al. (2021) examined the fidelity of the eight Asia-Pacific Economic
Cooperation (APEC) Climate Center (APCC) models in representing the inter-annual
variability and decadal shift in the northeast monsoon (NEM; October–December)rainfall over Southern Peninsular India (SPI). The observations showed a clear interannual and inter-decadal variability of NE monsoon rainfall. The analysis suggested thatmost of the models exhibited poor skill in representing the inter-annual variability. Only
APCC model rainfall is in phase with observed SPI rainfall variations on the inter-annual
time scale. More research work is required to improve both the statistical anddynamical models in making reliable long-range forecasts of NE Monsoon rainfall in the
coming years.

.

154

Chapter-7
NE monsoon over Sri Lanka
Sri Lanka lies within the tropics between 5° 55' to 9° 51' North latitude and between
79° 42' to 81° 53' East longitude, with a tropical climate. The central part of the southernhalf of the island is mountainous with heights more than 2.5 Km. The remainder of theisland is practically flat except for several small hills that rise abruptly in the lowlands.
These topographical features strongly affect the spatial patterns of winds, seasonalrainfall, temperature, relative humidity and other climatic elements, particularly during
the monsoon season.
7.1. Mean Rainfall
Sri Lanka received rainfall due to southwest and northeast monsoons, local
convective storms (thunderstorms) and low-pressure systems like lows/depressions
moving across the region. The mean annual rainfall varies from 900 mm in the driest
parts (southeastern and northwestern) to over 5000 mm in the wettest parts (western
slopes of the central highlands). Fig. 7.1 shows the spatial distribution of annual rainfall
over Sri Lanka taken from the Sri Lanka Met Department.

Fig. 7.1. Annual rainfall over Sri Lanka in mm. (Source: Sri Lanka Met Department).
155

As per the Sri Lanka Met Department, the seasons in Sri Lanka are classified into
1) First inter-monsoon season (March-April) 2) Southwest monsoon (May- September)
3) Second Inter-monsoon season (October-November) and 4) Northeast monsoon(December-February). The spatial distribution of mean rainfall during these four seasonsare shown in Fig. 7.2.

(a)

(b)

(C)

(d)

Fig. 7.2. Mean rainfall in mm during a) March-April b) May-September c) OctoberNovember and d) December-February. Source: Sri Lanka Meteorological Department.

156

The months October-November contributes maximum rainfall with the wholecountry experiencing rainfall more than 300 mm. Except during December-February,
maximum rainfall is observed over the western coast, especially the southwest coast.
During the December-February season, maximum rainfall is observed over the east
coast.
The monthly mean rainfall averaged over the whole Sri Lanka is shown in Fig. 7.3.
This time series was prepared using the merged rainfall data of CHIRPS(https://www.chc.ucsb.edu/data/chirps).

The

ClimateHazards

Group

InfraRed

Precipitation with Station Data (CHIRPS) is a 35+ year quasi-global rainfall data set
ranging from 1981 to near present. This data set incorporates o.050 resolution satellite
imagery and in-situ station data.
This plot of monthly rainfall clearly suggests that October-December months
contribute to maximum rainfall over Sri Lanka. Another smaller peak is observed in April
and May. This peak could be associated with the northward movement of ITCZ from theequator and associated convective activity, before the southwest monsoon sets in.
During the month of April and May, the ITCZ starts moving northwards and there could
be large scale convective activity associated with this movement due to low-level
convergence and abundant moisture content.
For the rest of this chapter, we refer to October to December as the northeast
monsoon (NEM) season, consistent with the designation of the India Meteorological
Department (IMD).
Domroes and Ranatunge (1992) have identified three types of orthogonal
structure of the monsoon regime in Sri Lanka using long-term mean monthly rainfall
data. Their analysis revealed that a large amount of rainfall occurs from March to
October in the southwestern parts of Sri Lanka, from December to February in theeastern parts, and in November in the northern and mid-western parts. Orthogonal
factor scores for the first three factors account for 93.6% of the total variance of mean

157

monthly rainfall. Seasonal changes in the monsoon wind system, ITCZ weather
phenomena, and topography are the main factors which influence the spatial structureof monsoon rainfall over Sri Lanka.

Fig. 7.3. Mean Monthly rainfall (mm/day) averaged over Sri Lanka using CHIRPS data.

Nisansala et al. (2019) analyzed Sri Lanka rainfall using the data during the period
1987-2017. They found increasing trends in annual rainfall at 24 stations with fivestations showing significant increasing trend. Annual rainfall at 13 locations (35%)
showed decreasing trend, but these trends were not significant (p < .05). There is anincreasing trend at 76, 51, 32, and 86% of stations during the First Inter-Monsoon (FIM),
Second Inter-Monsoon (SIM), South West Monsoon (SWM), and North East Monsoon(NEM) seasons, respectively. In general, the eastern, south eastern, north and northcentral regions of the country showed increasing rainfall trend over the last 31 years(1987–2017) while western, part of north western and central part of the country
indicated a decreasing rainfall trend during the same period. The annual trend in rainfall
is shown in Fig. 7.4 below.
158

Fig. 7.4. Spatial distribution of linear trends of annual rainfall for the period 1987-2017.
This plot is taken from Nisansala et al. (2020).

In Sri Lanka, convective activity with lightning contributes abundant quantity ofrainfall. Jayawardhana et al. (2014) studied the lightning activities over Sri Lanka using
data Lightning Imaging Sensor of TRMM Satellite. The period 1998-2012 was used forthe analysis. The highest occurrence of lighting activities is confined to the highly
populated western part of the island while the south eastern and mountain areas have
low occurrences. There is a clear spatial polarization of lightning activities during thesouth-west and north-east monsoon seasons. There is an increasing trend in lightning
activities, they appear to be increasing by 50 flashes per year. It has a seasonal
dependency with the south-west and first inter-monsoon seasons having the higher
increase. The percentage of lightning per month is highest (46%) during the Intermonsoon-1 period and the lowest (7%) during the Northeast monsoon season. Second
highest percentage of lightning per month (25%) can be observed during the intermonsoon-2 period followed by the Southwest monsoon (22%) period.
159

During the Oct-Dec season, Sri Lanka receives rainfall, often heavy rainfall due towestward moving depressions and tropical cyclones over the south Bay of Bengal. Fig
7.5 shows the tracks of depressions and tropical cyclones which crossed the coasts of Sri
Lanka during the October-December season for the period, 1971-2021.

Fig. 7.5. Tracks of Depressions and tropical cyclones crossing the coasts of Sri Lanka
during the period Oct-Dec, 1971-2021.

7.1. Diurnal Variations.
An analysis of diurnal variations over Sri Lanka was made using sub-daily TRMM
data set for the period 1998-2019. Similar results for the NE monsoon season over thesouth peninsula are discussed in Chapter-6 (section 6.1). The same methodology wasfollowed for this analysis also.
Fig. 7. 6 shows the three hourly mean rainfall over Sri Lanka and adjoining region,
averaged during the period 1998-2019. The plot clearly shows that there is significant
diurnal variation of rainfall over Sri Lanka and neighborhood during the NE monsoon
season. The changes are more evident over the western parts of Sri Lanka, where160rainfall peaks during the evening time (09-12 UTC). Over the eastern parts and adjoining
oceanic area, there is hardly any diurnal variation, but a major peak during the earlyhours (00-03 UTC).

Fig. 7.6. Three hourly mean rainfall over Sri Lanka and neighborhood, averaged using the
TRMM satellite data of 1998-2019.

The similar results are revealed in the Phase diagram obtained from the Harmonic
analysis as shown in Fig 7.7. The phase diagram shows clear peaking of rainfall during
evening and early night hours, which is consistent with Fig 7.6. This peaking could be
associated with the solar heating during day time and initiation of convection. Morestudies are required to establish the physical mechanisms for this observed diurnal
pattern. Also, it is important to understand whether the NWP model is capable ofpredicting this observed diurnal pattern over Sri Lanka.
161

Fig. 7.7. The phase diagram of the harmonic analysis of hourly rainfall using the satellite
data of 1998-2019. The phase diagram shows the time (IST) of maximum rainfall.

7.2. Intra-seasonal variations
There are not many studies addressing the intra-seasonal variability of the NE
monsoon rainfall over Sri Lanka, which is an important component of rainfall variability.
Therefore, a preliminary analysis on the intra-seasonal variability of rainfall over Sri
Lanka is done and the results are discussed below.
Fig. 7.8 shows the daily rainfall averaged over Sri Lanka during the period 1 Oct
2018- 31 Jan 2019 (above) and 1 Oct 2019- 31 Jan 2020 (below). These two years are
selected just as examples to show the rainfall variations within the season. The plotsuggests that there is significant rainfall variability within the season, with specificperiods of more rainfall, interspaced with little or no rains.

162

To examine whether daily rainfall has particular periodicity, a spectral analysis ofdaily rainfall was made for both the years, 2018-2019 and 2019-2020. The results areshown in Fig. 7.9.

Daily Rainfall 2018-2019
60

Rainfall (mm)

50
40
30
20
10

28/Jan/19

21/Jan/19

14/Jan/19

07/Jan/19

31/Dec/18

24/Dec/18

17/Dec/18

10/Dec/18

03/Dec/18

26/Nov/18

19/Nov/18

12/Nov/18

05/Nov/18

29/Oct/18

22/Oct/18

15/Oct/18

08/Oct/18

01/Oct/18

0

Daily Rainfall 2019-2020
50
45

Rainfall (mm)

40
35
30
25
20
15
10
5
0

Fig. 7.8. Daily rainfall (in mm) averaged over Sri Lanka during the period 1 Oct 2018- 31
Jan 2019 (above) and 1 Oct 2019- 31 Jan 2020 (below).
163

Fig. 7.9. The spectral analysis (Lomb-Scargle periodogram) of daily rainfall for the two
seasons. 2018-2019 (above) and 2019-2020 (below). The 95% significance level is shownas horizontal line in both the plots.

The results indicate that there is a strong periodicity of about 15-20 days, whichis statistically significant. The physical mechanisms of such periodicity are immediately

164

not known. More studies are required to understand the intra-seasonal variability ofrainfall over Sri Lanka and to examine the skill of its predictions using NWP models.
7.3. Inter-annual variations
A detailed analysis of year to year variations of NE monsoon over Sri Lanka is
discussed in this section. There are only a few studies on the inter-annual variability of
NE monsoon rainfall over Sri Lanka.
Suppiah (1996) studied the spatial and temporal variations in the relationships
between the Southern Oscillation Index (SOI) and rainfall over Sri Lanka. Major changes
in spatial patterns of correlations between seasonal rainfall and the SOI have occurredin Sri Lanka during the Southwest monsoon (SWM) and Second inter-monsoon (SIM)seasons. The periods of strong positive (negative) correlations during the SWM season
coincide with weak (strong) negative correlations during the SIM season. This
contrasting pattern is clear when the Indian and Sri Lankan summer monsoon rainfalls
were out of phase between 1900 and 1960, but not before 1900, or after 1960. The
sudden change in correlations around 1960 suggests a change in the coupled ocean–
atmosphere system that dominates the climate of these regions.
Suppiah (1997) studied the extremes of the Southern Oscillation (SO)Phenomenon over the equatorial Pacific and Sri Lanka rainfall. There were 27 El Niño
and 22 La Niña events, during the period from 1881 to 1990. Positive and negative
rainfall anomalies during the south-west monsoon (SWM) season are associated with La
Niña and El Niño events, but negative and positive rainfall anomalies are linked to La
Niña and El Niño events during the second intermonsoon (SIM) season. These
contrasting patterns are dominant in the dry zone of Sri Lanka.
Zubair and Ropelewski (2006) reported that the relationship between ENSO andthe northeast monsoon (NEM) in south peninsular India and Sri Lanka from October toDecember has not weakened. The mean circulation associated with ENSO over thisregion during October to December does not show the weakening evident in the165summer and indeed is modestly intensified so as to augment convection. The
intensification of the ENSO–NEM rainfall relationship is modest and within the historical
record but stands in contrast to the weakening relationship in summer. There ismodestly intensified convection over the Indian Ocean, strengthening of the circulation
associated with ENSO (Walker circulation), and enhanced rainfall during El Niño
episodes in a manner consistent with an augmented ENSO–NEM relationship.
Zubair et al. (2009) studied the predictability of Sri Lankan rainfall based on ENSO.
The El Niño-Southern Oscillation (ENSO) is a primary mode of climate variability of thisarea. They found that the rainfall is modestly predictable based on ENSO during
January–March, July–August and October–December. El Niño typically leads to wetter
conditions during October to December and drier conditions during January to Marchand July to August on average. The correlations of ENSO indices with rainfall arestatistically significant for October to December, January to March and July to August
and an analysis based on contingency tables shows modest predictability. The use of
ENSO indices derived from the central Pacific Ocean improves the predictability fromJanuary to June. The predictability based on ENSO for October to December rainfall isrobust on a decadal scale.
Abeysekera et al. (2019) studied the relationship between ENSO and rainfall over
Sri Lanka. The results clearly revealed a significant reduction of rainfall during both FirstInter Monsoon (FIM) and North East Monsoon (NEM) seasons during the El Niño years.
The Second Inter Monsoon (SIM) season showed a positive anomaly of rainfall during
the El Niño years. However, the effect of El Niño condition on the Southwest Monsoon
season (SWM) was not consistent. During La Niña conditions, an above normal rainfall
was observed in the FIM, SWM and NEM seasons where the strongest correlation wasevident during the NEM season. The SIM season has shown a below normal rainfall
during the La Niña period.

166

Using the seasonal (OND) rainfall over Sri Lanka, a detailed analysis on the interannual variability and its teleconnections with ENSO and IOD has been carried out. Fig
7.10 shows the year to year variations of NE monsoon rainfall over Sri Lanka, expressedas percent deviations. The OND mean monsoon rainfall (1971-2021) is 822 mm with
23% as coefficient of variation. Therefore, Sri Lanka receives much more rainfall during
the NE monsoon season, compared to the south peninsula and even comparable withthe southwest monsoon rainfall over India for four months (June to Sept), which is
870mm.
The green (red) lines suggest excess (deficient) monsoon years. During theperiod, 1961-2021, there were 12 deficient monsoon years and 10 excess monsoon
years. In 1980s and 1990s, Sri Lanka experienced more deficient years. During therecent decade there were four excess years and two deficient years.

NEM Rainfall
40
20
0
-20
-40
-60

1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021

Seasonal rainfall (%dep)

60

Year

Fig. 7.10. The inter-annual variation of NE monsoon rainfall (OND) over Sri Lanka,
expressed as percent departures.

167

A spectral analysis of seasonal rainfall is made and the results are given below.
Fig. 7.11 a shows the spectral normalized power (Lomb-Scargle periodogram) using thedata of 1961-2021. It suggests a peak around 2-3 years, but this periodicity is statisticallynot significant. The wavelet analysis shows the periodicity of 2-4 years during 1990-2000
and in the recent years, 2010-2020 as shown in Fig. 7.11 b.

Fig. 7.11. Spectral analysis of NE monsoon seasonal rainfall (1961-2021). a) the
normalized power using the Lomb-Scargle periodogram and b) wavelet power spectrum.
The 95% significance cone is also shown in Fig. 7.11 b.

168

Further, to explore the teleconnections with global forcings like ENSO, IOD etc, aspatial analysis of correlations between the NE monsoon rainfall and SST, OLR and
850 hPa zonal winds was done and the results are shown in Fig. 7.12. There is significant
correlation between NE monsoon rainfall and SST over the equatorial Pacific (Fig. 7.12 a)and the equatorial Indian Ocean (Fig. 7.12 b), representing the influence of ENSO and
IOD events. Please note the dipole like correlation patterns over the equatorial Indian
Ocean with negative (positive) correlation over the east equatorial Indian Ocean. Thus,
the NE monsoon rainfall is positively correlated to ENSO and IOD. An El Nino event andpositive IOD event are likely to enhance seasonal rainfall over Sri Lanka. This
relationship is further seen in Fig. 7.12 c and d. The NE monsoon rainfall over Sri Lanka ispositively correlated with the zonal winds over the equatorial east Pacific, but negatively
correlated with the zonal winds over the equatorial west Pacific Ocean. The same kindof relationship exists with OLR also, suggesting the strong influence of ENSO and IOD
events.

Fig. 7.12 a. Spatial distribution of correlations between OND NE monsoon rainfall and
Sea Surface Temperatures (SST) during the period 1961-2021.

169Fig. 7.12 b. Spatial distribution of correlations between OND NE monsoon rainfall and
Sea Surface Temperatures (SST) during the period 1961-2021.

Fig. 7.12 c. Spatial distribution of correlations between OND NE monsoon rainfall and
850 hPa zonal wind during the period 1961-2021.

170

Fig. 7.12 d. Spatial distribution of correlations between OND NE monsoon rainfall and
OLR during the period 1961-2021.

Fig. 7.13 a shows the monthly variations of correlations between monthly Nino
3.4 and IOD index with OND NE monsoon rainfall over Sri Lanka. The plot shows theconcurrent and strong correlations between Nino 3.4 and IOD Index with monsoon
rainfall. Comparatively, IOD index has stronger correlation with NE monsoon rainfall
over Sri Lanka compared to the Nino 3.4 during the OND season. But what is more
important is that statistically significant correlations are observed even before the NE
monsoon season, especially with the Nino 3.4. For example, the Nino 3.4 is significantly
correlated right from May onwards with the NE monsoon rainfall over Sri Lanka.
Similarly, the IOD index during August and September months is significantly correlatedwith NE monsoon rainfall. These inferences suggest scope for long range predictabilityof NE Monsoon rainfall over Sri Lanka. More work is required to explore these
relationships and to develop useful long-range prediction methods.
171

Correlation CoefficientMonthly Correlations
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Month
Nino 3.4

IOD Index

21 year moving correlations
Correlation Coefficient

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Last of 21 yrs
Nino 3.4

IOD Index

Fig. 7.13. a) Monthly correlations between Nino 3.4 (Blue) and IOD index (red) with OND
NE monsoon rainfall. Data of 1961-2021 was used. b) the 21 year moving correlations
between OND Nino 3.4 (Orange colour) and OND IOD index with the OND NE monsoon
rainfall showing secular variations.
.

172

Fig. 7.13 b shows the 21-year running correlations between the NE monsoon
rainfall and the Nino 3.4 and IOD index, showing the secular variations of therelationships. It shows the relationship has been more or less very robust throughout
the analysis period (1981-2021). However, during the recent years, the correlation
strength is shown as decreasing, especially the IOD Index. More work is required tounderstand the physical reasons for this observed weakening of the relationships.

173

Chapter-8
Numerical Weather Prediction Guidance
Numerical Weather Prediction (NWP) Models are extensively used to prepare
forecasts for short to medium range forecasts (up to 10 days) and extended range
forecasts (up to one month). Over the years, the skill of NWP systems has improved
substantially due to a) better assimilation methods of more quantity of data including
non-conventional data b) higher resolution NWP models c) improved model Physics andd) better methods of post-processing of model output data. At the Ministry of Earth
Sciences (MOES), there are two global forecasting systems which are now operational.
The first system is based on the NCEP Global Forecasting System (GFS) model, which
IMD uses to prepare the forecasts, four times a day (0000, 0600, 1200 and 1800 UTC).
These are deterministic forecasts up to 10 days. The GFS model is a global model with
12 km horizontal resolution. Using the same model, an ensemble prediction system wasalso developed by IITM scientists and put on operational work by IMD. This system
operational since 2017, provides probability forecasts of different parameters. All these
IMD

forecast

products

areavailable

at

the

IMD

websitehttps://nwp.imd.gov.in/gfsproducts_cycle00_mausam.php.
Another operational system is used by NCMRWF, Noida for generating forecasts(both deterministic and ensemble) twice a day. The NCMRWF Unified Model (NCUM)
was implemented in 2012 with a grid resolution of 25km (NCUMG:V1) which was
upgraded to 17km (NCUM-G:V3) in 2015, 12km (NCUM-G:V5) in 2018. The present
version (NCUM-G:V6) of NCUM-G has a horizontal grid resolution of ~12 km with 70
levels in the vertical reaching 80 km height. An advanced data assimilation method ofHybrid 4-Dimensional Variational (4D-Var) is used for the creation of NCUM global
analysis. The NCMRWF forecast products are available at https://www.ncmrwf.gov.in/

174

There are not enough studies on systematic verification of weather forecasts for
the NE monsoon season, except the two reports brought out by NCMRWF for 2020 and
2021 seasons.
Salient aspects of verification of forecasts during November 2021 using the
NCMRWF model are given below.
November 2021 witnessed unprecedented rainfall activity over south peninsula
during the NE monsoon season. Fig 8.1 shows the monthly accumulated rainfall during
November 2021 and its anomalies. This shows above normal rainfall activity over South
Peninsula with more than 100 mm excess rainfall over parts of Tamil Nadu andRayalaseema.

Fig. 8.1. Observed monthly rainfall (IMD) during November 2021 (left) and its anomaly(right). Unit: in mm.

Fig. 8.2 shows the vertically integrated moisture transport (VIMT) integrated up
to 300 hPa level. The NCUM analysis shows strong easterlies bringing abundantmoisture towards south Peninsula. However, the NCUM model predictions
underestimated this large-scale moisture transport towards South Peninsula. The
175

anomalies are much bigger in Day-5 forecast. The moisture transport was much weaker
than observed. This has resulted in negative bias in rainfall over south Peninsula(Fig. 8.3). Rainfall predicted by the model was less than that observed, even though themodel predicted the spatial distribution of rainfall very well. The underestimation of
rainfall could be related to under-estimation of moisture transport to the region in thelower tropospheric levels.

Fig. 8.2. a) The NCUM model analysis of vertically integrated (up to 300 hPa) moisturetransport (VMT) during Nov 2021. The forecast error in VMT for day-1 (b), day-2(c) andday-3(d).
176

The Skill scores of forecast categories with the NCUM model are given in Fig 8.4.
The Skill Scores are defined as follows:
1) Probability of Detection (POD): hits/(hits+misses)
2)

False Alarm Rate (FAR)= False alarms/ (Hits+false alarms)

3) Critical Success Index (CSI)= Hits/ (hits+false alarms+misses)
4) BIAS= (hits+false alarms)/(total number of observed events)
5) Pierce’s Skill Score (PSS)= hits/(hits+misses) – false alarms/(false alarms+correct
negatives)
6) Symmetric Extreme Dependency Index (SEDI) is defined as
SEDI = (log F-log H-log (1-F)+log (1-H))/ (log F+ Log H+ log(1-F)+log (1-H)
Where, H is Hit rate and F is false alarm rate.
Probability of Detection (POD) is larger for lower rainfall thresholds, but it
drastically reduces for larger amount of rainfall. POD for 2 cm rainfall at Day-1 forecast
is close to 0.30, but it is around 0.1 for Dady-5 forecast. False Alarm Rate (FAR) is larger
for higher amount of rainfall thresholds. FAR is more than 0.7 for larger amount of
rainfall. Therefore, the model has tendency of over predicting frequency of higher
amount of rainfall. The other skill scores (like CSI, BIAS, PSS and SEDI) are better for Day1 and Day-3 forecasts but reduces sharply for Day-5 forecasts.
Next, a discussion is made on the fidelity of the NCUM model in predicting two
weather systems which developed in November 2021 and affected South Peninsula. The
first weather system was a low-pressure system that formed over the Bay of Bengal andmoved northwestwards. It crossed the border of Tamil Nadu and Andhra Pradesh on 12
November 2021.

177

Fig. 8.3. Observed and Model Forecast Mean rainfall (mm/day) during November 2021.
a) Observed merged rainfall in mm/day. b) to d) represent model forecasts for day-1 today-3. Bottom three plots show model forecast errors for day-1 to day-3 forecasts.

178

Fig. 8.4. Statistical Skill Scores for the NCUM forecasts of rainfall during November
2021, a) POD b) FAR c) CSI d) BIAS e) PSS and f) SEDI for day-1 to day-3 forecasts.

Fig. 8.5 shows the 850 hPa wind analysis of 11 Nov 2021 and NCUM forecasts up
to 120 hrs in advance. The low pressure system with cyclonic circulation was clearlyseen in the analysis off Tamil Nadu coast on 11 November. It is interesting to see the179

NCUM model is able to predict the presence of cyclonic circulation off the Tamil Nadu
coast with similar intensity up to 72 hours in advance. However, beyond 72 hours,
model could not predict the cyclonic circulation correctly. Accordingly, the rainfall
activity is also well predicted by the model up to 72 hours in advance (Fig 8.6). Beyond
72 hours, the model under-predicted the intensity of rainfall and the model forecasts
have shown large biases.
Fig. 8.7 and Fig. 8.8 show the same details but for another weather system whichcrossed the land near north Andhra Pradesh on 19 Nov 2021. As in the previous case,the model could predict the presence of cyclonic circulation off Andhra coast up to 72
hours in advance. Rainfall was also well predicted by the model up to 72 hours inadvance.

Fig. 8.5. NCUM model analysis of 850 hPa winds at 0000 UTC, 11 November 2021 (a) andmodel forecast for day-1 (b), day-2 (c), day-3 (d), day-4 (e) and day-5 (f).

180

Fig. 8.6. Observed and NCUM model predictions of rainfall (cm) for 03 UTC 11 November
2021. a) observed, b) to f) represent forecasts of day-1 to day-5.

Fig. 8.7. NCUM model analysis of 850 hPa winds at 0000 UTC, 19 November 2021 (a) andmodel forecast for day-1 (b), day-2 (c), day-3 (d), day-4 (e) and day-5 (f).
181

Fig. 8.8. Observed and NCUM model predictions of rainfall (cm) for 03 UTC 19 November
2021. a) observed, b) to f) represent forecasts of day-1 to day-5.

Thus, the NCUM models have shown some useful skill in predicting large scale
circulation and rainfall patterns associated with weather systems during the NE
Monsoon season, at least up to 72 hours in advance.
In the next section, analysis of forecast verification using IMD/IITM GFS and GEFS
models is presented.

182

The categorical statistical scores for verification of precipitation forecasts basedon GFS T1534 for south peninsula are shown in Fig 8.9 for Day-1, Day-3 and Day-5
forecasts. This verification is made for three NE monsoon seasons, 2019-2021.
Probability of Detection (POD) is larger for lower rainfall thresholds, but it
drastically reduces for larger amount of rainfall. POD for 2 cm rainfall at Day-1 forecast
is close to 0.35, but it is around 0.2 for Dady-5 forecast. False Alarm Rate (FAR) is larger
for higher amount of rainfall thresholds. FAR is more than 0.7 for larger amount of
rainfall. Therefore, the model has tendency of over predicting frequency of higher
amount of rainfall. The other skill scores (like CSI, BIAS, PSS and SEDI) are better for Day1 and Day-3 forecasts but reduces sharply for Day-5 forecasts.

Fig. 8.9. Categorical skill scores for rainfall forecast of OND 2019-2021 from GFS T1534
for the Southern Peninsula region (8°-20°N, 68°-97.5°E, land only)

183

Fig. 8.10 shows the Reliability Diagram of prediction of rainfall during the NE
monsoon season (OND) (2019-2021) from the model GEFS T1534 for the southern
Peninsula region (8°-20°N, 68°-97.5°E, land only). The observed merged (satellite-raingauge) rainfall data are taken from Mitra et al. (2009). The columns are for Day 1, 2,
and 3, respectively, and the rows are for rainfall thresholds of 2.5 mm/day, 15.6mm/day, and 65.5 mm/day, respectively. A reliability diagram is a graph where theconditional distribution of the observations, given the forecast probability, is plottedagainst the forecast probability. The distributions of perfectly reliable forecasts areplotted along the 45- degree diagonal. If the curve falls in the grey-shaded region, it
indicates a skilful forecast. The curve obtained below the diagonal indicates overforecasting and vice-versa. Fig 46 shows that the model can display a skilful forecast
which decreases with increasing thresholds. For a threshold of 2.5 mm/day rainfall, theforecast shows a consistent skill through Day 1 to 3 as the curve is in the grey shaded
region of the graph. For the threshold of 15.6 mm/day of rainfall, the curve falls justbelow the grey region for higher forecast probability categories for all lead times. For
the threshold of 65.5 mm/day, the Day 1 curve shows a comparatively reliable forecast.
However, the type of curve obtained for Day 2 and 3 forecasts indicates under-sampling.
As all the curves are below the diagonal, we can say that the model forecast is overconfident or under-spread.
Fig. 8.11 shows the Relative Operating Characteristic (ROC) for the rainfall
forecast of OND 2019-2021 from the model GEFS T1534 for the Southern Peninsula
region (8°-20°N, 68°-97.5°E, land only). The columns are for Day 1, 2, and 3, respectively,
and the rows are for rainfall thresholds of 2.5 mm/day, 15.6 mm/day, and 65.5 mm/day,
respectively. The ROC is conditioned on the observations and gives a measure of theresolution of the forecast. Here resolution of the model forecast means thedistinguishing capacity between events and non-events. The ROC shows the forecast interms of Hit Rate and False Alarm Rate. The perfect forecast displays the curve along the184bottom left to the top left and then to the top right corner. A curve along the diagonal
indicates no skill, and a curve below the diagonal shows negative skill. Fig. 8.11 shows anear-perfect ROC for the 2.5 mm/day threshold and at all lead times. The skill decreases
for the 15.6 mm/day rainfall threshold, but there is a considerable dip in skill for the
65.5 mm/day threshold, but it still shows a positive skill of the forecast. Hence the ROC
gives a measure of the potential skill of the forecast.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 8.10. Reliability Diagram for rainfall forecast of OND 2019-2021 from GEFS T1534
for the Southern Peninsula region (8°-20°N, 68°-97.5°E, land only). The columns are for
Day 1, 2 and 3 respectively and the rows are for rainfall thresholds of 2.5 mm/day, 15.6mm/day and 65.5 mm/day respectively.

185

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 8.11. Relative Operating Characteristic (ROC) for rainfall forecast of OND 2019-2021
from GEFS T1534 for the Southern Peninsula region (8°-20°N, 68°-97.5°E, land only). The
columns are for Day 1, 2 and 3 respectively and the rows are for rainfall thresholds of
2.5 mm/day, 15.6 mm/day and 65.5 mm/day respectively.

In the earlier section, it was shown that the deterministic forecast skill is reduced
for rainfall of larger thresholds. Since we need to predict heavy rainfall events,
depending upon the deterministic forecasts alone will not be fruitful. We need to refer
to Ensemble forecasts to understand the uncertainties involved in the forecasts andthus to prepare probabilistic forecasts.

186

An ensemble weather forecast is a set of forecasts that present the range of
future weather possibilities. Multiple simulations are run, each with slight variation of its
initial conditions and with slightly perturbed weather models. These variations
represent the inevitable uncertainty in the initial conditions and approximations in themodels. They produce a range of possible weather conditions. The uncertainty
associated with every forecast means that different scenarios are possible and theforecast should reflect that. Single “deterministic” forecasts can be misleading as they
fail to provide this information.
Under the Monsoon Mission project of the MoES, two ensemble forecasting
systems have been developed and put on operational use. One system is based on UK
Met office weather prediction model and the second one is based on the NCEP Global
Forecasting System (GFS) weather prediction model. The details of these weather
prediction models are available in the papers by Sarkar et al. (2016), Chakraborty et al.
(2020), Deshpande et al. (2020, and Mukhopadhyay et al. (2022).
Here, a case study is discussed to demonstrate the utility of ensemble weather
forecasts during the NE monsoon season. The case study pertains to a heavy rainfall
event (exceeding 10 cm) which occurred over Tamil Nadu-South Andhra coast on 7 Nov
2021. Fig. 8.12 a, b and c show rainfall forecast from Global Ensemble Forecasting
System (GEFS) T1534 model of IMD/IITM valid for 7 Nov 2021 for Day-1, Day-3 and Day5 forecasts. The IMD-GPM observed rainfall is shown in Fig. 8.12 a, which shows heavy
rainfall over the east coast of Tamil Nadu/Andhra coast. The forecasts show higher
probabilities of rainfall of even 15.6 mm/day. It is interesting to note the GEFS model
indicated a chance of heavy rainfall exceeding 65 mm/day even at Day-3 and Day-5
forecasts. This ensemble forecast product thus provides an additional tool forforecasters on the possibility of extreme events like heavy rainfall and therefore such
ensemble forecast products should be extensively used.

187

(a)

(b)

(d)

(e)

(c)

Fig. 8.12 a. Day 1 rainfall forecast from GEFS T1534 valid for 7 November 2021 with (a)IMD-GPM (b) Ensemble mean and forecast showing probability of exceedance from (c)15.6 mm/day (d) 65.5 mm/day (e) 115 mm/day rainfall threshold.

188

(a)

(b)

(d)

(e)

(c)

Fig. 8.12 b. Day 3 rainfall forecast from GEFS T1534 valid for 7 November 2021 with (a)IMD-GPM (b) Ensemble mean and forecast showing probability of exceedance from (c)15.6 mm/day (d) 65.5 mm/day (e) 115 mm/day rainfall threshold.

189

(a)

(b
)

(d)

(e)

(c
)

Fig. 8.12 c. Day 5 rainfall forecast from GEFS T1534 valid for 7 November 2021 with (a)IMD-GPM (b) Ensemble mean and forecast showing probability of exceedance from (c)15.6 mm/day (d) 65.5 mm/day (e) 115 mm/day rainfall threshold.

Thus, from the above discussions, we can conclude that the present-day NWP
model forecasts have some useful skill in predicting various aspects of the NE Monsoon
season. Forecasters should make use of the NWP model forecasts along with otherforecasting tools like satellite pictures, radar data etc for preparing more skillfulforecasts during the NE monsoon season. The ensemble forecasts are very useful for
preparing probability forecasts to account uncertainties involved in the forecasts.
190

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