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Understanding convection features over Bay of Bengal using SST and
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atmospheric variables
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R. Uma, T.V. Lakshmi Kumar and M.S. Narayanan
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Department of Physics, SRM University, Kattankulathur, Tamilnadu, India
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Abstract
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Tropical Oceanic region are frequently prone to deep convections. Hence it is very
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essential to understand the features of convection with the help of oceanic and atmospheric
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variables such as SST, OLR, Rainfall, relative humidity, Columnar Water Vapour (CWV) etc
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and the linkage among them. In our present study, we have divided the Bay of Bengal (BoB)
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into ten different sub regions (SR) and have attempted to study the connection between the above
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stated variables during convective and non-convective events in the southwest monsoon (SWM)
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season (June to September) for the period 1998 - 2010. The monthly behaviour of SST / OLR
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decreased by 0.5 °C / 14 W/m2 from May to June and increased by 0.1 °C / 7 W/m2 from
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September to October. Among ten SRs, SR 5 and SR 10 are observed to be coldest and warmest
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respectively based on the SST variations. Intra-seasonal oscillations of the above mentioned
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variables show the influences of quasi bi-weekly oscillations (QBWO) and Madden Julian
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Oscillations (MJO). As the threshold values for SST, OLR and rainfall were already reported, we
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have drawn our attention to deduce a threshold value for water vapour in lower level troposphere
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(Water vapour density (WVD) at 850 mb) which highly influences the convection. In arriving at
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a threshold of low level water vapour, we have analysed the convective and non-convective
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events of each central 1⁰ x 1⁰ grid in all the SRs for the period from 1998 to 2010, along with
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water vapour scale height. Our analysis inferred that the low level water vapour density at
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850 mb varied above 12 g/m3during convective days and below 12 g/m3during non-convective
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days. We noticed that the variability in water vapour density is more in non convective days than
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in convective days over BoB. The results of the study may be useful to understand the water
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vapour dynamics with SST, OLR and rainfall.
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Keyword: Sea Surface Temperature, OLR, water vapor density, Convection, and Bay of Bengal
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Author for correspondence:
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Dr.T.V. Lakshmi Kumar
Assistant Professor
Department of Physics
SRM University
Kattankulathur, Tamilnadu, India – 603 203
E mail : lkumarap@gmail.com
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1 Introduction
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Convection is a physical process which makes the earth liveable by preventing the earth
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from overheating due to solar radiation through vertical transport of heat and moisture in an
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unstable atmosphere. This is the principle mechanism for the formation of clouds and
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atmospheric circulation, which is responsible for the redistribution of heat from warm equatorial
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regions to higher latitudes. Over the globe, oceans are dominant heat sources for the earth’s
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troposphere as they are more capable of absorbing, retaining and transferring heat when
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compared to land (Waliser and Graham 1993). Tropical Oceans are observed to be the regions
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prone to frequent deep convections (Vinayachandran and Shetye et al. 1991). Hence, it is very
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essential to understand the features of convection with the help of oceanic and atmospheric
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variables such as SST, OLR, rainfall, relative humidity, CWV and linkage among them.
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However, the basic element which drives the convection is the vertical transport of wind over a
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specific region. The data provided by the satellite sensors of multiple channels covering the
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whole ocean at finer saptio-temporal scale is of immense help to understand the convection
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features.
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Many aspects of relationship between SST and large scale convective systems using OLR
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for deep convection has been revealed by several researchers (Waliser and Graham 1993, Zhang
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1993). One of the major outcome of their studies is that intense deep convection occurs more
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frequently when the SST values are in the range of 27 – 29 °C (Gadgil et al. 1984; Sud et al.
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1999). They have used OLR (OLR<240 W/m2) as a tool to identify the location of deep
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convection and reported that both intensity as well as the occurrence of deep convection
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diminishes as SST increases beyond 29.5 °C.
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Also, a few investigations had been carried out to infer the connection of SST with water
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vapour at different levels of troposphere and deep convection, ( Holloway and Neelin et al. 2009;
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Sherwood 2010) as water vapour involves in transfer of heat and moisture through updraft and
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downdraft. The linkage of heavy rainfall events in relation to SST and water vapour as a
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resultant of convection were also reported (Shankar et al. 2007; Zelinka and Hartman 2009).
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Sherwood (1999a) reported that the humidity of lower troposphere is an important precursor for
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initiation of deep convection in tropical western pacific. By conditionally averaging the
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precipitation on CWV, Bretherton (2004) has noticed a sharp rise in rainfall at relatively high
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CWV. Behaviour of moisture in terms of humidity is highly variable both vertically from lower
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troposphere (850 mb) to upper (200 mb) troposphere and horizontally from warm pool to cold
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pool when compared to temperature. Thus, positive feedback of water vapour plays a major role
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in initiation and sustainability of deep convection.
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As BoB is one of the most important heat source region of tropics and is prone to
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frequent cyclonic activities, we have attempted to understand the features of convection over
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BoB (0° to 20° N and 82° to 97° E) with the help of oceanic and atmospheric variables, such as
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SST, OLR, Rainfall and CWV. In our present work, we first studied the variations in SST, OLR
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during SWM season with an emphasis on convective and non- convective events. Further, we
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have used wavelet analysis to study the intra-seasonal oscillations and focused on changes of
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water vapour during convection with an aim to report threshold for water vapour of lower
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troposphere on convective days over BoB.
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2 Data and Methodology
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The present study makes use of gridded SST, OLR, rainfall data sets for the analysis. The
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authors
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http://www.esrl.noaa.gov/psd/data/gridded/data ) and TMPA 3B42 V6 rainfall data sets (Data
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source: http://disc.gsfc.nasa.gov ) on daily basis at a spatial resolution of 1° x 1° by re-gridding
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the data sets which are available at a finer spatial resolution of 0.25° x 0.25°. CWV in the free
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troposphere (850 – 500 mb) has been calculated by integrating the ERA interim (Data source:
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http://apps.ecmwf.int/datasets/data/interim-full-daily ) specific humidity data from the 850 – 500
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mb using Trapezoidal rule (maintaining the same spatial resolution of 1° x 1°).Daily OLR values
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provided by CDC – NOAA (Data source: http://www.esrl.noaa.gov/psd/data/gridded/data ) at a
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spatial resolution of 2.5° x 2.5°, were used as a proxy for identification of convective event.
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These data sets are being widely used to study the atmospheric and oceanic processes (Shankar
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2007, Holloway and Neelin et al. 2009).
have
used
NOAA
Optimally
Interpolated
SST
(Data
source:
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The present study is confined to SWM season of India from 1998 – 2010. Ten 5° x 5°
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SRs over Bay of Bengal (0° to 20° N and 82° to 97° E) are chosen such that each one of the SR
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comprises of twenty five 1° x 1° grids (Fig.1). The analysis commenced by obtaining the daily
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and monthly mean values of OLR, SST, rainfall and CWV for each of the ten 5° x 5° SRs and
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study region as a whole. To study the Interasrasonal Variability (ISV) of the above mentioned
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parameters wavelet analysis has been used. Wavelet transforms (WT) is a significant tool to
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extract the multi-frequency non-stationary powers of a time series. Wavelets is a family of
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functions constructed from translations and dilations of a single function called the "mother
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wavelet"ψ(t). Morlet wavelet which is used as mother wavelet in our present study excels over
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the other wavelets due to its ability to detect both time-dependent amplitude and phase for
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different frequencies exhibited in the time series. It is defined by
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𝝍𝒂,𝒃
(𝒕)
=
𝟏
√|𝒂|
𝒕−𝒃
𝝍(
𝒂
),
𝒂, 𝒃𝝐𝑹, 𝒂 ≠ 𝟎
(1)
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where, a and b are scaling and translation parameter which measures the degree of
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compression and translation respectively which determines the time location of the wavelet.
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Wavelets have time-widths adapted to their frequencies. Wavelet transforms is significantly
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applied in analysing non-stationary signal when compared to Fourier transform. Remarkable
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difference between Fourier transform and Wavelet transform is that wavelets are well localized
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in both time and frequency domain whereas the former is only localized in frequency domain.
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Capability of Wavelet theory to reveal aspects of data such as trends, breakdown points, and
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discontinuities in higher derivatives and self-similarity makes it as an excellent signal processing
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technique which is missed by other signal analysis techniques (Sifuzzaman et al. 2009).
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Theoretical details of wavelet analysis can be referred from Daubechies (1992)”
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3 Region of interest
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BoB is prone to frequent monsoon depressions and cyclonic disturbances. Hence it is
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very important to study the ocean- atmosphere interaction over this region which helps to
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understand the cyclogenisis, estimation of cyclone intensity etc. The study region (BoB) is
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proven to be having a non-linearity between the precipitation and SST and there is a spatially
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variable lag relationship between them (Roxy et al. 2013). Over the Arabian Sea, surface
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convergence is strong enough to uplift the moisture which results in fast local convection where
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as in BoB it is relatively weak hence the response in SST to the precipitation anomaly is slower.
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Roxy (2014) have attempted to elucidate the relation between SST and rainfall in a large scale by
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considering three different regions in Arabian Sea, BoB and South China Sea and concluded that
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the lag between these two variables must be considered for the better understanding of the
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convection phenomenon. It is also reported that the existence of upper threshold (29 °C) of SST
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for convection as reported by Gadgil et al. (1984) is uncertain and convection takes place even at
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SST greater than 30 °C (Roxy 2014). However, Roxy (2014) has not considered the threshold for
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rainfall as an outcome of convection in the analysis. In our present analysis we have divided the
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study region in to ten SRs where some regions are found to be more convective regions while
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others are as low convective ones. These 10 SRs are also distinctly varied in terms of rainfall and
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water vapor.
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4 Definition of a convective event
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We have considered a day as a convective day/event when all the three parameters discussed
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above namely SST, OLR and rainfall attain their threshold values. Their threshold values are
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mentioned below:
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
SST:
27.5 °C < SST < 29 °C – Gadgil et al. (1984)
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
OLR:
OLR < 240 W/m2 – Zhang et al. (1993).
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
Rainfall:
Rainfall > 20 mm – Shankar et al. (2007).
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It is to be noted that the authors of the present paper have considered the wind criteria prior to
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the above mentioned criteria in carrying out the work. The ascending wind at 500 mb is taken as
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convective event while the descending one is treated as non-convective.
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5 Results and Discussions
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5.1 Convective features of SST and OLR over BoB during SWM season
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The long term monthly mean (1998 - 2010) and standard deviation (SD) values of SST
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have been calculated and plotted for all individual SRs (for brevity sake, all the plots are not
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shown). From the mean values of SST, we observed that among the ten SRs, SR 5/ SR 10 is
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found to be the coldest/ warmest with a mean SST of 28.6 °C / 29.3 °C during SWM season
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(Fig.2). SR 4 is found to be the highly convective SR from the frequency count of the maximum
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number of convective events which has been elucidated in the forthcoming sections. The reason
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for SR 5 to be the coldest is attributed to the intrusion of southwest monsoon current in this SR
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during the period of May to September which cools the southern BoB (Shankar et al. 2007).
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Vinayachandran and Shetye (1991) studied the structure of warm pools of equatorial Indian
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Ocean (Arabian Sea and Bay of Bengal) and observed that the SST over the SR 10 of our present
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study to be having the maximum SST during SWM season of the order of 29 - 30 °C.
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Indian Ocean becomes the warmest area among the world oceans during April and May.
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As it encounters the onset of SWM season, the SST values reduce due to strengthening of wind
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circulation and remains in a range of 27 – 29 °C in the Bay during the months of June to August.
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Then SST starts increasing from the mid September (Shenoi et al. 2002). The fall of SST is
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found to be higher in SR 3 and 4 with 0.7 °C and 0.9 °C respectively as where SR 9 and 10 have
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recorded lower values of 0.2 °C and 0.1 °C respectively. There is no significant increase of SST
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in individual SRs from September to October. From the monthly mean and SD of all individual
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SRs (figure not shown for all SRs), it has been observed that the SRs which are in the latitudinal
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belt 5° – 20° N show a similar monthly behaviour as stated by Shenoi et al. (2002) and those
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which are very near to the equator belt show a decrement in SST values throughout May to
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October and seems to be warmer than other SRs with an SST range of 29 – 30 °C.
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OLR being the measure of upwelling thermal radiation, its variability is highly dependent
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on the SST variation. Lower/higher value of OLR indicates the presence of high cloud tops/low
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clouds or clear conditions (Waliser and Graham 1993). Due to the above mentioned reasons,
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OLR has been considered as a proxy to indentify deep convection in many studies to understand
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the convection mechanism (Zhang 1993). On this basis, we carried out a similar daily and
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monthly analysis with OLR data sets and observed that the monthly mean values of all ten SRs
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are below 240 W/m2 which is reported as the threshold for the occurrence of convection (Zhang
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et al. 1993). Fig.3 shows the monthly mean and SD of OLR for the cold SR (SR 5), warm SR
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(SR 10) and highly convective SR (SR 4). Among the ten individual SRs (figure not shown for
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all individual SRs), monthly mean OLR is observed to be lowest in SRs 3 and 4 and highest in
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SR 5 during the SWM season. From Fig.3 it can be noted that OLR declined (14 W/m2) from
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May to June, and increased (7 W/m2) from September to October . Alike SST, the monthly
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behaviour of OLR is also inferred to be uniform in the SRs 1 to 7 (5° to 20° N).
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5.2 Linkage between rainfall and convection
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Rainfall being considered as the major outcome of a convective event, we used it as a
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parameter to achieve our goal of understanding the features of convection. In the present study,
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we made use of the rainfall in framing the criteria for a convective event/day over BoB which is
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mentioned in the data and methodology section.
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Based on the criteria for a convective event, we have identified the number of convective
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days/events in all the individual SRs. Table 1 gives the information about the number of
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convective events that occurred in each 5⁰ x 5⁰ SR during SWM season of the study period.
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From Table 1, it is obvious that SR 4 is highly convective with a record of 266
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convective events followed by SR 1(Number of events: 246) and SR 3(Number of events: 163).
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These are the SRs located in the northern bay having the favourable conditions for deep
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convective event with daily mean SST (27 – 29 °C) and OLR (185 – 205 W/m2).These results
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show good agreement with the findings of Mooley and Shukla (1989) where, they reported that
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the formation of number of low pressure system exceeds in northern Bay than in the other part of
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the North Indian Ocean. Number of convective events are observed to be very low in the SRs 8,
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9 and 10 (Table 1). This could be due to the impact of higher SSTs beyond the threshold i.e
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above 29 °C in these SRs. The convective activity and intensity is reported to be decreasing with
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increasing SST beyond the threshold due to evaporative cooling (Zhang et al.1995) and super
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green house effect as reported by Imandar and Ramanathan 1994.
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5.3 Intraseasonal variability (ISV)
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It is reported that the ISV (10 – 60 day Oscillations) of SST generates favourable
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conditions for convective activity (Roxy and Tanimoto 2007). On this basis, we attempted to
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study the ISV of all the four variables over the study area. ISV of the SWM season is
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characterized by two dominant modes, one between 10 and 20 days (Quasi Bi-Weekly
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Oscillation (QBWO)) with a westward or north-westward propagation pattern over the monsoon
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region and the other between 30 and 60 days which exhibits a northward or north-eastward
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propagation called Madden Julian Oscillation (MJO), (Krishnamurti and Ardanuy 1980;
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Goswami and Ajayamohan 2001). We studied these special characteristic features by using
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wavelet power spectra.
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Wavelet analysis has been widely used to reveal the characteristics and temporal
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dynamics of various parameters like rainfall-river runoff (Lafreniere and Sharp 2002), large scale
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features of rainfall over Indian landmass (Uma et al. 2013), SST and South Asian Summer
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monsoon (Torrence and Compo 1998; Vianayachandran et al. 2012), and their relationship at
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diurnal, intra-seasonal (10 – 20day (QBWO), 30-60 day (MJO)), intra-annual (2-7 years ENSO)
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time scales. (Wang and Wang 1996)
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Time series of SST, OLR, rainfall and CWV are subjected to “Morelet” wavelet analysis
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for the period May to October to avoid the impact of edge effect. Fig.4 depicts the wavelet
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power (absolute value squared) spectra of SST (a and e), OLR (b and f), rainfall (c and g) and
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CWV (d and h) of the study region during a weak monsoon (2002) and an active monsoon
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(2007) respectively. The area under the black line is the cone of influence. From Fig.4 (a-h), we
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observe the presence of 30 – 60 days low frequency oscillations and 10 – 20 days high frequency
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oscillations in all the four parameters. The spectra of all the four parameters exhibit more or less
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similar pattern of maximum power in early June and the power slowly increases from 45 days
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(spread of monsoon) and extends up to 120 days (July – August major monsoon months) and
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starts decreasing as the season withdrawal takes place (Lawrance and Webster 2000).
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Strength of the ISV in SST and OLR are observed to be high during good monsoon
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(2007) than in a deficit monsoon (2002). Though the strength of the 10- 20 day oscillations of
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SST and OLR are low, they are observed to be significant with 95% confidence level. Study of
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Hareesh et al. (2001) on ISV in the central Bay during 1999 SWM season, have revealed the
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existence of 10 – 32 days oscillation by applying Fast Fourier Transform (FFT) to SST time
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series. The results of Han et al. (2006) on the impact of these sub monthly oscillations of wind
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and OLR has shown that it can cause significant 10-30 days changes in SST over equatorial
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basin and northern BoB. Spatially, this results with warming in the central western Indian Ocean
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basin and northern BoB, cooling in the eastern equatorial warm pool as well as southern Bay
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before the event peak. This higher amplitude of ISV of SST is attributed to the air-sea fluxes and
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role of salinity since BoB receives more amount of fresh water from river runoff and excess of
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rainfall (Hareesh et al. 2001).
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SWM seasonal rainfall is reported to be very high over BoB and the number of low
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pressure systems is also high when compared to Arabian Sea (Mooley and Shukla 1989). ISV of
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the rainfall is observed to be high over the north-eastern BoB (Vinayachandran et al. 2012).
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Coinciding with these findings, spectrum of rainfall over SRs 1, 3 and 4 (figure not shown) of
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our present study also exhibited a strong ISV with higher power.
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We also studied the wavelet power spectra of SST, OLR, rainfall and CWV of the highly
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convective area (SR 4) located in the north-eastern Bay during a poor monsoon (2002) and good
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(previously it was i) weak and active and ii) bad and good monsoon in few places, MSN sir
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wanted to denote it as poor and good hence I have followed the same)monsoon (2007) (figure
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not shown). Similar features are observed in the wavelet spectrum of CWV with distinct
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variations in its power. The localized variations of 10 - 20 day seemed to be existing in both
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good and poor monsoon years. The strength of the signal was found to be consistent in poor
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monsoon year 2002 than in the good monsoon year 2007. From the above analysis, we conclude
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that SST and other atmospheric variables have by and large similar variations over the study
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area. Though the wavelet spectra are capable in depicting the intra seasonal variations of SST
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and other atmospheric variables, the lead and lag of these parameters cannot be explored. It is
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also important to see that the SSTs over a region influence the convection or the advective winds
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modify the SSTs to cause deep convection. Bielli and Hartmann (2004) reported that the zonal
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winds at the coastal areas of BoB travel to deep ocean thus modifying the SSTs over that region
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to initiate convection. Similarly, Lanzante (1996) found that the SSTs maintained the lag
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correlation of 10 - 12 days with the wind and has the 1-12 days of lead with the rainfall over a
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region. Satheesan and Krishnamurthy (2005) studied the frequency spectrum coherence analysis
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to identify the lead-lag between the parameters of zonal and meridional wind and temperature
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and reported that they have 6 day and 12 day lag relation over Gadanki (13.5⁰ N, 79.2⁰ E). In the
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present study, we tried to examine the lead and lag of SST and CWV over a few 1⁰ x 1⁰ grid
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locations of the study area. We chose three 1⁰ x 1⁰ grids each from hot SR, cold SR and
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convective SR of the study area and subjected to FFT.
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Table 2 illustrates the lead and lag of SST/CWV during the study period over the above
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mentioned SRs. The lead variable is mentioned in the table and the hyphen represents the zero
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lead / lag. The lead lag showed that the CWV and SSTs maintained 6 to 10 days either lead or
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lag during the study period. In the cold SR, most of the years have witnessed CWV leading the
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SSTs. The SW monsoon winds from Arabian Sea converge at these regions causing the lower
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SSTs which in turn the CWV predominantly leads the SSTs. In the hot SR as well as in the most
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convective SR, the situation is mixed. In some of the years, CWV is leading and in the other
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years, SST showed the lead. There is no much lead-lag relation between CWV and SSTs during
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the years 2001 and 2002 of the study period. This might be due to the convection caused by local
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SSTs or the convection caused by advective winds.
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5.4 Relation between water vapor, rainfall and convection
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It is a crucial task to understand the spatio-temporal variation and relation of water
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vapour with convection. Free tropospheric (850 – 500 mb) moisture is reported to be more
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variable when compared to other layers of the atmosphere (Holloway and Neelin 2009). They
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found that moisture profile, conditionally averaged on precipitation over Nauru Island showed a
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strong association between rainfall and moisture variability in the free troposphere and boundary
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layer variability. This is also been confirmed from the observational studies of Brotherton et al.
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2004 where they have observed a sharp rise in the rainfall at higher CWV.
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We examined to see the percentage departure of CWV from the day of convection to the
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very next day of convection by taking each 1⁰ x 1⁰ grid of the 5⁰ x 5⁰ SR 1. We resulted with
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totally 2417 convective events in which 996 showed a positive anomaly and 1421 number of
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events showed negative anomaly which shows that generally there is a decrease in CWV just
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after the convective event. It is reported that intense precipitation events in the tropics are
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preceded by an increase in low level humidity. Convection removes water vapour from a very
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moist boundary layer through two processes i) precipitation and ii) transportation to the very dry
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upper troposphere, resulting in dramatic changes to the upper-level moisture (Zelinka and
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Hartmann 2009). This could be a probable reason behind the higher number of events showing
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negative anomaly during the very next day of convection in our present analysis.
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Understanding the change in the humidity at different altitudes during different stages of
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evolution of the convective events is very important. In the present study, we tried to delineate
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the behaviour of low level water vapour (850 mb) expressed in terms of water vapour density.
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Studies revealed that even small level change in the boundary layer moisture can greatly
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influence the initiation of convective activity (Keil 2008; Sherwood 2010). It has been stated by
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Weckwreth et al. (2005) and Fabry et al. (2006) from their radar reflectivity observations that
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enhanced low level moisture occurs prior to convection initiation. Most of the analysis are
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carried out by taking individual convective events like thunderstorm, hail, squall lines etc. It has
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to be noted that in our case, we are defining a convective event on a daily basis rather than
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considering each and every individual convective events. Due to the same reason, we did not
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extend our analysis to upper level water vapour, since it varies more significantly during deep
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convection and is short lived.
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For the current analysis, we took central 1⁰ x 1⁰ grid from each 5⁰ x 5⁰ SR, totally ten
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1⁰ x 1⁰ grid from ten 5⁰ x 5⁰ SRs and attempted to observe the variation of water vapour density
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during individual convective (satisfies the criteria of vertical wind velocity at 500mb, OLR,
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rainfall and SST) and non-convective (doesn't obey any of the four criteria) days. The number of
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individual convective events occurred during SWM season of the study period (1998 - 2010),
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mean and SD of water vapour density (WVD) during convective and non-convective events for
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each individual 1⁰ x 1⁰ SR are calculated.
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From the calculated values, we noticed that the variability of low level WVD is observed
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to be high for non convective events for all the SRs and number of convective events is found to
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be more than non convective events in SR 4 where as in other SRs the non convective events are
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high. Fig. 5(a) shows the plot for number of convective events and mean WVD at 850 mb along
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with their SD for the period 1998 – 2010. The data on primary Y-axis represent the cumulative
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number of convective days of the central 1⁰ x 1⁰ grids from all SRs. The secondary Y- axis
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denotes the mean WVD at 850 mb along with its SD of the respective boxes. From Fig. 5(a) it is
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observed that the entire study period has shown a WVD of > 12 g/m3 in all years. The
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corresponding number of convective days varied from 28 days (in the year 2002) to a maximum
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of 113 (in the year 1999). Similarly from Fig. 5(b) it is understood that the WVD at 850 mb <12
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g/m3 in all the years. It is also noticed from Fig .5(a and b) the WVD at 850 mb has more SD of
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about 0.6 during the non-convective days when compared to convective days (SD=0.2).
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In the process of finding the threshold value of WVD at 850 mb, we analysed the water
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vapour separately for convective and non-convective days of the entire study period and we
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inferred the frequency count of WVD at 850 mb with intervals ranging from 1 to 4 g/m3. This
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analysis helped us to understand the variability of water vapour density along with the other test
331
variables. Deriving a mathematical construct for defining a threshold is a difficult task. However,
332
we considered all the convective and non-convective events for the entire study period and
333
performed the frequency count analysis.
334
Figure 6(a and b) illustrates the percentage contribution of a) convective and b) non-
335
convective events with WVD lesser/greater than 12 g/m3 respectively. From Fig.6(a and b), it is
336
clear that upon the total 1008/1359 convective/non-convective events WVD at 850 mb of 86% /
337
77 % of the events are found to be above/below 12 g/m3. We carried out the same analysis by
338
eliminating the upper limit in the SST criterion, in view of recent study of Roxy (2014). Though
339
the number of convective events increased to 1653 there is no change in the percentage of days
340
having WVD above 12 g/m3.
341
The recent review of Sherwood et al. (2010) on understanding the convective interaction
342
with water vapour and changes associated with water vapour in warmer climate have revealed a
343
very important point that it is the relative humidity which has to be focused rather than specific
344
humidity since, it is closely associated with the change in surface and environmental
16
345
temperature. To be precise, relative humidity is more dependent on lapse rate. Based on the work
346
of Tomasi (1984), we did a preliminary analysis and tabulated the temperature, relative humidity
347
and WVD at different altitudes in the free troposphere with regular interval of 0.5 km during the
348
selected convective and non-convective days.
349
Table 3 shows the mean temperature, relative humidity and WVD in the free troposphere
350
for the average of all the ten selected central 1⁰ x 1⁰ grids of each SR at different altitudes during
351
convective and non-convective days. From Table 3 it has been observed that the temperature
352
variability is very small even though it influences the relative humidity and WVD varies greatly
353
during convective and non-convective days. During convective days, from 850 mb to the levels
354
of boundary layer (2- 3 km) the relative humidity is found to be decreasing and then starts
355
increasing till 5.5 km where as in non-convective days it shows a similar behaviour of increase
356
up to 4 km after a lull in the boundary layer and in addition to that a decrement is observed
357
further if we go up to 5 .5 km.
358
We also examined the water vapour scale height by using also the surface level water
359
vapour density during convective and non convective days. This analysis not only helped to
360
derive the ranges of scale height during convective and non-convective days but also to
361
understand the variations in WVD with the scale height. The scale height of WVD ranged from
362
1.3 km to 2.8 km during the non-convective days, in case of convective days, the scale height
363
varied between 3 km to 3.5 km. Hence, it may be noticed that a marginal scale height of 2.9 km
364
can be considered to distinguish convective and non-convective days over BoB during the study
365
period.
366
17
367
6. Conclusions
368
In the present analysis the authors have attempted to understand the features of convection using
369
satellite derived SST and other atmospheric variables over Bay of Bengal. The study concludes
370
the following
371
i)
The mean SST and OLR over the study region are following a trend of decrement
372
(0.5 °C) from the month of May to June and increment (0.1 °C) from the month of
373
September to October
374
ii)
SRs of spatial average of 5° x 5°, SR 5 and SR 10 (Fig.1) are observed to be the
375
coldest and warmest over the study region from their SST values and SR 4 to be the
376
most convective one since it has recorded maximum number of convective events
377
(266) in the study period.
378
iii)
ISV of SST and other atmospheric parameters studied through wavelet analysis
379
showed the dominance of the major periodicities of SWM season, 30 – 60 day (MJO)
380
and 10 – 20 day (QBWO) with significant variability in the power of each parameter
381
during different years.
382
iv)
From the lead-lag analysis, it is observed that the CWV and SSTs maintained a 6 to
383
10 days lead or lag during the study period. In the cold SR, most of the years have
384
witnessed CWV leading the SST and in the remaining most convective and warm
385
box, neither a lead nor lag behaviour is observed in the study period.
18
386
v)
Decrease in the CWV just after the convective event is illustrated by our analysis of
387
observing the anomaly of CWV immediately after a convective rainfall event in the
388
SR 1 which is located in the head Bay.
389
vi)
In our main attempt to explore the existence of a threshold for WVD (850 mb) in
390
lower troposphere, we have arrived at a conclusion that WVD of 86% / 77% of the
391
convective / non convective events are found to be above/below 12 g/m3 by applying
392
the convective criteria to the central 1° x 1° grid of each sub region. Such a distinct
393
demarcation in scale height is found to be at 2.9 km.
394
vii)
From the mean vertical profile of temperature, relative humidity and WVD in the free
395
troposphere, for the average of all the ten selected central 1⁰ x 1⁰ grids of each 5⁰ x 5⁰
396
SRs, we observed though the temperature variability is very small, it influences the
397
relative humidity and WVD greatly during convective and non-convective days.
398
During convective days from 850 mb to the level of boundary layer (2- 3 km) the
399
relative humidity is found to be decreasing and then starts increasing till 5.5 km
400
where as in non-convective days it shows a similar behaviour of increase upto 4 km
401
after a lull in the boundary layer and in addition to that a decrement is observed
402
further if we go upto 5.5 km.
403
404
viii)
The variability of lower tropospheric WVD is found to be high for non-convective
days (0.6 g/m3) when compared to convective days (0.2 g/m3).
405
19
406
Acknowledgement
407
This work was carried out with the support of project funded by ISRO RESPOND. We
408
gratefully acknowledge Dr. B.V. Krishnamurthy, former director of SPL, VSSC, Trivandrum for
409
his useful suggestions and encouragement. We thank D.Selvaraj, Senior Research Fellow, SRM
410
University for programming assistance. Our sincere thanks to the anonymous reviewers for their
411
constructive comments.
412
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