Study on Internal Waves over the Bay of Bengal Using ALOS PALSAR L band SAR Imageries

doi:10.21203/rs.3.rs-4286039/v1

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Study on Internal Waves over the Bay of Bengal Using ALOS PALSAR L band SAR Imageries

https://doi.org/10.21203/rs.3.rs-4286039/v1

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Using ALOS PALSAR L-band SAR data, internal waves (IWs) with distinct characteristics ranging from weak manifestations to strong signatures are observed throughout the continental shelf edge of the Bay of Bengal and are not confined to a particular region or season. The wavelength, phase speed, and amplitude may vary depending upon the oceanic environmental conditions. The wavelengths computed from the present study vary from 0.17 to 4.8 km, and the phase speed ranges from 0.04 to 0.2 m/s. An attempt was made to study the morphological features of IWs using SAR imagery. IWs with different signatures (elevation and depression) were observed. Interestingly, they coexist simultaneously and crossover in the BOB. This may imply that either the IWs generated from the deep ocean propagating over shelf regions may be reflected or that river discharge may act as a force to disturb stratified layers. In this study, depressed IWs generated off the Kakinada region by the Godavari River plume were observed, and similar IW characteristics were observed in the head BOB. This may imply that many other major and minor rivers may act as another source for the generation of IWs. However, this aspect of the study needs further investigation in the BOB.

Earth and environmental sciences/Ocean sciences/Physical oceanography

Earth and environmental sciences/Natural hazards

Internal waves

wave length

Phase speed

polarity

river discharge

stratification

morphology

The Bay of Bengal (BOB) is one of the most complex and dynamic regions in nature and is land-locked in the North. In addition, it receives a large inflow of fresh water from various rivers and rivulets that open into the bay due to the nature of the bay (an abrupt increase in depth), which favors the formation of IWs, primarily upon tidal forcing. Studying IWs in BOB is gaining importance for understanding several biogeophysical processes. They play a significant role in understanding mesoscale and submesoscale processes and the mixing and exchange of heat and nutrients and are useful for various defense purposes. Researchers in this region often observe internal wave activity through in situ measurements, numerical modeling, SAR, and other remote sensing techniques. Jackson (2007)¹ reported that large-amplitude IWs generated in the Andaman Sea traverse eastern Sri Lanka and southeastern India. Sridevi et al. (2010)² observed internal wave activity off Bheemunipatnam using thermistor chain and CTD data with 2 min and hourly intervals, respectively. Prasad et al. (2011)³ observed IWs in the North BOB (off the Chilka region). They also presented the monthly distribution of SAR-observed IWs in the region from 1993–2005, showing internal wave activity in all the seasons. Rao et al.⁴ investigated the generation and propagation of low-frequency IWs along the east coast of India, and Babu et al. ⁵ subsequently studied IW-associated induced mixing in the surface layers using the Princeton Ocean Model (POM) model. Pradhan et al.⁶ adopted the MIT-GCM model for the simulation of IWs in the western BOB and later studied their variability during spring-neap conditions by Pradhan et al.⁷ and their interseasonal variability⁸. Similar work was carried out by Joshi et al ⁹ to study the generation and propagation of IWs in the western BOB. Phaniharam et al.¹⁰ studied IW characteristics along the northwestern BOB using SAR imagery. In addition, using in situ observations in the southern BOB, Lozovatsky et al.¹¹ studied high-frequency internal wave activity, and Wijesekera et al.¹² studied their characteristics and dynamics. Later, modeling studies by Jensen et al.¹³ revealed that the IWs generated off the Andaman and Nicobar Islands propagated into the central BOB. These authors supported their study with remote sensing through MODIS observation of IWs in the same region. In the present study, Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOSPAL SAR) images were analyzed to identify, detect, and study internal wave characteristics such as wavelength, phase speed, and direction of propagation all over the BOB. In addition, an attempt has been made to study the morphological features of IWs in SAR images. By understanding the morphology of IWs, it is possible to better understand their impact on the ocean and develop strategies to mitigate their hazards. This study is based on the ALOS PALSAR L-band SAR imageries, and it will form a basis to extend this study using the upcoming NASA-ISRO SAR (NISAR) mission data.

The BOB is a typical sea with a dynamic and complex nature. In addition, the abrupt increase in depth and significant tidal forcing favor the formation of IWs over the BOB. The study region and underlying bathymetry are as shown in Fig. 1. The observation of IWs through SAR imagery is particularly amenable. However, to observe these IWs in SAR images, they are significantly strong enough to manifest the ocean surface in such a way that they appear as alternating dark and bright bands against a gray background. Their appearance in SAR images also depends upon local environmental conditions, such as wind speed and direction, surface currents, and the presence of surface films.¹⁵. Earlier studies on SAR identification of IW activity were often restricted to the region between 17.5–20.5°N and 83.5–87.5°E in the northern BOB¹⁶, ⁹, ¹⁰. However, the present study shows that they generate and propagate all over the continental shelf edge/rim of the BOB. Figure 2 shows the selected ALOS PALSAR L-band SAR images revealing the IWs. Their chief generation mechanism is the perturbation of stratified layers over continental shelves by tidal forces and shoreward propagation, as also suggested by earlier studies. They can also be generated by lee waves, tidal beam resonance, plumes, and the transformation of the internal tide¹⁵. ³ and ¹⁰ computed the IW characteristics, such as the wavelength, phase speed, propagation direction, and amplitude, with the aid of observational data using SAR images over the same region and frequent IW activity observed from September to May. All these studies were primarily confined to the head or north BOB. Although observational (in situ) and modeling studies pertaining to IWs have been carried out over the BOB (as discussed earlier in the introduction section), studies using SAR imagery are rather limited. The details of the SAR images used in this study are as shown in Table 1.

As the BOB experiences northeast (NE) monsoon, southwest (SW) monsoon, and pre- and postmonsoon seasons, the latter two seasons are more favorable for the formation of low-pressure systems as well as IWs. However, in this study, IW activity was observed even in the monsoon season, although strong winds prevailed during this season. This reveals that stratification is present even in the monsoon period as the IWs propagate along the differing density layers. This may be due to the persistence of freshwater discharge from rivers during monsoons and may be due to the low-pressure systems during pre- and postmonsoon periods. This may indicate the persistence of fresh water throughout the year in the northern and eastern BOB, as reported by ¹⁷, ¹⁸, ¹⁹ and ¹³. The observed locations of the IWs in the SAR images and the sources of river discharge into the bay are as shown in Fig. 3. The processed ALOS PALSAR L-band SAR images showing IW features over the BOB are as shown in Fig. 4. They vary from a single soliton to a large group containing more than 20 individuals. They are often observed in groups or in packets. Although they appear throughout the year, they interact with the underlying bathymetry and local oceanographic conditions to exhibit different IW morphological features. IW morphology is the study of the shape and structure of IWs.

Jackson et al.¹⁵ provided an overview of IW detection theories in SAR imagery and the factors that influence IW morphological signatures, including surface, subsurface, and IW properties. In this study, we investigated the morphological features of SAR-observed IWs in the BOB. As shown in Fig. 4, except for the ALPSRP139980350 (single soliton) image, in all the ALOS PALSAR images, the observed IWs are either in groups or in packets. In addition, different types of patterns, such as bright bands followed by dark bands, dark bands followed by bright bands, and single bright bands, are also present. The morphology of the IWs in the ocean can be affected by a number of factors, including the density difference between the different ocean layers, the wavelength of the wave, the depth of the ocean, and the presence of any other oceanic phenomena such as currents or waves ²⁰. Da Silva et al.^{20, 21} classified SAR observations of IW signatures as single positive signs (+) for the bright band, single negative signs (-) for the dark band, or double signs (+/-) for the dark and bright bands. The most common pattern is the IWs of depression, which have nonlinearity, appearing as a bright band followed by a dark band (single positive sign). The characteristics of this signature pattern can be influenced by various factors, such as the environment at the ocean surface (wind speed, wind direction, presence of surface films) and the properties of the IWs themselves (mode, half-width, amplitude, and currents). The extracted wind speed and direction plots for the respective images on the respective day of the SAR images from CCMP v3.1 data are shown in Supplementary Fig. 1.

In most of these SAR images, IWs appear as a bright band on a gray or black background (single positive sign). A bright band-only signature is observed in the ALPSRP139980350 image (single soliton), and this morphology appears when the modulations induced by the breaking event occur. In addition, a dark band against a gray background (single negative sign) also appeared in the ALPSRP132690320, ALPSRP085280400, and ALPSRP190450400 images. In this study, the scene IDs ALPSRP132690320, ALPSRP144210410, ALPSRP131670240, and ALPSRP085280400 exhibited peculiar behavior. SAR images with local bathymetry overlaid to study their nature and characteristics are as shown in Fig. 5. These four images show IW signatures apart from those generated by the local topography. These IWs generated over the BOB exhibit complex and peculiar characteristics.

For example, Fig. 6 shows the IW signatures observed off the Kakinada region having image ID of ALPSRP132690320. Interestingly, the single positive sign and single negative sign signatures coexisted and crossed each other without overlapping in the BOB. This may imply that multiple layers of stratification exist in the BOB. The backscatter profile/transect drawn across these signatures clearly shows the difference in polarity/sign. An extensive study of these kinds of IWs was discussed in ²⁰. In addition, these IWs appear to be approaching different directions, which may indicate that there might be other sources of IW generation (such as high river discharge) or may be due to IW reflection itself (yet to be studied in the future). This triggering of IWs by river discharge has been observed by several studies, as river plumes can also be a significant source for the generation of IWs³¹. The generation of IWs is due to the high discharge from the Mzymta River²² and the generation of IWs under different discharge conditions, such as subcritical²³ and supercritical²² conditions. Similarly, this region is also influenced by Godavari River discharge, and its plume obtained from Landsat 5 images on 22 August 2010 is shown in Fig. 7. Their studies also revealed that IWs can be generated from (even) moderate to high discharge conditions and that their wavelengths and amplitudes vary accordingly. Their study also suggested a general mechanism for river plumes/discharge generating IWs in the coastal ocean in which the river dumps freshwater into the ocean, which forms plumes that spread out due to stratification, and as the plume meets seawater, a zone with a sharp contrast in density forms at the leading edge, called the plume front. Then, the interaction between the plume's flow and this density difference creates a condition where the plume front acts as a moving disturbance called a hydraulic jump²³. This disturbance triggers internal waves to propagate away from the plume front with generally negative polarity, as shown in Fig. 6b. Other IWs with negative polarity were observed off the stretch of the Mahanadi-Brahmani River confluence. These depressed IWs generated in deep water appear to propagate toward shallow regions. Similar results were obtained for all four images, as discussed in the earlier paragraph. As these IWs propagate toward coastal regions, they undergo changes in polarity, refraction, etc., and tend to interact with coastal phenomena. Thus, they exhibit complex patterns.

The most common IW manifestation is the bright band, followed by the dark band of mode-1 IWs of depression ¹⁵. However, the existence of higher modes is also possible in the BOB due to the presence of different stratification conditions ¹⁵. The first and second images of ALPSRP162590150 and ALPSRP162590160 in Fig. 5 are examples of mode-2 IWs. As these mode-2 IWs can persist for more than a day ²⁰, their presence might be observable in the Global Open HYCOM model simulations. It is worth mentioning here that there are not sufficient in situ observations in this region. The daily Global Open HYCOM model simulations of sea surface temperature, U–current, V-current, and SSH data on 11 Feb 2009 showed significant variations, indicating the occurrence of IW activity, as shown in Supplementary Figs. 2 and 3. SAR observation of IWs provides information about their wavelengths, propagation direction, phase speed, polarity, and amplitudes with the aid of in situ data (amplitude computation is not carried out in this study). As previously reported, IWs are generally elevated in this study and are usually generated only when the top layer (H1) is thicker than the bottom layer (H2). However, IWs of depression were also observed, where the top layer (H1) was thinner than the bottom layer (H2). This conversely reveals that IWs provide information about the interior of the ocean, such as stratification/thermocline depth²⁵. The two-layered Kortweg-de Vries (KDV) equation governing these waves is generally used to study their characteristics²⁶,²⁷,²⁸,²⁹,³⁰,²⁵, ³¹, ¹⁰ and is given as

$$\frac{\partial {\eta }}{\partial \text{t}}+{\text{C}}_{0}\frac{\partial {\eta }}{\partial \text{x}}+{\alpha }{\eta }\frac{\partial {\eta }}{\partial \text{x}}+{\beta }\frac{{\partial }^{3}{\eta }}{\partial {\text{x}}^{3}}=0$$

The nonlinear and dispersion coefficients are

$${\alpha }=\frac{3}{2}\frac{{\text{H}}_{1}-{\text{H}}_{2}}{{\text{H}}_{1}{\text{H}}_{2}}{\text{c}}_{0}$$

$${\beta }=\frac{1}{6}{\text{H}}_{1}{\text{H}}_{2}{\text{C}}_{0}$$

$${\text{C}}_{0}={\left[{\Delta }{\rho }\text{g}{\text{H}}_{1}{\text{H}}_{2}∕{\rho }\left({\text{H}}_{1}+{\text{H}}_{2}\right)\right]}^{\frac{1}{2}}$$

where η is the interfacial displacement, H₁ and H₂ are the top and bottom layers of the ocean, respectively, ρ is the density of water, g is the gravity constant, and ∆ρ is the density difference between the two layers.

Generally, the bottom layer (H₂) is thicker than the top bottom layer (H₁). Then, the displacement becomes

$$\eta \left(x,t\right)=-A sec {h}^{2}\left[\frac{2\left(x-{C}_{t}\right)}{\lambda }\right]$$

where A is the amplitude, which represents the soliton of the depression. Its phase speed (C) and wavelength $\left(\lambda \right)$ are expressed as

$$C={c}_{0}\left(1-\frac{A\alpha }{3{\text{c}}_{0}}\right) ; \lambda ={4\left(\frac{-3\beta }{\alpha A}\right)}^{\frac{1}{2}}$$

The wavelengths computed from the present study vary from 0.17 to 4.8 km. They generally propagate toward the coast. The phase speed ranges from 0.04 to 0.2 m/s. The computed IW phase speed and wavelengths are as shown in Table 2. Further detailed research using more data is required to understand the morphology of these IWs.

In this study, ALOS PALSAR SAR images were used to study IWs over the BOB. The BOB is one of the most complex and dynamic in nature and has a large inflow of fresh water from various rivers and rivulets that open into the bay. Due to its bay nature, an abrupt increase in depth favors the formation of IW activity, especially upon tidal forcing. IW activity is present all along the continental shelf edge of the BOB but is not confined to a particular region. The wavelength, phase speed, and amplitude may vary depending on the oceanic environmental conditions. Their appearance varies from weak solitons to strong wave groups/packets. The wavelengths computed from the present study vary from 0.17 to 4.8 km, and they generally propagate toward the coast. The phase speed ranges from 0.04 to 0.2 m/s. Unorganized, irregular IWs at the generating area become well-organized (rank ordered) and well-matured internal solitons as they propagate away from the generating area. IW activity is present even in the monsoon season, although strong winds prevail during this season. This might be due to the persistence of freshwater discharged from the rivers due to low-pressure systems during the pre- and post-monsoon periods. The IWs of different polarities observed in the BOB exhibit complex patterns. Complex IW patterns, such as multiple groups moving in different directions, appeared close to the river mouths, especially in the north or at the head of the bay. Interestingly, the single positive sign and single negative sign signatures coexisted without overlapping in the BOB. This may imply that multiple layers of stratification exist in the BOB (a further investigation is necessary to support this statement). In addition, these IWs appear to approach from different directions, which may indicate that there might be other sources of internal wave generation (such as high river discharge, high winds, etc.) or may be due to internal wave reflection, which will be studied in the future. In addition, the presence of surface films ³² may influence the morphology of these IWs, and river discharge might be another forcing factor for internal wave generation in the BOB (further research is necessary to support this assumption). An attempt was made to study the morphological features of the IWs in the BOB. As they propagate toward coasts, they also tend to interact with coastal phenomena such as eddies, surface waves, stratification, and coastal currents ^12, and in model simulations ^13, they exhibit complex characteristics such as breaking, refraction, and polarity. However, further investigations using long-term and additional data could shed more light on the dynamics of the IWs in the Bay of Bengal.

ALOS PALSAR SAR images of level 2.2 were collected from the Alaska Satellite Facility (ASF) DAAC Earthdata (nasa.gov), which archives and distributes SAR imagery. Then, the processed images are analyzed to detect and identify IW patterns such as single solitons or wave packets. Only the SAR images with IWs and without repeated scenes were selected; otherwise, they were rejected. The SAR images acquired from the ALOS PALSAR L-band SAR data from 2007–2010 are shown in Fig. 2. Then, the processed images were analyzed to detect and identify IW patterns such as single solitons or wave packets. Their characteristics like wavelength, group speed/ phase speeds and direction of propagation were computed using BILKO software (Byfield et al. 2005).

Author Contribution

P.S.N., S.V. and B.S. conceived the study; P.S.N., R.H.K., K.S.S., P.S. , C.S. , and B.A.K. carried out the analyses and wrote the manuscript. S.V., TVS.U.B., S.J. and T.B.K. made important suggestions to improve the quality of the manuscript.

Acknowledgement

AcknowledgmentsWe would like to thank, Director of INCOIS for his support and encouragement in carrying out this research. The Secretary, Ministry of Earth Sciences (MoES) is also thanked for his support. The authors thank the Alaska Satellite Facility (ASF) for providing the SAR images, ESA and MATLAB for the software used for the SAR image analysis. We thank EarthExplorer (usgs.gov) Landsat Imagery. We also thank the global HyCOM data at www.hycom.org for providing model output data of sea surface temperature, U, V current and SSH data. The wind data obtained from CCMP Version 3.0 analyses were produced by Remote Sensing Systems and sponsored by NASA Earth Science funding. The data are available at www.remss.com. We thank curie for mauscript language correction.

Data Availability

Data availability:ALOS PALSAR SAR images of level 2.2 were collected from the Alaska Satellite Facility (ASF) DAAC Earthdata (nasa.gov), which archives and distributes SAR imagery © JAXA/METI ALOS PALSAR L1.0 2007. Accessed through ASF DAAC. ASF Data Search (alaska.edu). Landsat Imagery obtained from EarthExplorer (usgs.gov). The Global HYCOM data used in the supplementary figures obtained from https://tds.hycom.org/thredds/catalogs/GLBv0.08/expt_53.X.html through get_hycom_online.m script from wenfanwu in github. Wind data obtained from CCMP Version-3.0 analyses are produced by Remote Sensing Systems and sponsored by NASA Earth Science funding. Data are available at www.remss.com.

Jackson, C. Internal wave detection using the Moderate Resolution Imaging Spectroradiometer (MODIS). J Geophys Res Oceans 112, 11012 (2007).
Sridevi, B. et al. Impact of internal waves on sound propagation off Bhimilipatnam, east coast of India. Estuar Coast Shelf Sci 88, 249–259 (2010).
Prasad, K. V. S. R. & Rajasekhar, M. Space borne SAR observations of oceanic internal waves in North Bay of Bengal. Natural Hazards 57, 657–667 (2011).
Rao, A. D. et al. Investigation of the generation and propagation of low frequency internal waves: A case study for the east coast of India. Estuar Coast Shelf Sci 88, 143–152 (2010).
Babu, S. V. & Rao, A. D. Mixing in the surface layers in association with internal waves during winter in the northwestern Bay of Bengal. Natural Hazards 57, 551–562 (2011).
Pradhan, H. K., Joshi, M. & Rao, A. D. Simulation of internal waves in the western Bay of Bengal using MITGCM: A case study. OCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension 1–5 (2013) doi:10.1109/OCEANS-Bergen.2013.6607966.
Pradhan, H. K., Rao, A. D. & Joshi, M. Neap—spring variability of internal waves over the shelf-slope along western Bay of Bengal associated with local stratification. Natural Hazards 80, 1369–1380 (2016).
Pradhan, H. K., Rao, A. D. & Mohanty, S. Interseasonal variability of internal tides in the western Bay of Bengal. Natural Hazards 84, 809–820 (2016).
Joshi, M. et al. Internal waves over the shelf in the western Bay of Bengal: a case study. Ocean Dyn 67, 147–161 (2017).
Phaniharam, S. A., Chintam, V., Baggu, G. & Koneru, V. S. R. P. Study of internal wave characteristics off northwest Bay of Bengal using synthetic aperture radar. Natural Hazards 104, 2451–2460 (2020).
Lozovatsky, I. et al. A snapshot of internal waves and hydrodynamic instabilities in the southern Bay of Bengal. J Geophys Res Oceans 121, 5898–5915 (2016).
Wijesekera, H. W. et al. Internal tidal currents and solitons in the southern Bay of Bengal. Deep Sea Res 2 Top Stud Oceanogr 168, (2019).
Jensen, T. G. et al. Numerical modeling of tidally generated internal wave radiation from the Andaman Sea into the Bay of Bengal. Deep Sea Research Part II: Topical Studies in Oceanography 172, 104710 (2020).
Marks, K. M., Smith, W. H. F. & Sandwell, D. T. Evolution of errors in the altimetric bathymetry model used by Google Earth and GEBCO. Mar Geophys Res (Dordr) 31, 223–238 (2010).
Jackson, C., da Silva, J., Jeans, G., Alpers, W. & Caruso, M. Nonlinear Internal Waves in Synthetic Aperture Radar Imagery. Oceanography 26, (2013).
Prasad, K. & Rajasekhar, M. Observations of oceanic internal waves in bay of Bengal using synthetic aperture radar. in Processing of SEASAR. 2006 1–6 (Citeseer, 2006).
Sengupta, D., Bharath Raj, G. N. & Shenoi, S. S. C. Surface freshwater from Bay of Bengal runoff and Indonesian Throughflow in the tropical Indian Ocean. Geophys Res Lett 33, (2006).
Papa, F., Durand, F., Rossow, W. B., Rahman, A. & Bala, S. K. Satellite altimeter-derived monthly discharge of the Ganga-Brahmaputra River and its seasonal to interannual variations from 1993 to 2008. J Geophys Res Oceans 115, (2010).
Papa, F. et al. Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: An update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal. J Geophys Res Oceans 117, (2012).
Da Silva, J. C. B., Ermakov, S. A., Robinson, I. S., Jeans, D. R. G. & Kijashko, S. V. Role of surface films in ERS SAR signatures of internal waves on the shelf 1. Short-period internal waves. J Geophys Res Oceans 103, 8009–8031 (1998).
Da Silva, J. C. B., Ermakov, S. A. & Robinson, I. S. Role of surface films in ERS SAR signatures of internal waves on the shelf 3. Mode transitions. J Geophys Res Oceans 105, 24089–24104 (2000).
Osadchiev, A. A. Small Mountainous Rivers Generate High-Frequency Internal Waves in Coastal Ocean. Scientific Reports 2018 8:1 8, 1–8 (2018).
Mendes, R. et al. On the generation of internal waves by river plumes in subcritical initial conditions. Scientific Reports 2021 11:1 11, 1–12 (2021).
Robinson, I. S. Discovering the Ocean from Space: The Unique Applications of Satellite Oceanography. (Springer Science & Business Media, 2010).
Korteweg, D. J. & De Vries, G. XLI. On the change of form of long waves advancing in a rectangular canal, and on a new type of long stationary waves. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 39, 422–443 (1895).
Shi-qiang, D. Solitary waves at the interface of a two-layer fluid. Appl Math Mech 3, 777–788 (1982).
Drazin, P. G. Solitons, vol. 85, of London Mathematical Society Lecture Note Series. Preprint at (1983).
Liu, A. K., Chang, Y. S., Hsu, M.-K. & Liang, N. K. Evolution of nonlinear internal waves in the East and South China Seas. J Geophys Res Oceans 103, 7995–8008 (1998).
Zheng, Q., Yuan, Y., Klemas, V. & Yan, X.-H. Theoretical expression for an ocean internal soliton synthetic aperture radar image and determination of the soliton characteristic half width. J Geophys Res Oceans 106, 31415–31423 (2001).
Chen, G.-Y., Su, F.-C., Wang, C.-M., Liu, C.-T. & Tseng, R.-S. Derivation of internal solitary wave amplitude in the South China Sea deep basin from satellite images. J Oceanogr 67, 689–697 (2011).
Nash, J. D. & Moum, J. N. River plumes as a source of large-amplitude internal waves in the coastal ocean. Nature 2005 437:7057 437, 400–403 (2005).
LaFond, E. C. Oceanographic studies in the Bay of Bengal. Proceedings of the Indian Academy of Sciences - Section B 46, 1–46 (1957).

Table 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

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You are reading this latest preprint version

Study on Internal Waves over the Bay of Bengal Using ALOS PALSAR L band SAR Imageries

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Abstract

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Introduction

Results

Discussion

Methods

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1