The response of the upper ocean to tropical cyclone Viyaru over the Bay of Bengal

Kashem, Md. Abul; Ahmed, Md. Kawser; Akhter, M. A. E.; Chowdhury, K M Azam

doi:10.1007/s13131-019-1370-1

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…In this study, the 6-hourly best track of the TC and oceanographic, atmospheric, and wave data were obtained from the following websites: China Meteorological Administration tropical cyclone center (http:// tcdata.typhoon.org.cn), Argo data (ftp://ftp.ifremer.fr/ifremer/argo), Remote Sensing of SST (https://www.remss.com), HYCOM reanalysis data (http://www.hycom.org/data/glbv0pt08/expt-93pt0), European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 hourly reanalysis data (https://cds.climate.copernicus.eu), and ECMWF high-resolution operational forecasts data (NCAR RDA Dataset ds113.1 (ucar.edu)). Kashem et al (2019) defined the ILD as the depth where the temperature is one degree lower than that of the ocean at 5 m depth, and the BLT was estimated by subtracting the ILD and MLD. The MLD was evaluated based on the density criteria of Kara et al (2000).…”

Section: Methodsmentioning

confidence: 99%

“…The typical phenomena include a distinct SST cooling within 6 °C and an increase in sea surface salinity (SSS) along the typhoon track (Price, 1981;D'Asaro et al, 2007;Cheung et al, 2013). In addition, freshwater discharged from terrestrial rivers and heavy precipitation significantly affect the features of the upper ocean, such as the SST, SSS, density stratification, mixing layer depth, barrier layer thickness, diapycnal diffusivity, and so forth, during the passage of TCs (Sprintall and Tomczak, 1992;Kashem et al, 2019;Qiao et al, 2022). During high winds, nearly 75%-90% of SST cooling is caused by the entrainment of subsurface colder water into the mixed layer.…”

Section: Introductionmentioning

confidence: 99%

“…The development of observational methods, assimilation technology, and multi-source datasets, including in situ observations, satellite remote data, and numerical assimilation results, has helped to investigate air-sea processes during a typhoon. For example, Kashem et al (2019) investigated the upper ocean response to tropical cyclone Viyaru based on Argo floats and reanalysis datasets. Oginni et al (2021) depicted the air-sea boundary layer under super typhoon Haiyan using satellites, surface drifters, Argo floats, and reanalysis datasets and explored the potential mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Features of upper ocean and surface waves during the passage of super typhoon Hinnamnor (2022)

Zhang

2023

Front. Mar. Sci.

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Scientific understanding of super typhoons (STYs) is essential for environmental and human-made disaster prevention. The interactive processes among the atmosphere, ocean, and surface waves have an intimate relationship within the STY system. This study chose STY Hinnamnor (2022) as an example and used multi-source data to investigate how it affected the upper ocean. First, Argo floats data at two positions were collected to investigate the variation of sea surface temperature (SST), sea surface salinity (SSS), isothermal layer depth (ILD), mixed layer depth (MLD), barrier layer thickness (BLT), and eddy viscosity (EV) during pre- and post-STY. The STY passed through two Argo floats; hence, the SST, ILD, and BLT significantly decreased post-STY, whereas the MLD and EV increased. The SSS decreased by 0.26 psu where the STY passed southwestward, whereas it increased by 0.11 psu where the STY began to move northward. Subsequently, the remote sensing data and re-analysis data were used to study the evolution of the SST, precipitation, runoff, and profiles of the upper ocean pre- and post-STY. The results reveal that intensive vertical mixing and upwelling occurred in the region where the direction of the STY movement switched. It also revealed that the runoff and heavy precipitation increased the water salinity in this area. In addition, the reanalysis data indicated that the significant wave height (SWH) and the mean wave period (MWP) near the cyclone center became longer than in other areas. The temporal evolution of the spectral peak period (SPP) demonstrated the generation of a swell zone on the right side of the typhoon track when the STY moved northward.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Features of upper ocean and surface waves during the passage of super typhoon Hinnamnor (2022)

Zhang

2023

Front. Mar. Sci.

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“…Information on the four typhoons is displayed in Table 1, while the tracks and intensities of these four typhoons are shown in Figure 1. Many researchers also analyzed and compared the daily SST changes during typhoons instead of calculating the SSC [35][36][37]. Despite the importance of the sea surface response to typhoons, few papers have discussed the algorithm to estimate the SSC, whose aspects have not been illustrated and studied in detail till now.…”

Section: Typhoon Track Data and Basic Informationmentioning

confidence: 99%

Estimating Typhoon-Induced Sea Surface Cooling Based upon Satellite Observations

Song

Xiang

Guo

et al. 2020

Water

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Typhoons frequently occur in the summer in the northwestern Pacific Ocean, and the responses of the upper ocean to typhoons have drawn extensive attention for decades. In the present work, a modified grid-based maximum response (GMR) method was proposed to estimate the sea surface cooling (SSC) caused by typhoons. The current algorithm (CA) is different from the original GMR method mainly in two aspects: (1) it uses a 5 day average rather than a 2 day average of the sea surface temperature (SST) before the typhoon as the reference temperature; (2) it modifies the fixed radius of 400 km to the level-7 Beaufort scale wind-force (~17.1 m/s) radius to determine the area where the SSC should be calculated. Then the MW-IR OISST data derived from satellite observations were used to compare the SSC estimated by different algorithms in four typhoon cases, Megi, LionRock, Trami and KongRey. The results show that, in all cases, maximum response methods have approached similar results, while the others seemed to have underestimated the SSC in degrees. In the slow-moving LionRock case, grid-based methods were found to have better performance, while in the successive typhoon cases, Trami and KongRey, CA showed an improved result in representing the pre-existing sea surface status before the typhoon KongRey by using the pentad mean SST as the reference temperature. In addition, the use of level-7 wind-force coverage made the results much livelier. In a word, the algorithm proposed here is valid in general. It has advantages in estimating the SSC caused by both slow-moving typhoons and successive typhoons, and should be further applied to related research.

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“…As depression play a major role in cyclone prediction, analysis of cluster and recognition of a pattern in rainfall forecast for an hourly basis was presented in [7], therefore improving the rainfall forecast accuracy. A case study on the Bay of Bengal was presented in [8].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Cyclone Using Kalman Spatio Temporal and Two Dimensional Deep Learning Model

Rajesh,

Ramaswamy,

Kannan

2020

MJCS

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Cyclone Classification and Prediction models rely on large intensity based on the maximum speed of the wind, along with the classification of intensity. The computational constraints blended with the formation of those intensities, cyclone classification and prediction firmly depreciate the full range of optimal features required for classification and hence accurate representation is less possible. Keeping this point, our study inspects the potential of Spatio-temporal features using a machine learning algorithm as an alternative to the current study of cyclones. This method is called, Spatio-Temporal Kalman Momentum and Two Dimensional Deep Learning (SKM-2DDP) for cyclone classification and prediction. To start with, pre-processing is performed by applying the Kalman Momentum Conservation Filter mechanism based on the design of the Dvorak technique to obtain optimal estimates of state variables and reduce the computational burden involved to remove noise from input cyclone images. With the resultant denoised input cyclone images, Spatio Temporal Feature Extraction is performed. Features obtained from the inherent intrinsic properties of pre-processed cyclone images of several weather conditions result in successful classification. Followed by pre-processing, in this work, features constituting both pixel-wise intensities over time and the directional length are being considered. The dependency of autocorrelation with each pixel’s intensities over time and two temporal features are helped for coarse classification of weather conditions according to their visual effects. Finally, with the inherent intrinsic features extracted, a Two Dimensional Deep Learning model is utilized to foretell the cyclone intensity. Experimental evaluation of the proposed SKM-2DDP method is accomplished using images of cyclone dataset with many factors just as peak signal to noise ratio, prediction accuracy, prediction time and false-positive rate. Our own selves have considered with several cyclone images acquired from two different cyclone image datasets namely OCKHI_DEC2017 and VARDAH_DEC2016.

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The response of the upper ocean to tropical cyclone Viyaru over the Bay of Bengal

Cited by 12 publications

References 16 publications

Features of upper ocean and surface waves during the passage of super typhoon Hinnamnor (2022)

Features of upper ocean and surface waves during the passage of super typhoon Hinnamnor (2022)

Estimating Typhoon-Induced Sea Surface Cooling Based upon Satellite Observations

Prediction of Cyclone Using Kalman Spatio Temporal and Two Dimensional Deep Learning Model

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