Impact of Assimilation of Satellite Retrieved Ocean Surface Winds on the Tropical Cyclone Simulations Over the North Indian Ocean

Bhate, Jyoti; Munsi, Arpita; Kesarkar, Amit P.; Kutty, Govindan; Deb, S. K.

doi:10.1029/2020ea001517

Cited by 11 publications

(2 citation statements)

References 82 publications

(116 reference statements)

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“…The RMSE in the O-A is less than the same in O-B for all the three TCs, as observed in Bgure 1. Similar results were found in our earlier study Bhate et al (2021). The validation of winds and structure has also been discussed in the companion paper (Munsi et al 2021).…”

Section: Mesoscale Modelling and Experimental Detailssupporting

confidence: 89%

Simulated dynamics and thermodynamics processes leading to the rapid intensification of rare tropical cyclones over the North Indian Oceans

et al. 2022

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The life cycle dynamics and intensiBcation processes of three long-duration tropical cyclones (TCs), viz., Fani (2019), Luban (2018), and Ockhi (2017) formed over the North Indian Ocean (NIO) have been investigated by developing a high-resolution (6 km 9 6 km) mesoscale analysis using WRF and En3D-VAR data assimilation system. The release of CAPE in nearly saturated middle-level relative humidity caused intense diabatic heating, leading to an increase in low-level convergence triggering rapid inten-siBcation (RI). The strengthening of the relative vorticity tendency terms was due to vertical stretching (TC Fani) and middle tropospheric advection (TCs Luban and Ockhi). The increase or decrease in uppertropospheric divergence led to RI through two different mechanisms. The increase in upper divergence strengthens the vortical convection (in TC Luban and Fani) by enhancing the moisture and heat transport, whereas its decrease caused a reduction in the upper-level ventilation Cow at 200 hPa followed by moisture accumulation, enhanced diabatic heating, and strengthened the warm core (TC Ockhi). The RI caused the vortex of three cyclones to extend up to the upper troposphere. The well organised wind during RI led the unorganised, weak, discontinuous vertical vortex columns to become organised with intense vertical velocity throughout the column. Spatial distributions of Okubo-Wiess (OW) parameter showed TC core dominated by vorticity than strain, since deep depression (DD) stages.

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Section: Mesoscale Modelling and Experimental Detailssupporting

confidence: 89%

Simulated dynamics and thermodynamics processes leading to the rapid intensification of rare tropical cyclones over the North Indian Oceans

et al. 2022

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“…In the region of the North Indian Ocean (Region 2), the RAMA buoy data of 23 stations were obtained from Pacific Marine Environmental Laboratory's global tropical moored buoy array (https://www.pmel.noaa.gov/tao/drupal/disdel/ (accessed on 3 February 2022)) [24]. RAMA is the tropical buoys array over the Indian Ocean deployed to measure various atmospheric and oceanic parameters and provide the surface winds at 4 m height [25].…”

Section: Buoy Measurementsmentioning

confidence: 99%

A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

et al. 2022

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Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. the highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD.

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Evaluating the Performance of Sentinel-1 GRDH and OCN Datasets in Capturing Sea Surface Wind in Indonesian Seas

Faristyawan,

Suhadha,

Wardani

2024

Springer Proceedings in Physics

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Impact of Assimilation of Satellite Retrieved Ocean Surface Winds on the Tropical Cyclone Simulations Over the North Indian Ocean

Cited by 11 publications

References 82 publications

Simulated dynamics and thermodynamics processes leading to the rapid intensification of rare tropical cyclones over the North Indian Oceans

Simulated dynamics and thermodynamics processes leading to the rapid intensification of rare tropical cyclones over the North Indian Oceans

A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

Evaluating the Performance of Sentinel-1 GRDH and OCN Datasets in Capturing Sea Surface Wind in Indonesian Seas

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