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<p>In order to better predict the intensity and track of tropical cyclones (TCs), near surface wind data is essential for accurately representing the vortex structure of TCs in numerical weather prediction (NWP) systems. Space-based microwave instruments, such as radiometers, scatterometers, and altimeters, are the main source of near-surface wind data over the oceanic regions as in-situ observing systems are able to offer only a limited wind information. These instruments have poor temporal sampling due to their polar orbits. On the other hand, higher temporal wind information in the lower and mid troposphere is provided by atmospheric motion vectors (AMVs) that are generated from the movement of clouds and water vapour in geostationary satellite images. In this study, an effort has been made to show how AMVs could be used to derive near surface winds over the oceanic region. We used a TC as a case study in this work. Linear regression analysis is performed between low-level AMVs retrieved from the Indian National Satellite (INSAT-3D/3DR) and collocated surface winds retrieved from a wide swath Indian scatterometer on-board Earth observation Satellite (EOS)-06 over the TCs formed in the North Indian Ocean (NIO) during the year 2023-24. The developed regression equation is employed to calculate the surface winds by using AMVs. Validation with an independent dataset over a TC region using scatterometer surface winds demonstrate AMVs potential to derive near surface wind structure. Additionally, we analysed ERA5 reanalysis data during TC MOCHA (11–13 May 2023) to examine the relationship between 10&#xa0;m winds and lower tropospheric winds (700,800 and 900&#xa0;hPa). First- and second order polynomial fits were applied separately to the ‘u’ and ‘v’ wind components to establish the relationship. Our findings establish a reliable framework for deriving near-surface wind fields from lower tropospheric wind data, offering significant value for TC monitoring when surface observations are sparse.</p>

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Synergy Between Polar-Orbiting and Geostationary Sensors in Estimating Near Surface Winds Over the Oceanic Region: A Tropical Cyclone Case Study

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Neeru Jaiswal,
  • Randhir Singh,
  • P. K. Thapliyal

摘要

In order to better predict the intensity and track of tropical cyclones (TCs), near surface wind data is essential for accurately representing the vortex structure of TCs in numerical weather prediction (NWP) systems. Space-based microwave instruments, such as radiometers, scatterometers, and altimeters, are the main source of near-surface wind data over the oceanic regions as in-situ observing systems are able to offer only a limited wind information. These instruments have poor temporal sampling due to their polar orbits. On the other hand, higher temporal wind information in the lower and mid troposphere is provided by atmospheric motion vectors (AMVs) that are generated from the movement of clouds and water vapour in geostationary satellite images. In this study, an effort has been made to show how AMVs could be used to derive near surface winds over the oceanic region. We used a TC as a case study in this work. Linear regression analysis is performed between low-level AMVs retrieved from the Indian National Satellite (INSAT-3D/3DR) and collocated surface winds retrieved from a wide swath Indian scatterometer on-board Earth observation Satellite (EOS)-06 over the TCs formed in the North Indian Ocean (NIO) during the year 2023-24. The developed regression equation is employed to calculate the surface winds by using AMVs. Validation with an independent dataset over a TC region using scatterometer surface winds demonstrate AMVs potential to derive near surface wind structure. Additionally, we analysed ERA5 reanalysis data during TC MOCHA (11–13 May 2023) to examine the relationship between 10 m winds and lower tropospheric winds (700,800 and 900 hPa). First- and second order polynomial fits were applied separately to the ‘u’ and ‘v’ wind components to establish the relationship. Our findings establish a reliable framework for deriving near-surface wind fields from lower tropospheric wind data, offering significant value for TC monitoring when surface observations are sparse.