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微波遥感资料在台风丰满度提取中的应用研究

Application of Microwave Remote Sensing Data in Typhoon Fullness Extraction

  • 摘要: 台风丰满度(TCF)以无量纲形式联合台风内外核尺度来表征风暴结构,对台风业务预报具有一定的指示意义。星载微波传感器可以不受天气条件限制直接探测台风海面风,为TCF的准确提取提供了新的技术手段。本文以2023年超强台风“玛娃”为例,通过采集的星载合成孔径雷达(SAR)、散射计和辐射计等微波遥感资料建立TCF提取模型,并利用最佳台风路径数据集资料对提取结果进行验证。结果显示,多源星载微波传感器探测资料在TCF估算方面具有良好的适用性,与最佳台风路径数据的均方根误差(RMSE)为0.07,相关系数为0.82。虽然星载散射计估算的台风强度明显低于参考资料,但它对台风最大风速半径(RMW)和17m/s风圈半径(R17)的估算是准确的,ASCAT-B估算RMW和R17 的RMSE分别为17.9 km和35.2 km,ASCAT-C则分别为18.5 km和43.8 km。本文证明了虽然微波传感器工作在不同波段以及拥有不同的空间分辨率,但是它们在TCF提取方面都具有较好适用性。

     

    Abstract: Tropical cyclone fullness (TCF) is a dimensionless metric that integrates both the inner- and outer- core scales of a typhoon to characterize the storm wind structures, which shows particular direction significance for operational typhoon forecasting. Spaceborne microwave sensors, which are capable of directly observing typhoon ocean surface winds regardless of weather conditions, offer a new technical approach for the accurate extraction of TCF.Taking super typhoon Mawar (2023) as example, this study proposed a TCF retrieve method by collecting spaceborne Synthetic Aperture Radar (SAR), scatterometers, and radiometers data. Subsequently, we make a comparison between model results and typhoon best-track data. Results show that multi-source spaceborne microwave sensor data has good applicability in TCF estimation. The root mean square error (RMSE) and correlation coefficient between satellite-retrieved and best-track data are 0.07 and correlation coefficient 0.82, respectively. Although the typhoon intensity estimates from spaceborne scatterometers are significantly lower than the reference data, the estimates for the radius of maximum wind (RMW) and the radius of 17 m/s winds (R17) are accurate. Specifically, ASCAT-B measured storm RMW and R17 are 17.9 km and 35.2 km, and ASCAT-C are 18.5 km and 43.8 km for ASCAT-C, respectively. Despite working different frequency bands and having varying spatial resolutions, this study demonstrates that multisource microwave sensors are highly suitable for TCF extraction.

     

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