| ZOU Juhong,ZENG Tao,CUI Songxue. 2015. A high wind geophysical model fuction for QuikSCAT wind retrievals and application to Typhoon IOKE. Acta Oceanologica Sinica, 34(7):65－73 |
| A high wind geophysical model fuction for QuikSCAT wind retrievals and application to Typhoon IOKE |
| 一种QuikSCAT大风地球物理模型及其在台风IOKE风场反演中的应用 |
| Received:November 14, 2014 Revised:January 07, 2015 |
| DOI：10.1007/s13131-015-0698-4 |
| Key words:geophysical model function high wind QuikSCAT neural network wind retrieval |
| 中文关键词: 地球物理模型 高风速 QuikSCAT 神经网络 风场反演 |
| 基金项目: |
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| Abstract: |
| The geophysical model function (GMF) describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that the GMF plays an important role in an ocean wind vector retrieval. The performance of the existing Ku-band model function QSCAT-1 is considered to be effective at low and moderate wind speed ranges. However, in the conditions of higher wind speeds, the existing algorithms diverge alarmingly. owing to the lack of in situ data required for developing the GMF for the high wind conditions, the QSCAT-1 appears to overestimate the σ0, which results in underestimating the wind speeds. Several match-up QuikSCAT and special sensor microwave/imager (SSM/I) wind speed measurements of the typhoons occurring in the west Pacific Ocean are analyzed. The results show that the SSM/I wind exhibits better agreement with the "best track" analysis wind speed than the QuikSCAT wind retrieved using QSCAT-1. On the basis of this evaluation, a correction of the QSCAT-1 model function for wind speed above 16 m/s is proposed, which uses the collocated SSM/I and QuikSCAT measurements as a training set, and a neural network approach as a multiple nonlinear regression technologytechnology.In order to validate the revised GMF for high winds, the modified GMF was applied to the QuikSCAT observations of Hurricane IOKE. The wind estimated by the QuikSCAT for Typhoon IOKE in 2006 was improved with the maximum wind speed reaching 55 m/s. An error analysis was performed using the wind fields from the Holland model as the surface truth. The results show an improved agreement with the Holland model wind when compared with the wind estimated using the QSCAT-1. However, large bias still existed, indicating that the effects of rain must be considered for further improvement. |
| 中文摘要: |
| 地球物理模型用于描述后向散射系数与海面风场之间的关系, 在风场反演中扮演非常重要的作用, 可直接决定风场反演精度。现有QSCAT1地球物理模型在中低风速条件下表现尚可, 但在高风速条件下精度较差。由于缺乏现场数据, QSCAT1在高风速条件下通常高估sigma0, 使得反演获得的风速偏低。本研究利用QuikSCAT和SSM/I在西太平洋上对台风的同步结果进行对比分析, 结果表明SSM/I在高风速条件下的观测结果更加接近"最佳路径分析"结果。在此基础上, 本研究采用QuikSCAT和SSM/I的同步观测数据, 并采用人工神经网络方法, 建立大风地球物理模型, 并将该模型应用于QuikSCAT对台风IOKE观测的风场反演, 反演所得最高风速达55m/s。同时, 运用Holland台风模型对反演结果进行对比分析, 结果表明本研究建立的大风地球物理模型较QSCAT1精度有较大提高, 单反演结果中仍存在一定误差, 在台风条件下更高精度的风场反演有必要考虑降雨影响。 |
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