| doi: 10.12013/qxyjzyj2023-016 |
| 基于FY-4A卫星数据的强对流云团识别与监测 |
Identification and monitoring of severe convective clouds based on FY 4A satellite data |
| 摘要点击 284 全文点击 215 投稿时间：2023-01-30 修订日期：2023-03-18 |
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| 基金: 福州市科技重大项目（编号：2022-ZD-005） |
| 中文关键词: 强对流云团，识别，卫星数据，滑动窗口法，动态阈值 |
| 英文关键词: severe convective cloud identification satellite data sliding window method dynamic threshold |
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| 引用:季凌潇,洪水洁,徐能通.2023,基于FY-4A卫星数据的强对流云团识别与监测[J].气象与减灾研究,46(2):111-119 |
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| 中文摘要: |
| 为解决强对流监测问题，克服地区亮温特征对卫星监测的影响，利用FY 4A卫星L1数据，结合滑动窗口方法和多通道动态阈值自动识别法，对典型强对流云团进行识别与监测。结果表明：1）多通道动态阈值自动识别方法结合滑动窗口方法，可避免人为设置阈值的主观性，整合强对流的区域识别结果，实现全国强对流云团监测。2）此方法具有良好的强对流云团识别效果，识别正确率达到89%；3）FY 4A卫星识别结果与雷达反射率高值区基本一致，能够准确监测强对流云团发生发展和移动的过程，具有较高的识别精度。 |
| Abstract: |
| In order to solve the problem of national convection monitoring and overcome the impact of regional bright temperature characteristics on satellite monitoring, based on the FY 4A satellite L1 data, the typical convection in China was identified and monitored using sliding window method and automatic identification method of multi channel dynamic threshold. The results showed that: 1) The subjectivity of artificially setting thresholds was avoided by using the sliding window method combined with the automatic identification method of multi channel dynamic threshold. Then, the monitoring of national severe convective clouds was achieved by integrating the regional identification results of convection. 2) This method showed high identification performance for severe convective clouds, with an identification accuracy of 89%. 3) The identification results of the FY 4A satellite data were basically consistent with the high value areas of radar reflectivity. The high identification accuracy indicated that the development and movement of severe convective clouds could be accurately monitored. |
