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遥感技术与应用  2019, Vol. 34 Issue (5): 1091-1100    DOI: 10.11873/j.issn.1004-0323.2019.5.1091
降水遥感观测专栏     
基于FY-3C MWHTS的台风降水反演算法研究
李娜1,2,3(),张升伟1,2(),何杰颖1,2
1. 中国科学院微波遥感技术重点实验室,北京 100190
2. 中国科学院国家空间科学中心,北京 100190
3. 中国科学院大学,北京 100049
Research on Typhoon Precipitation Retrieval Algorithm based on FY-3C MWHTS
Na Li1,2,3(),Shengwei Zhang1,2(),Jieying He1,2
1. Key Laboratory of Microwave Remote Sensing, Chinese Academy of Sciences, Beijing 100190, China
2. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
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摘要:

为估测台风带来的地表瞬时降雨率,利用FY-3C上搭载的微波湿温探测仪(Microwave Humidity and Temperature Sounder,MWHTS)的L1级在轨观测亮度温度数据与多卫星降水分析TMPA(Tropical Rainfall Measuring Mission(TRMM)Multi-Satellite Precipitation Analysis)3B42降水产品数据,通过多元线性回归和BP神经网络两种算法对台风区的降水情况进行了反演研究。结果表明,由这两种算法反演的降水分布图可以清晰地看到台风中心、云墙以及螺旋雨带等台风的位置、分布及结构信息,这与TMPA 3B42降水产品数据估测到的台风降水分布图相一致。此外,从定量的角度来看,TMPA 3B42降水数据与这两种反演算法反演的地表瞬时降水量(mm/hr)都具有较高的相关性和较小的偏差和均方根误差,反演的精度较高。故这两种算法都可以用来反演台风区的降水量,同时也表明FY-3C MWHTS微波在轨观测资料在台风区监测及降水研究中能发挥出较高的应用价值。

关键词: FY-3C MWHTS台风降水反演BP神经网络多元线性回归    
Abstract:

In order to estimate the instantaneous precipitation rates brought by the typhoon, the Level 1 brightness temperatures from the Microwave Humidity and Temperature Sounder (MWHTS) onboard the FY-3C satellite and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 precipitation product data are used to retrieve the precipitation rates in the typhoon area using the multiple linear regression and BP neural network retrieval algorithms. The results show that the precipitation distribution maps retrieved by these two algorithms can be clearly observed the location, distribution and structural information of the typhoons such as typhoon center, cloud wall and spiral rain belt, which are consistent with the TMPA 3B42 precipitation product data. In addition, from a quantitative point of view, the TMPA 3B42 precipitation data and surface precipitation rate (mm/hr) retrieved by these two precipitation retrieval algorithms reach higher correlation and smaller deviations and root mean square errors, and the retrieval accuracy is higher. Therefore, these two retrieval algorithms can be used to retrieve the precipitation in the typhoon area. It also shows that microwave on-orbit observation data from the FY-3C MWHTS can play a high application value in typhoon monitoring and precipitation research.

Key words: FY-3C MWHTS    Typhoon precipitation retrieval    BP neural network    Multiple linear regression
收稿日期: 2018-08-22 出版日期: 2019-12-05
ZTFLH:  P412.27  
基金资助: 国家重点研发计划项目(2018YFB0504900);军委装备发展部预研基金项目(6140136010116);国家重点研发计划项目(2017YFB0502800)
通讯作者: 张升伟     E-mail: lina_nssc@163.com;zhangshengwei@ mirslab.cn
作者简介: 李 娜(1989-),女,河南禹州人,博士研究生,主要从事星载微波载荷的物理参数反演及资料同化研究。E?mail:lina_nssc@163.com
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引用本文:

李娜,张升伟,何杰颖. 基于FY-3C MWHTS的台风降水反演算法研究[J]. 遥感技术与应用, 2019, 34(5): 1091-1100.

Na Li,Shengwei Zhang,Jieying He. Research on Typhoon Precipitation Retrieval Algorithm based on FY-3C MWHTS. Remote Sensing Technology and Application, 2019, 34(5): 1091-1100.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.1091        http://www.rsta.ac.cn/CN/Y2019/V34/I5/1091

序号

中心频率

/GHz

极化

带宽

/MHz

频率稳

定度/MHz

动态范围/K灵敏度/K在轨灵敏度/K定标精度/K3 dB波束宽度权重函数峰值/hPa
189.0V1 500503~3401.00.231.32.0°窗区
2118.75±0.08H20303~3403.61.622.02.0°30
3118.75±0.2H100303~3402.00.752.02.0°50
4118.75±0.3H165303~3401.60.592.02.0°100
5118.75±0.8H200303~3401.60.652.02.0°250
6118.75±1.1H200303~3401.60.522.02.0°350
7118.75±2.5H200303~3401.60.492.02.0°地表
8118.75±3.0H1 000303~3401.00.272.02.0°地表
9118.75±5.0H2 000303~3401.00.272.02.0°地表
10150.0V1 500503~3401.00.341.31.1°窗区
11183.31±1H500303~3401.00.471.31.1°300
12183.31±1.8H700303~3401.00.341.31.1°400
13183.31±3H1 000303~3401.00.31.31.1°500
14183.31±4.5H2 000303~3401.00.221.31.1°700
15183.31±7H2 000303~3401.00.271.31.1°800
表1  FY-3C MWHTS的通道设计指标
图1  FY-3C MWHTS在轨工作图
载荷通道刈幅宽度/km像元数FOVs分辨率/km
MWHTS1~92 6459829
MWHTS10~152 6459816
ATMS3~162 5809632
ATMS17~222 5809616
MHS1~52 3109016
表2  不同载荷的参数对比情况
图2  2017年10月22日1355-1536UTC FY-3C MWHTS观测到的全球亮温分布情况
图3  2016年10月15日0033-0214UTC FY-3C MWHTS监测到的台风区的亮温分布情况
图4  由TMPA 3B42数据估测的全球降水分布图(2017年6月10日1200UTC)
图5  由TMPA 3B42数据估测的台风区降水分布图(2016年10月15日0000UTC)
图6  3层BP神经网络模型示意图
图7  亮温及降水图(第一、二列分别是2016年9月24日12:18~14:00 UTC和9月25日11:59~13:41 UTC两个时段的亮温及降水图)
时间(UTC)CorrBias(mm/hr)RMSE(mm/hr)

20160924

12:18~14:00

0.900.310.79

20160925

11:59~13:41

0.890.300.69
表3  多元线性回归反演的降水相关系数与误差分析
时间(UTC)CorrBias(mm/hr)Bias(mm/hr)

20160924

12:18~14:00

0.920.280.71

20160925

11:59~13:41

0.890.320.67
表4  BP神经网络反演的降水相关系数与误差分析
图8  相关系数图(第一列为多元线性回归反演算法,第二列为BP神经网络反演算法)
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