基于变分多参数正则化方法融合卫星散射计资料

张凯峰; 邓婉月; 王挺; 王慧鹏; 项杰; 宋清涛; 刘春霞

doi:10.3969/j.issn.0253-4193.2017.12.012

基于变分多参数正则化方法融合卫星散射计资料

doi: 10.3969/j.issn.0253-4193.2017.12.012

1.
解放军理工大学气象海洋学院, 江苏南京 211101
2.
新疆维吾尔自治区环境监测总站, 新疆乌鲁木齐 830011
3.
75839部队, 广东广州 510510
4.
解放军理工大学气象海洋学院, 江苏南京 211101;南京大学中尺度灾害性天气教育部重点实验室, 江苏南京 210023
5.
国家卫星海洋应用中心, 北京 100081
6.
中国气象局广州热带海洋气象研究所, 广东广州 510080

基金项目: 国家自然科学基金（41275113）；全球变化与海气相互作用专项。

计量
- 文章访问数: 817
- HTML全文浏览量: 10
- PDF下载量: 673
- 被引次数: 0
出版历程
- 收稿日期: 2017-01-23
- 修回日期: 2017-03-16

Blending satellite scatterometer data based on variational with multi-parameter regularization method

1.
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China
2.
Xinjiang Uygur Autonomous Region Environmental Monitoring Station, Urumqi 830011, China
3.
Unit No. 75839 of PLA, Guangzhou 510510, China
4.
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China;Key Laboratory of Mesoscale Severe Weather (Nanjing University), Ministry of Education, Nanjing 210023, China
5.
National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China
6.
Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510080, China

摘要

摘要: 基于常规三维变分同化（3DVAR）思想和反问题中的正则化技术，提出了适用于风场融合的带正则化约束项的3DVAR方法，在南海海域开展数据融合试验，同时采用模型函数方法确定合理的正则化参数，针对一次台风个例进行了QuikSCAT散射计海面风场数据和华南中尺度模式海面风场数据的融合试验，结果表明采用带正则化约束的3DVAR融合方法，明显消除了常规3DVAR方法融合风场时带来的虚假信息，融合后分析风场以及涡度场和散度场分布均匀，结构清晰，气旋中心显著，且分析场中观测起主导作用；采用信号自由度（DFS）方法对融合方法进行定量评估，发现相对常规3DVAR方法，带正则化约束的3DVAR融合系统中观测数据提供的DFS较多，同时提高了观测场对分析场的影响；基于独立观测资料对融合结果进行检验发现相对华南中尺度模式和常规3DVAR方法的统计结果，带正则化约束的3DVAR方法得到的风场具有最小的均方根误差和最大的相关系数。
- 3DVAR /
- 正则化 /
- QuikSCAT /
- 信号自由度 /
- 模型函数
Abstract: A 3DVAR method with regularization constraints is proposed to blend sea surface wind data in the South China Sea based on the traditional 3DVAR and regularization technology of the inverse problem, and the model function method which is used to determine the reasonable regularization parameters and then the blended experiments of the satellite scatterometer (QuikSCAT) and Guang Zhou Mesoscale Model (GZMM) sea surface wind field data are carried out for a typhoon case. Results show that when we use the regularization method for experiments, the false information caused by the traditional 3DVAR is eliminated obviously and the noise is almost disappeared, at the same time, the wind field and vorticity field as well as divergence field are distributed evenly, and the structure is clear, more importantly, it is clear that the cyclone center is remarkable, and observation is dramatic in the analysis field. Besides, the degrees of freedom for signal (DFS) method is used to evaluate blended systems quantitatively, it is found that the regularized constraint 3DVAR system has a higher DFS and observation influence related to traditional 3DVAR. The blended results are tested based on the independent observation data, it indicates that the result of regularized constraint 3DVAR method has the smallest root mean square error and maximum correlation coefficient, which is better than the statistical result of GZMM and the conventional 3DVAR method.
- 3DVAR /
- regularization /
- QuikSCAT /
- the degrees of freedom for signal /
- model function

HTML全文

参考文献(27)

Zou Xiaolei, Xiao Qingnong. Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme[J]. Journal of the Atmospheric Sciences, 2000, 57(6):836-860.

Chen S, Vandenberghe F, Petty G W, et al. Application of SSM/I satellite data to a hurricane simulation[J]. Quarterly Journal of the Royal Meteorological Society, 2004, 130(598):801-825.

Chen Shuhua. The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulations[J]. Monthly Weather Review, 2007, 135(2):549-566.

Singh R, Pal P K, Kishtawal C M, et al. The impact of variational assimilation of SSM/I and QuikSCAT satellite observations on the numerical simulation of Indian Ocean tropical cyclones[J]. Weather and Forecasting, 2008, 23(3):460-476.

Tang Wenqing, Liu W T. Objective Interpolation of Scatterometer Winds[M]. California:JPL Publication, 1996.

Royle J A, Berliner L M, Wikle C K, et al. A hierarchical spatial model for constructing wind fields from scatterometer data in the labrador sea[M]//Case Studies in Bayesian Statistics. New York:Springer, 1999:367-382.

Perrie W, Dunlap E, Vachon P W, et al. Marine wind analysis from remotely sensed measurements[J]. Canadian Journal of Remote Sensing, 2002, 28(3):450-465.

Atlas R. A multiyear global surface wind velocity dataset using SSM/I wind observations[J]. Bulletin of the American Meteorological Society, 1996, 77(5):869-882.

Chao Yi, Li Zhijin, Kindle J C, et al. A high-resolution surface vector wind product for coastal oceans:Blending satellite scatterometer measurements with regional mesoscale atmospheric model simulations[J]. Geophysical Research Letters, 2003, 30(1):1013.

凌征, 王桂华, 陈大可, 等. 中国近海风场融合[C]//中国"数字海洋"论坛论文集. 天津:国家海洋信息中心, 2008:90-94. Ling Zheng, Wang Guihua, Chen Dake, et al. China's offshore wind field fusion[C]//China's "Digital Ocean" Forum Proceedings. Tianjin:National Maritime Information Centres, 2008:90-94.

蒋兴伟, 宋清涛. 海洋卫星微波遥感技术发展现状与展望[J]. 科技导报, 2010, 28(3):105-111. Jiang Xingwei, Song Qingtao. Satellite microwave measurements of the global oceans and future missions[J]. Science & Technology Review, 2010, 28(3):105-111.

齐亚琳, 林明森. 数据融合技术在海洋二号卫星数据中的应用[J]. 航天器工程, 2012, 21(3):117-123. Qi Yalin, Lin Mingsen. Application of the data fusion technique in HY-2 satellite data[J]. Spacecraft Engineering, 2012, 21(3):117-123.

刘宇昕, 张毅, 王兆徽, 等. 基于ASCAT微波散射计风场与NCEP再分析风场的全球海洋表面混合风场[J]. 海洋预报, 2014, 31(3):10-18. Liu Yuxin, Zhang Yi, Wang Zhaohui, et al. Global sea surface blended winds based on the scatterometer winds and NCEP reanalyzed winds[J]. Marine Forecasts, 2014, 31(3):10-18.

项杰,王慧鹏,王春明,等. 南海海面风场变分融合的初步研究[J]. 热带气象学报, 2015, 31(2):153-160. Xiang Jie, Wang Huipeng, Wang Chunming, et al. Preliminary study on variational blending of surface vector winds in South China Sea[J]. Journal of Tropical Meteorology, 2015, 31(2):153-160.

肖庭延, 于慎根, 王彦飞. 反问题的数值解法[M]. 北京:科学出版社, 2003. Xiao Tingyan, Yu Shengen, Wang Yanfei. Numerical Solution of Inverse Problem[M]. Beijing:Science Press, 2003.

刘继军. 不适定问题的正则化方法及应用[M]. 北京:科学出版社, 2005. Liu Jijun.. The Regularization Method and Application of Unsuitable Problem[M]. Beijing:Science Press, 2005.

Wang Wanqiu, Xie Pingping. A multiplatform-merged (MPM) SST analysis[J]. Journal of Climate, 2007, 20(9):1662-1679.

姜祝辉, 黄思训, 杜华栋, 等. 利用变分结合正则化方法对高度计风速资料调整海面风场的研究[J]. 物理学报, 2010, 59(12):8968-8977. Jiang Zhuhui, Huang Sixun, Du Huadong, et al. A new approach to adjusting sea surface wind using altimeter wind data by variational regularization method[J]. Acta Physica Sinica, 2010, 59(12):8968-8977.

赵延来, 黄思训, 杜华栋, 等. 正则化方法同化多普勒天气雷达资料及对降雨预报的影响[J]. 物理学报, 2011, 60(7):875-886. Zhao Yanlai測甠浈敵牡楮捧愠汓?牸敵慮氬椠穄慵琠楈潵湡孤?嵮??丠略浴攠牡楬献挠桒敥??慬瑡桲敩浺慡瑴楩歯???づ????ㄠ????????????扩牮?嬠??嵰?坬慥湲朠?婡???甠汤瑡楴?瀠慡牮慤洠敩瑴敳爠?呮楦歬桵潥湮潣癥?牯敮朠異汲慥牣楩穰慩瑴楡潴湩?慮渠摦?浲潥摣敡汳?晛畊湝挮琠楁潣湴?愠灐灨特潳慩捣桡?瑓潩?瑩档敡?搠愲洰瀱攱搬??漰爨漷稩漺瘸?瀵爭椸游挶椮瀼汢敲￣晛漲爰?挠桗潡潮獧椠湓杩?牨敥杮畧氬愠版極穡慮瑧椠潓湩?灵慮爬愠浘敩瑡敮牧猠孊?嵥???潴甠牡湬愮氠?潨晲??漭浤灩畭瑥慮瑳楩潯湮慡汬?慩湯摮??灰灨汥楲敩摣??慯瑭桯敧浲慡瑰楨捹猠???は????????????????ㄠ?????扯牤?孬??嵵?剣潴摩杯敮爠獡???????渠癩敮爠獔敩??敯瑮桯潶搠獲?晧潵牬??瑩浺潡獴灩桯敮牛楊捝?匠潊畯湵摲楮湡杬?呯桦攠潇牥祯?慨湹摳?偣牡慬挠瑒楥捳敥孡?嵣??卓楰湡杣慥瀠潐牨敹?坩潣牳氬搠′匰挱椶攬渠琱椲昱椨挱′倩町戱氲椱猰栴椭渱朲??漵?值瑢敲￣?琲搱????ぴ?ぐ??扰牵?孳??嵮??畡灢畯?????慹甮琠桑極敩牫?偃???慳牣潩捥桮散?匠???癡愠汰畲慯瑤極潣湴?潵晳?瑲栧敳?業浡灮慵捡瑬?漠晖?潲扳獩敯牮瘲愮琲椲漰渰猱?潅湂?慏湌慝氮祛猲攰猱?椭渱‰???慝渮摤????噪慰牬?扮慡獳敡搮?潯湶?楱湵晩潫牳浣慡瑴椯潱湳?捡潴渠瑤敯湣琮孨?嵭???潲渾瑛栲氲祝?坅敢慵瑣桨敩爠?刬攠癇楲敡睢??㈠えㄠ???????????㈠??????luation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data[J]. Journal of Atmospheric and Oceanic Technology, 2002, 19(12):2049-2062.

Hoffman R N, Leidner S M, Henderson J M, et al. A two-dimensional variational analysis method for NSCAT ambiguity removal:Methodology, sensitivity, and tuning[J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(5):585-605.

Parrish D F, Derber J C. The National Meteorological Center's spectral statistical-interpolation analysis system[J]. Monthly Weather Review, 1992, 120(8):1747-1763.

曹小群, 黄思训, 张卫民, 等. 区域三维变分同化中背景误差协方差的模拟[J]. 气象科学, 2008, 28(1):8-14. Cao Xiaoqun, Huang Sixun, Zhang Weimin, et al. Modeling background error covariance in regional 3D-VAR[J]. Scientia Meteorologica Sinica, 2008, 28(1):8-14.

Kunisch K. On a class of damped Morozov principles[J]. Computing, 1993, 50(3):185-198.

Hansen P C, O'Leary D P. The use of the L-curve in the regularization of discrete ill-posed problems[J]. SIAM Journal on Scientific Computing, 1993, 14(6):1487-1503.

Golub G H, Heath M, Wahba G. Generalized cross-validation as a method for choosing a good ridge parameter[J]. Technometrics, 1979, 21(2):215-223.

Kunisch K, Zou J. Iterative choices of regularization parameters in linear inverse problems[J]. Inverse Problems, 1998, 14(5):1247.

Lu S, Pereverzev S V. Multi-parameter regularization and its

施引文献

资源附件(0)

访问统计

计量

文章访问数: 817
HTML全文浏览量: 10
PDF下载量: 673
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

基于变分多参数正则化方法融合卫星散射计资料

doi: 10.3969/j.issn.0253-4193.2017.12.012

计量

Blending satellite scatterometer data based on variational with multi-parameter regularization method

计量

目录

留言板

基于变分多参数正则化方法融合卫星散射计资料

doi: 10.3969/j.issn.0253-4193.2017.12.012

计量

出版历程

Blending satellite scatterometer data based on variational with multi-parameter regularization method

计量

出版历程

目录