人工神经网络在南海近海面气温反演中的应用研究

doi:10.11978/j.issn.1009-5470.2012.02.002

Journal of Tropical Oceanography ›› 2012, Vol. 31 ›› Issue (2): 7-14.doi: 10.11978/j.issn.1009-5470.2012.02.002cstr: 32234.14.j.issn.1009-5470.2012.02.002

• Marine Meteorology • Previous Articles Next Articles

Retrieving near-surface air temperature in the South China Sea using artificial neural network

WU Xin-rong^1,2,3,4, HAN Gui-jun^1,2, ZHANG Xue-feng^1,2,3,4, WANG Xi-dong^1,2,3,4

1. Key Laboratory of Marine Environmental Information Technology, SOA, Tianjin 300171, China 2. National Marine Data and Information Service, Tianjin 300171, China 3. South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China 4. Graduate University of Chinese Academy of Sciences, Beijing 100039, China

Received:2010-11-10 Revised:2011-01-18 Online:2012-06-05 Published:2012-06-05

Abstract

Abstract: Based on artificial neural network (ANN), the authors retrieved near-surface air temperature (AT) from sea surface temperature (SST), wind speed (WS) and sea level pressure (SLP) of the International Comprehensive Ocean-Atmosphere Dataset (ICOADS). Modeling sample spans from 1981 to 2000, while validating sample spans from 2001 to 2008. The adopted ANN introduces momentum factor to back propagation (BP) algorithm to escape from local extremes. In addition, batch processing gradient descent method was used to remove the effect of sequential training. Retrieving results in the South China Sea (SCS) demonstrates that ANN is better than multi-factor linear regression, especially for coastal areas during spring and winter, where strong non-linear relation exists between SST, WS, SLP and AT. In conclusion, ANN behaves similarly for each month, with root mean square error (RMSE) between 1.5 ℃ and 1.8 ℃ and mean absolute error (MAE) between 1.1 ℃ and 1.3 ℃ .

Key words: Artificial neural network, BP algorithm, Multi-factor linear regression

CLC Number:

P731

WU Xin-rong,HAN Gui-jun,ZHANG Xue-feng,WANG Xi-dong. Retrieving near-surface air temperature in the South China Sea using artificial neural network[J].Journal of Tropical Oceanography, 2012, 31(2): 7-14.

References

KUBOTA M, SHIKAUCHI A. Air temperature at ocean surface derived from surface-level humidity[J]. J Oceanogr, 1995, 51: 619-634. KONDA M, IMASATO N, SHIBATA A. A new method to determine near-sea surface air temperature by using satellite data[J]. J Geophys Res, 1996, 102(c6): 14349-14360. JONES C, PETERSON P, GAUTIER C. A new method for deriving ocean surface specific humidity and air temperature: An artificial neural network approach[J]. J App Meteorol, 1999, 38: 1229-1245. SINGH R, VASUDEVAN B G., PAL P K et al. Artificial neural network approach for estimation of surface specific humidity and air temperature using multifrequency scanning microwave radiometer[J]. J Earth Syst Sci, 2004, 113(1): 89-101. SINGH R, KISHTAWAL C M, JOSHI P C. Estimation of monthly mean air-sea temperature difference from satellite observations using genetic algorithm[J]. Geophys Res Lett, 2005, 32, L02807.1-L02807.5. SINGH R, JOSHI P C, KISHTAWAL C M. A new method to determine near surface air temperature from satellite observations[J]. Int J Remote Sens, 2006, 27: 2831-2846. JACKSON D L, WICK G A, BATES J J. Near-surface retrieval of air temperature and specific humidity using multisensor microwave satellite observations[J]. J Geophys Res, 2006, 111, D10306, doi:10.1029/2005JD006431 伍玉梅, 何宜军, 孟雷. 利用卫星资料反演月平均近海面气温和湿度[J]. 海洋与湖沼, 2008, 39(6): 546-551. 王丽静, 何宜军, 张彪: 利用遗传算法反演实时近海面气温和比湿[J]. 海洋科学, 2009, 33(4): 20-24. 蒋宗礼. 人工神经网络导论[M]. 北京: 高等教育出版社, 2001: 39-54. ZHUANG W, XIE S P, WANG D X, et al. Intraseasonal variability in sea surface height over the South China Sea[J]. J Geophys Res, 115, C04010, doi:10.1029/2009JC005647. 陈颖珺, 谢强, 蒙伟光, 等. 不同海表面温度对南海台风“杜鹃”的影响试验[J]. 热带气象学报, 2009, 25(4): 401-406. QIU C H, WANG D X, HIROSHI K, et al. Validation of AVHRR and TMI-derived sea surface temperature in the northern South China Sea[J]. Cont Shelf Res, 2009, 29: 2358-2366. SHU Y Q, ZHU J, XIAO X J, et al. Performance of four sea surface temperature assimilation schemes in the South China Sea[J]. Cont Shelf Res, 2009, 29: 1489-1501.

Retrieving near-surface air temperature in the South China Sea using artificial neural network

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 1

Metrics

Comments

Recommended 0