Predicting summer rainfall in the Yangtze River basin with neural networks

International Journal of Climatology

Volumn 28, Issue 7, 2008, Pages 925-936

  Hartmann, Heike ^a   Becker, Stefan ^b   King, Lorenz ^a  

^a JUSTUS LIEBIG UNIVERSITY   (Germany)

^b UNIVERSITY OF WISCONSIN OSHKOSH   (United States)

Author keywords

China; Correlation analysis; Neural network analysis; Principal component analysis; Summer rainfall; Yangtze

Indexed keywords

ARTIFICIAL INTELLIGENCE; ATMOSPHERIC PRESSURE; ATMOSPHERIC TEMPERATURE; FINANCIAL DATA PROCESSING; FORECASTING; IMAGE CLASSIFICATION; OCEANOGRAPHY; PRECIPITATION (METEOROLOGY); PRINCIPAL COMPONENT ANALYSIS; RIVERS; SEA LEVEL; SUBMARINE GEOPHYSICS; TIME SERIES ANALYSIS; VEGETATION; WATER LEVELS; WATERSHEDS;

ACCURATE PREDICTION; INPUT VARIABLES; NEURAL NETWORK ALGORITHMS; NEURAL NETWORK TECHNIQUES; PRINCIPAL COMPONENT ANALYSIS (PCA); RAINFALL VARIABILITY; SCANDINAVIA; SEA LEVEL PRESSURES (SLP); SEA SURFACE TEMPERATURES (SSTS); SNOW DEPTHS; SOUTHERN OSCILLATION INDEX (SOI); SUMMER RAINFALL; TELECONNECTION INDICES; TIBETAN PLATEAU; TIME SERIES; TOTAL VARIANCE; YANGTZE RIVER;

NEURAL NETWORKS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CORRELATION; PRECIPITATION ASSESSMENT; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; RAINFALL; SEA LEVEL PRESSURE; SEA SURFACE TEMPERATURE; SOUTHERN OSCILLATION; SUMMER; TIME SERIES;

ASIA; ATLANTIC OCEAN; ATLANTIC OCEAN (EAST); CHINA; EURASIA; FAR EAST; QINGHAI-XIZANG PLATEAU; RUSSIAN FEDERATION; YANGTZE BASIN;

EID: 47049099411     PISSN: 08998418     EISSN: 10970088     Source Type: Journal    
DOI: 10.1002/joc.1588     Document Type: Article

References (33)

1. Backhaus K, Erichson B, Plinke W, Weiber R. 2003. Multivariate Analysemethoden. Springer: Berlin.
2. Bao C. 1987. Synoptic Meteorology in China. Springer: Berlin.
3. Barnston AG, Livzey RE. 1987. Classification, seasonality and persistence of Low-Frequency atmospheric circulation patterns. Monthly Weather Review 115: 1083-1126.
4. Becker S, Hartmann H, Zharig Q, Wu Y, Jiang T. 2007. Cyclicity analysis of precipitation regimes in the Yangtze river basin, china. International Journal of Climatology, (in press).
5. Bordi I, Fraedrich K, Jiang JM, Sutera A. 2004. Spatio-temporal variability of dry and wet periods in eastern China. Theoretical and Applied Climatology 79: 81-91, DOI: 10.1007/s00704-004-0053-8.
6. Cannon AJ, McKendry IG. 2002. A graphical sensitivity analysis for statistical climate models: application to Indian monsoon rainfall prediction by neural networks and multiple linear regression models. International Journal of Climatology 22: 1687-1708.
7. Chan JCL, Shi JE. 1999. Prediction of the summer monsoon rainfall over south China. International Journal of Climatology 19: 1255-1265.
8. Chang CP, Zhang Y, Li T. 2000. Interannual and Interdecadal variations of the east Asian summer monsoon and tropical pacific SSTs. Part I: Roles of the subtropical ridge. Journal of Climate 13: 4310-4325.
9. Chou C. 2003. Land-sea beating contrast in an idealized Asian summer monsoon. Climate Dynamics 21: 11-25, DOI: 10.1007/ s00382-003-0315-7.
10. Climate Prediction Center. 2007. Northern Hemisphere Teleconnection Patterns. Available: http://www.cpc.ncep.noaa.gov/data/teledoc/ telecontents.shtml, Accessed on May 2, 2007.
11. Comrie AC, Glenn EC. 1998. Principal componerns-based regionalization of precipitation regimes across the southwest United States and northern Mexico, with an application to monsoon precipitation variability. Climate Research 10: 201-215.
12. Gao H, Wang Y, He J. 2006. Weakening significance of ENSO as a predictor of precipitation in China. Geophysical Research Letters 33: L09807, DOI: 10.1029/2005GL025511.
13. Guhathakurta P, Rajeevan M, Thapliyal V. 1999. Long range forecasting Indian summer monsoon rainfall by a hybrid principal component neural network model. Meteorology and Atmospheric Physics 71: 255-266.
14. Guo Y, Zhao Y, Wang J. 2002. Numerical simulation of the Relationships between the 1998 Yangtze river valley flood and SST anomalies. Advances in Atmospheric Sciences 19: 391-404.
15. Hsieh WW, Tang B. 1998. Applying neural network models to prediction and data analysis in meteorology and oceanography. Bulletin of the American Meteorological Society 79: 18551870.
16. Jiang T, Zhang Q, Zhu D, Wu Y. 2006. Yangtze floods and droughts (China) and teleconnections with ENSO activities (1470-2003). Quaternary International 144: 29-37, DOI: 10.1016/j.quaint.2005.05.010.
17. Kistler R, Kalnay E, Collins W, Saba S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M. 2001. The NCEP_NCAR 50-Year reanalysis: Monthly means CD-ROM and documentation. Bulletin of the American Meteorological Society 82: 247-267.
18. Navone HD, Ceccatto HA. 1994. Predicting Indian monsoon rainfall: a neural network approach. Climate Dynamics 10: 305-312.
19. Philipp A. 2003. Zirkulationsdynamische Telekonnektivitätt des Sommernbiederschlags im südhemispherischen Afrika. PhD dissertation, University of Wuerzburg, Wuerzburg.
20. Philipp A, Jacobeit J. 2002. Comprehensive modes of teleconnections between southern African summer rainfall and the large-scale atmospheric circulation. In Geowissenschaftliche Untersuchungen in Afrika IV, Wiverzburger Geographische Arbeiten 97. Sponholz B (ed.). Selbstverlag des Instituts für Geographie: Wuerzburg.
21. Principe J, Lefebvre C, Lynn G, Fancourt C, Wooten D. 2005. NeuroSolutions -Documentation. NeuroDimension, Inc.: Gainesville, FL.
22. Qian W, Kang HS, Lee DK. 2002. Distribution of seasonal rainfall in the East Asian monsoon region. Theoretical and Applied Climatology 73: 151-168, DOI: 10.1007/s00704-002-0679-3.
23. Qian YF, Zheng YQ, Zhang Y, Miao Q. 2003. Responses to China's summer monsoon climate to snow anomaly over the tibetan plateau. International Journal of Climatology 23: 593-613, DOI: 10.1002/joc.901.
24. Sahai AK, Soman MK, Satyan V. 2000. All India summer monsoon rainfall prediction using artificial neural network. Climate Dynamics 16: 291-302.
25. Sahai AK, Pattanaik DR, Satyan V, Grimm AM. 2003. Teleconnections in recent time and prediction of Indian summer monsoon rainfall. Meteorology and Atmospheric Physics 84: 217-227, DOI: 10.1007/s00703-002-0595-1.
26. Smith TM, Reynolds RW. 2004. Improved extended reconstruction of SST (1854-1997). Journal of Climate 17: 2466-2477.
27. Teschl R, Randeu WL. 2006. A neural network model for short term river flow prediction. Natural Hazards and Earth System Sciences 6: 629-635.
28. Venkatesan C, Raskar SD, Tambe SS, Kulkarni BD, Keshavamurty RN. 1997. Prediction of all India summer monsoon rainfall using Error-back-propagation neural networks. Meteorology and Atmospheric Physics 62: 225-240.
29. Wu TW, Qian ZA. 2003. The relation between the Tibetan winter snow and the asian summer monsoon and rainfall: An observational investigation. Journal of Climate 16: 2038-2051.
30. Wu YJ, Gough WA, Jiang T, Kung HAT. 2006. The variation of floods in the middle reaches of the Yangtze River and ist teleconnections with El Niño events. Advances in Geosciences 6: 201-205.
31. Wu X, Cao H, Flitman A, Wei F, Feng G. 2001. Forecasting monsoon precipitation using artificial neural networks. Advances in Atmospheric Sciences 18: 950-958.
32. Xu Q. 1995. Analysis of causes and seasonal prediction of the severe floods in Yangtze/Huaihe basins during summer 1991. Advances in Atmospheric Sciences 12: 215-224.
33. Zhao M. 1999. Analysis of the 1998 Yangtse river flood and consideration of flood control strategy. In Proceedings of the '99 International Symposium on Flood Control, November 11-13, Beijing: 244-253.