Articles | Volume 14, issue 2
https://doi.org/10.5194/os-14-301-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-14-301-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Apr 2018
Research article |
| 24 Apr 2018
| 24 Apr 2018
Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle
Mei Hong
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Xi Chen
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, China
Ren Zhang
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Dong Wang
Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies,
State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, 210093, China
Shuanghe Shen
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Vijay P. Singh
Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843, USA
Related subject area
Approach: Numerical Models | Depth range: Surface | Geographical range: All Geographic Regions | Phenomena: Temperature, Salinity and Density Fields
An empirical model for the statistics of sea surface diurnal warming
M. J. Filipiak, C. J. Merchant, H. Kettle, and P. Le Borgne
Ocean Sci., 8, 197–209, https://doi.org/10.5194/os-8-197-2012, https://doi.org/10.5194/os-8-197-2012, 2012
Cited articles
Ashok, K., Guan, Z., and Yamagata, T.: Impact of the Indian Ocean Dipole on the decadal relationship between the Indian mon soon rainfall and ENSO, Geophys. Res. Lett., 28, 4499–4502, https://doi.org/10.1029/2001GL013294, 2001.
Balmaseda, M. A., Davey, M. K., and Anderson, D. L. T.: Decadal and seasonal dependence of ENSO prediction skill, J. Climate, 8, 2705–2715, https://doi.org/10.1175/1520-0442(1995)008<2705:DASDOE>2.0.CO;2, 1995.
Barnston, A. G., Tippett, M. K., L'Heureux, M. L., Li, S., and Dewitt, D. G.: Skill of real-time seasonal ENSO model predictions during 2002–2011, B. Am. Meteorol. Soc., 93, 631–651, https://doi.org/10.1175/BAMS-D-11-00111.1, 2012.
Belkin, M. and Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation, Netural Comput., 15, 1373–1391, https://doi.org/10.1162/089976603321780317, 2003.
Bjerknes, J.: Atmsopheric telconnections from the equtorail Pacific, Mon. Weather Rev., 97, 163–172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2, 1969.
Büttner, K., Salau, J., and Krieter, J.: Temporal correlation coefficient for directed networks, Springerplus, 5, 1198–1203, https://doi.org/10.1186/s40064-016-2875-0, 2016.
Cao, H. X.: Self-memorization Equation in Atmospheric Motion, Sci. China Ser. B, 36, 845–855, 1993.
Carton, J. A. and Giese, B. S.: A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA), Mon. Weather Rev., 136, 2999–3011, https://doi.org/10.1175/2007MWR1978.1, 2008.
Chen, D., Zebiak, S. E., Busalacchi, A. J., and Cane, M. A.: An Improved Procedure for El Nirio Forecasting: Implications for Predictability, Science, 269, 1699–1702, 1995.
Chen, X. D., Xia, J., and Xu, Q.: Differential Hydrological Grey Model (DHGM) with self-memory function and its application to flood forecasting, Sci. China Tech. Sci., 52, 1039–1049, https://doi.org/10.1007/s11431-008-0320-5, 2009.
Chen, G., Shao, B. M., Han, Y., Ma, J., and Chapron, B.: Modality of semiannual to multidecadal oscillations in global sea surface temperature variability, J. Geophys. Res., 115, 1–14, https://doi.org/10.1029/2009JC005574, 2010.
Clarke, A. J. and Van Gorder, S.: Improving El Niño prediction using a space–time integration of Indo-Pacific winds and equatorial Pacific upper ocean heat content, Geophys. Res. Lett., 30, 1399, https://doi.org/10.1029/2002GL016673, 2003.
Davey, M., Huddleston, M., Sperber, K. R., Braconnot, P., Bryan, F., Chen, D., Colman, R., and Cooper, C.: A study of coupled model climatology and variability in tropical ocean regions, Clim. Dynam., 18, 403–420, https://doi.org/10.1007/s00382-001-0188-6, 2002.
Delecluse, P., Davey, M., Kitamura, Y., Philander, S., Suarez, M., and Bengtsson, L.: TOGA review paper: coupled general circulation modeling of the tropical Pacific, J. Geophys. Res., 103, 14357–14373, https://doi.org/10.1029/97JC02546, 1998.
Dommenget, D. and Latif, M.: A Cautionary Note on the Interpretation of EOFs, J. Climate, 15, 216–225, https://doi.org/10.1175/1520-0442(2002)015<0216:ACNOTI>2.0.CO;2, 2002.
Drosdowsky, W.: Statistical prediction of ENSO (Niño 3) using sub-surface temperature data, Geophys. Res. Lett., 33, L03710, https://doi.org/10.1029/2005GL024866, 2006.
Everitt, B. S. and Skrondal, A.: Cambridge Dictionary of Statistics, Cambridge University Press, Cambridge, 2010.
Feng, G. L., Cao, H. X., Gao, X. Q., Dong, W. J., and Chou, J. F.: Prediction of precipitation during summer monsoon with self-memorial model, Adv. Atmos. Sci., 18, 701–709, 2001.
Fraedrich, K.: Estimating weather and climate predictability on attractors, J. Atmos. Sci., 44, 722–728, https://doi.org/10.1175/1520-0469(1987)044<0722:EWACPO>2.0.CO;2, 1987.
Glantz, M. H., Katz, R. W., and Nicholls, N. (Eds.): Teleconnections linking worldwide climate anomalies, Cambridge University Press, Cambrige, UK, 74 pp., 1991.
Golbraikh, A. and Tropsha, A.: Beware of q2!, J. Mol. Graph. Model., 20, 269–276, https://doi.org/10.1016/S1093-3263(01)00123-1, 2002.
Golbraikh, A., Shen, M., Xiao, Z. Y., Xiao, Y. D., Lee, K.-H., and Tropsha, A.: Rational selection of training and test sets for the development of validated QSAR models, J. Comput. Aid. Mol. Des., 17, 241–253, https://doi.org/10.1023/A:1025386326946, 2003.
Gu, X. Q.: A spectral model based on atmospheric self-memorization principle, Chinese Sci. Bull., 43, 1692–1702, https://doi.org/10.1007/BF02883967, 1998.
Hong, M., Zhang, R., Wu, G. X., and He, J. H.: A Nonlinear Dynamic System Reconstruction of the Subtropical High Characteristic Index based on Genetic Algorithm, Chinese J. Atmos. Sci., 31, 346–352, 2007.
Hong, M., Zhang, R., Ma, C. C., Wang, D., Bai, C., Min, J., and Chen, Y. D.: A Non-Linear Dynamical–Statistical Model for Reconstruction of the Air–Sea Element Fields in the Tropical Pacific Ocean, Atmos. Ocean, 52, 26956–26980, https://doi.org/10.1080/07055900.2014.908765, 2014.
Hong, M., Wang, D., Zhang, R., Chen, X., Ge, J. J., and Yu, D. D.: Reconstruction and forecast experiments of a statistical-dynamical model of the Western Pacific subtropical high and Eastern Asian summer monsoon factors, Weather Forecast., 30, 206–216, https://doi.org/10.1175/MWR-D-15-0151.1, 2015.
Hong, M., Zhang, R., Hu, Y., Yu, D. D., and Ge, J. J.: Catastrophe and Mechanism Analyses of Multiple Equilibria in the Western Pacific Subtropical High System Based on Objective Fitting of Spatial Basis Functions, Mon. Weather Rev., 144, 997–1015, https://doi.org/10.1175/MWR-D-15-0151.1, 2016.
Hong, M., Zhang, R., Li, M., Wang, S., Zeng, W., and Wang, Z. X.: Bifurcations and catastrophes in a nonlinear dynamical model of the western Pacific subtropical high ridge line index and its evolution mechanism, Theor. Appl. Climatol., 129, 363–384, https://doi.org/10.1007/s00704-016-1777-y, 2017.
Hu, T. S., Lam, K. C., and Ng, S. T.: River flow time series prediction with a range-dependent neural network, Hydrolog. Sci. J., 46, 729–745, https://doi.org/10.1080/02626660109492867, 2001.
Hu, Y. J., Zhong, Z., Zhu, Y. M., and Ha, Y.: A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley, Theor. Appl. Climatol., 132, 1–11, https://doi.org/10.1007/s00704-017-2094-9, 2017.
Huang, J., Yi, Y., Wang, S., and Chou, J. F.: An analogue-dynamical long-range numerical weather prediction system incorporating historical evolution, Q. J. Roy. Meteorol. Soc., 119, 547–565, https://doi.org/10.1002/qj.49711951111, 1993.
Jin, E. K., James, L. K., Wang, B., Park, C.-K., Kang, I. S., Kirtman, B. P., Kug, J.-S., Kumar, A., Luo, J.-J., Schemm, J., Shukla, J., and Yamagata, T.: Current status of ENSO prediction skill in coupled ocean–atmosphere models, Clim. Dynam., 31, 647–664, https://doi.org/10.1007/s00382-008-0397-3, 2008.
Johnson, S. D., Battisit, D. S., and Sarachik, E. S.: Empirically Derived Markov Models and Prediction of Tropical Pacific Sea Surface Temperature Anomalies, J. Climate, 13, 3–17, https://doi.org/10.1175/1520-0442(2000)013<0003:EDMMAP>2.0.CO;2, 2000.
Kalnay, E., Kanamitsu, M., and Kistler, R.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–470, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Kim, J.-W., An, S.-I., Jun, S.-Y., Park, H.-J., and Yeh, S.-W.: ENSO and East Asian winter monsoon relationship modulation associated with the anomalous northwest Pacific anticyclone, Clim. Dynam., 49, 1157–1179, https://doi.org/10.1007/s00382-016-3371-5, 2017.
L'Heureux, M. L., Collins, D. C., and Hu, Z.-Z.: Linear trends in sea surface temperature of the tropical Pacific Ocean and implications for the El Ninõ-Southern Oscillation, Clim. Dynam., 40, 1223–1236, https://doi.org/10.1007/s00382-012-1331-2, 2013.
Liebmann, B. and Smith, C. A.: Description of a Complete (Interpolated) Outgoing Longwave Radiation Dataset, B. Am. Meteorol. Soc., 77, 1275–1277, https://doi.org/10.2307/26233278, 1996.
Luo, J.-J., Masson, S., Behera, S., Shingu, S., and Yamagata, T.: Seasonal climate predictabilityin a coupled OAGCM using a different approach forensemble forecasts, J. Climate, 18, 4474–4497, https://doi.org/10.1175/JCLI3526.1, 2005.
Mechoso, C. R., Robertson, A. W., Barth, N., Davey, M. K., Delecluse, P., Gent, P. R., Ineson, S., Kirtman, B., Latif, M., Le Treut, H., Nagai, T., Neelin, J. D., Philander, S. G. H., Polcher, J., Schopf, P. S., Stockdale, T., Suarez, M. J., Terray, L., Thual, O., and Tribbia, J. J.: The seasonal cycle over the tropical Pacific in coupled atmosphere–ocean generalcirculation models, Mon. Weather Rev., 123, 2825–2838, https://doi.org/10.1175/1520-0493(1995)123<2825:TSCOTT>2.0.CO;2, 1995.
Molteni, F., Ferranti, L., and Balmaseda, M.: ECMWF seasonal forecast system, CLIVAR Exch., 43, 7–9, 2007.
Moore, A. M., Zavala-Garay, J., Tang, Y., Kleeman, R., Weaver, A. T., Vialard, J., Sahami, K., Anderson David, L. T., and Fisher, M.: Optimal forcing patterns for coupled models of ENSO, J. Climate, 19, 4683–4699, https://doi.org/10.1175/JCLI3870.1, 2006.
Neelin, J. D., Latif, M., and Allaart, M. A. F.: Tropical air–sea interaction in general circulation models, Clim. Dynam., 7, 73–104, https://doi.org/10.1007/BF00209610, 1992.
Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., and Latora, V.: Graph metrics for temporal networks, in: Temporal networks, edited by: Holme, P. and Saramäki, J., Springer, Berlin, 15–40, https://doi.org/10.1007/978-3-642-36461-7_2, 2013.
Palmer, T. N., Alessandri, A., Andersen, U., and Thomson, M. C.: Development of a European multi-model ensemble system forseasonal to interannual prediction (DEMETER), B. Am. Meteorol. Soc., 85, 853–872, https://doi.org/10.1175/BAMS-85-6-853, 2004.
Phelps, M. W., Kumar, A., and O'Brien, J. J.: Potential Predictability in the NCEP CPC Dynamical Seasonal Forecast System, J. Climate, 17, 3775–3785, https://doi.org/10.1175/1520-0442(2004)017<3775:PPITNC>2.0.CO;2, 2004.
Philander, S. G., Pacanowski, R. C., Lau, N.-C., and Nath, M. J.: Simulation of ENSO with a global atmospheric GCM coupled to a high resolution, tropical Pacific Ocean GCM, J. Climate, 5, 308–329, https://doi.org/10.1175/1520-0442(1992)005<0308:SOEWAG>2.0.CO;2, 1992.
Qin, G. H. and Li, Z. H.: Over-fitting of BP NN research and its application, Engin. J. Wuhan Univ., 39, 1671–1679, 2006.
Rasmusson, E. M. and Carpenter, T. H.: Variations in tropical seasurface temperature and surface wind fields associated with the Southern Oscillation/E1 Niño, Mon. Weather Rev., 10, 354–384, https://doi.org/10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2, 1982.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An improved in situand satellite SST analysis for climate, J. Climate, 15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015, 2002.
Saha, S., Nadiga, C., Thiaw, J., and Peña, M.: The NCEP climate forecast system, J. Climate, 19, 3483–3517, https://doi.org/10.1175/JCLI3812.1, 2006.
Saji, N. H., Goswami, B. N., Vinayachandran, P. N., and Yamagata, T.: A dipole mode in the tropical Indian Ocean, Nature, 401, 360–363, https://doi.org/10.1038/43854, 1999.
Smith, T. M.: Improved extended reconstruction of SST (1854–1997), J. Climate, 17, 2466–2477, https://doi.org/10.1175/1520-0442(2004)017<2466:IEROS>2.0.CO;2, 2004.
Takens, F.: Detecting strange attractors in fluid turbulence, Lect. Notes Math., 898, 361–381, 1981.
Timmermann, A., Voss, H. U., and Pasmanter, R.: Empirical Dynamical System Modeling of ENSO Using Nonlinear Inverse Techniques, J. Phys. Oceanogr., 31, 1579–1598, https://doi.org/10.1175/1520-0485(2001)031<1579:EDSMOE>2.0.CO;2, 2001.
Tofallis, C.: A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation, J. Oper. Res. Soc., 66, 1352–1362, https://doi.org/10.1057/jors.2014.103131358, 2015.
Tomita, T. and Yasunari, T.: Role of the northeast winter monsoon on the biennial oscillation of the ENSO/monsoon system, J. Meteorol. Soc. Jpn., 74, 399–413, https://doi.org/10.2151/jmsj1965.74.4_399, 1996.
Trenberth, E. K., Branstator, G. W., Karoly, D., Kumar, A., Lau, N. C., and Ropelewski, C.: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures, J. Geophys. Res., 107, 14291–14324, https://doi.org/10.1029/97JC01444, 1998.
Wang, B., Wu, R., and Lukas, R.: Roles of western North Pacific wind variation in thermocline adjustment and ENSO phase transition, J. Meteorol. Soc. Jpn., 77, 1–16, https://doi.org/10.2151/jmsj1965.77.1_1, 1999.
Wang, B., Wu, R., and Li, T.: Atmoshere-warm ocean interaction and its impacts on Asian-Australian monsoon variation, J. Climate, 16, 1195–1211, https://doi.org/10.1175/1520-0442(2003)16<1195:AOIAII>2.0.CO;2, 2003.
Wang, B., Lee, J. H., Kang, I. S., Shukla, J., Park, C.-K., Kumar, A., Schemm, J., Cocke, S., Kug, J.-S., Luo, J.-J., Zhou, T., Wang, B., Fu, X., Yun, W.-T., and Alves O.: Advance and prospectus of seasonal prediction: assessment of the APCC/CIiPAS 14-Model Ensemble Retrospective Seasonal Prediction (1980–2004), Clim. Dynam., 33, 93–117, https://doi.org/10.1007/s00382-008-0460-0, 2009.
Wang, C., Weisberg, R. H., and Virmani, J. I.: Western Pacific interannual variability associated with the El Niño–Southern Oscillation, J. Geophys. Res., 104, 5131–5149, https://doi.org/10.1029/1998JC900090, 1999.
Wang, L.: Intelligent optimization algorithms and its application, Tsinghua University Press, Chendu, 23–24, 2001.
Wang, L., Chen, W., and Huang, R. H.: Interdecadal modulation of PDO on the impact of ENSO on the east Asian winter monsoon, Geophys. Res. Lett., 35, L20702, https://doi.org/10.1029/2008GL035287, 2008.
Wang, W. C., Chau, K. W., Cheng, C. T., and Qiu, L.: A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, J. Hydrol., 374, 294–306, https://doi.org/10.1016/j.jhydrol.2009.06.019, 2009.
Webster, P. J., Moore, A. M., Loschnigg, J. P., and Leben, R. R.: Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98, Nature, 401, 356–360, https://doi.org/10.1038/43848, 1999.
Weinberger, K. Q. and Saul, L.: Unsupervised learning of image manifolds by semidefinite programming, Int. J. Comput. Vis., 70, 77–90, https://doi.org/10.1007/s11263-005-4939-z, 2006.
Xu, B. C., Wang, Z. S., Wu, J. P., and Zhou, E. M.: Interaction between sea surface temperature (SST) of information regions and southern oscillation index (SOI) in Tropical Pacific Ocean, Mar. Sci. Bull., 12, 211–225, 1993.
Yang, S., Lau, K. M., and Kim, K. M.: Variations of the East Asian jet stream and Asian-Pacific-American winter climate anomalies, J. Climate, 15, 306–325, https://doi.org/10.1175/1520-0442(2002)015<0306:VOTEAJ>2.0.CO;2, 2002.
Yang, S.-H. and Lu, R.: Predictability of the East Asian winter monsoon indices by the coupled models of ENSEMBLES, Adv. Atmos. Sci., 31, 1279–1292, https://doi.org/10.1007/s00376-014-4020-8, 2014.
Yim, S. Y., Wang, B., and Kwon, M.: Interdecadal change of the controlling mechanisms for East Asian early summer rainfall variation around the mid-1990s, Clim. Dynam., 42, 1325–1333, https://doi.org/10.1007/s00382-013-1760-6, 2013.
Yim, S.-Y., Wang, B., Xing, W., and Lu, M.-M.: Prediction of Meiyu rainfall in Taiwan by multi-lead physical empirical models, Clim. Dynam., 44, 3033–3042, https://doi.org/10.1007/s00382-014-2340-0, 2015.
Yoon, J. and Yeh, S. W.: Influence of the Pacific Decadal Oscillation on the relationship between El Niño and the northeast Asian summer monsoon, J. Climate, 23, 4525–4537, https://doi.org/10.1175/2010JCLI3352.1, 2010.
Yu, H., Huang, J., and Chou, J.: Improvement of Medium-Range Forecasts Using the Analogue-Dynamical Method, Mon. Weather Rev., 142, 1570–1587, https://doi.org/10.1175/MWR-D-13-00250.1, 2014a.
Yu, H., Huang, J., Li, W., and Feng, G.: Development of the analogue-dynamical method for error correction of numerical forecasts, J. Meteorol. Res.-PRC, 28, 934–947, https://doi.org/10.1007/s13351-014-4077-4, 2014b.
Zhang, R., Hong, M., Sun, Z. B., Niu, S. J., Zhu, W. J., Min, J. Z., and Wan, Q. L.: Non-linear Dynamic Model Retrieval of Subtropical High Based on Empirical Orthogonal Function and Genetic Algorithm, Appl. Math. Mech.-Engl., 27, 1645–1654, 2006.
Zhang, R., Hong, M., Wang, H. Z., Chen, Y. D., and Wang, Y. L.: Retrieval of the non-linear dynamic forecast model of El Nino/La Nina index based on the genetic algorithm optimization, Chin. J. Geophys., 51, 1354–1362, 2008.
Zhang, R. H., Sumi, A., and Kimoto, M.: Impact of El Niño on the East Asian monsoon: A diagnostic study of the '86/87 and '91/92 events, J. Meteorol. Soc. Jpn., 74, 49–62, https://doi.org/10.2151/jmsj1965.74.1_49, 1996.
Zhang, R. H., Zhou, G. Q., and Chao, J. P.: ENSO Dynamics and Its Prediction, Chinese J. Atmos. Sci., 27, 674–688, 2003a.
Zhang, R. H., Zebiak, S. E., Kleeman, R., and Keenlyside, N.: A new intermediate coupled model for El Niño simulation and prediction, Geophys. Res. Lett., 30, 153–166, https://doi.org/10.1029/2003GL018010, 2003b.
Zhang, Y. L., Yu, Y. Q., and Duan, W. S.: The spring prediction barrier of ENSO in retrospective prediction experiments as shown by the four coupled ocean–atmosphere models, Acta Meteorol. Sin,, 70, 506–519, 2012.
Zhao, J. H., Liu, X. Y., Jiang, H. Y., and Li, L. P.: Characteristics of Sea Surface Height in Tropical Pacific and its relationship with ENSO events, Meteorol. Environ. Sci., 35, 33–39, 2012.
Zheng, C. W., Pan, J., and Li, C. Y.: Global oceanic wind speed trends, Ocean Coast. Manage., 129, 15–24, https://doi.org/10.1016/j.ocecoaman.2016.05.001, 2016.
Zheng, C. W., Wang, Q., and Li, C. Y.: An overview of medium- to long-term predictions of global wave energy resources, Renew. Sust. Energ. Rev., 79, 1492–1502, https://doi.org/10.1016/j.rser.2017.05.109, 2017.
Zhou, L.-T. and Wu, R. G.: Respective impacts of the East Asian winter monsoon and ENSO on winter rainfall in China, J. Geophys. Res., 115, 1–11, https://doi.org/10.1029/2009JD012502, 2010.
Short summary
With the objective of tackling the problem of inaccurate long-term ENSO forecasts, a new forecasting model of the SSTA field was proposed based on a dynamic system reconstruction idea and the principle of self-memorization. The improved model was used to forecast the SSTA field. The forecasted SSTA fields of three types of events are accurate. The improved model also has good forecasting results of the ENSO index. So our model has an advantage in ENSO prediction precision and length.
With the objective of tackling the problem of inaccurate long-term ENSO forecasts, a new...
