Theoretical and Applied Climatology

, Volume 136, Issue 1–2, pp 703–715 | Cite as

A seasonal forecast scheme for the Inner Mongolia spring drought part-II: a logical reasoning evidence-based method for spring predictions

  • Tao GaoEmail author
  • Wulan Wulan
  • Xiao YuEmail author
  • Zelong Yang
  • Jing Gao
  • Weiqi Hua
  • Peng Yang
  • Yaobing Si
Original Paper


Spring precipitation is the predominant factor that controls meteorological drought in Inner Mongolia (IM), China. This study used the anomaly percentage of spring precipitation (PAP) as a drought index to measure spring drought. A scheme for forecasting seasonal drought was designed based on evidence of spring drought occurrence and speculative reasoning methods introduced in computer artificial intelligence theory. Forecast signals with sufficient lead-time for predictions of spring drought were extracted from eight crucial areas of oceans and 500-hPa geopotential height. Using standardized values, these signals were synthesized into three examples of spring drought evidence (SDE) depending on their primary effects on three major atmospheric circulation components of spring precipitation in IM: the western Pacific subtropical high, North Polar vortex, and East Asian trough. Thresholds for the SDE were determined following numerical analyses of the influential factors. Furthermore, five logical reasoning rules for distinguishing the occurrence of SDE were designed after examining all possible combined cases. The degree of confidence in the rules was determined based on estimations of their prior probabilities. Then, an optimized logical reasoning scheme was identified for judging the possibility of spring drought. The scheme was successful in hindcast predictions of 11 of the 16 (accuracy: 68.8%) spring droughts that have occurred during 1960–2009. Moreover, the accuracy ratio for the same period was 82.0% for drought (PAP ≤ −20%) or not (PAP > −20%). Predictions for the recent 6-year period (2010–2015) demonstrated successful outcomes.


Spring drought Forecast signals Artificial intelligence Evidence theory Speculative reasoning method 



The first author cordially thanks the anonymous reviewers and editors for their constructive comments and suggestions for the improvement of this paper. Many thanks are offer to the NCEP/NCAR of NOAA and the National Climate Center of the China Meteorological Administration for providing datasets. This study was supported by the National Natural Science Foundation of China (No. 40965007 and No. 41665003) and Financial Support for Scientific Research of Inner Mongolia Meteorological Bureau, China Meteorological Administration (No. nmqxkjcx201607).


  1. Campbell SD, Olson SH (1987) Recognizing low-altitude wind shear hazards from Doppler weather radar: an artificial intelligence approach. J of Atmospheric Oceanic Technol 4(1):5–18CrossRefGoogle Scholar
  2. Cao XZ, Min JJ, Liu HZ, Zhao SR, Wang SG (2008) Application of classification and integration to rainfall forecast (in Chinese with an English abstract). Meteorological Monthly 34(10):3–11Google Scholar
  3. Chen YC, Gao T (2008) Technical analysis for influence factors of food production in Inner Mongolia and its development. Inner Mongolia Agr Sci Technol 4:1–6 (in Chinese with an English abstract)Google Scholar
  4. Elio R, de Haan J, Strong GS (1987) METEOR: an artificial intelligence system for convective storm forecasting. J of Atmos and Oceanic Technology 4:19–28CrossRefGoogle Scholar
  5. Fang X, Ji LD, Ma YQ (2002) The application of artificial intelligence technology in tropical cyclone forecast (in Chinese with an English abstract). Marine Forecasts 19(2):54–63Google Scholar
  6. Gao T, Yu X (2003) The influential factors on the variation of grain yield in Inner Mongolia Autonomous Region in recent 50 years (in Chinese with an English abstract). Resources Environ Arid Region 17(2):60–64Google Scholar
  7. Gao T, Zhang XB, Li YP, Wang HM, Xiao SJ, Wu L, Teng QB (2009) Potential predictors for spring season dust storm forecast in Inner Mongolia, China. Theor Appl Climatol 97:255–263CrossRefGoogle Scholar
  8. Gao T, Si YB, Yu X, Wulan, Yang P, Gao J (2018) A seasonal forecast scheme for the Inner Mongolia spring drought. Part-I: Dynamic characteristics of the atmospheric circulation and forecast signals. Theor Appl Climatol.
  9. Huang XY, Jin L (2013) An artificial intelligence prediction model based on principal component analysis for typhoon tracks (in Chinese with an English abstract). Chinese J Atmospheric Sci 37(5):1154–1164Google Scholar
  10. Jin L, Yao C, Huang XY (2008) A nonlinear artificial intelligence ensemble prediction model for typhoon intensity. Mon Weather Rev 136:4541–4554CrossRefGoogle Scholar
  11. Malmgren BA, Winter A (1999) Climate zonation in Puerto Rico on principal components analysis and an artificial neural network. J Clim 12:977–985CrossRefGoogle Scholar
  12. Nilsson NJ (1980) Principles of artificial intelligence. Tioga pub. CO. Morgan Kaufmann, San Francisco, p 476Google Scholar
  13. Peng GF, Duan X, Shu KN, Zhou Y (2007) Using KDD technology to study relationship between MGD and precipitation (in Chinese with an English abstract). Meteorological Sci Technol 35(2):252–257Google Scholar
  14. Shi CY, Huang CN and Wang JQ. 1993. Artificial intelligence theory (in Chinese). Tsinghua University Press, Beijing, pp446Google Scholar
  15. von Storch H, Zwiers FW (1999) Statistical analysis in climate research. Cambridge University press, Cambridge, pp 111–pp 113Google Scholar
  16. Sun JH. 2008. Rainfall forecast of Wending city based on artificial neural network model (in Chinese with an English abstract). Thesis of master degree, Chinese Marine UniversityGoogle Scholar
  17. Zhao HY (2007) The quantitative relation between biological yield of natural grasses and climate conditions in the east-north of Inner Mongolia (in Chinese with an English abstract). Pratacultural Sci 24(3):8–11Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Inner Mongolia Meteorological InstituteHohhotPeople’s Republic of China
  2. 2.Computer Application Academy of Inner MongoliaHohhotPeople’s Republic of China
  3. 3.Dalateqi Meteorological BureauInner MongoliaChina
  4. 4.Inner Mongolia Climate CenterHohhotChina
  5. 5.Student of Durham UniversityDurhamUK
  6. 6.Inner Mongolia Meteorological Data CenterHohhotChina
  7. 7.Inner Mongolia Meteorological Service CenterHohhotChina

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