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A new algorithm for seasonal precipitation forecast based on global atmospheric hydrological water budget

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  • Wu, Yong-Ping
  • Feng, Guo-Lin

Abstract

Precipitation forecast has been identified as one of the central issues in climate research. However, the underlying mechanisms of precipitation are far from being understood. In this paper, a new algorithm of forecasting precipitation based on law of conservation of mass in hydrological cycle is proposed and its feasibility is verified. The algorithm mainly include three steps: in the first step, the area we employ is divided into a number of sub-areas, the precipitation source and evaporation whereabouts equations for sub-regions are established, and the rationality of them can be verified by checking whether the precipitation source and evaporation equations meet a self-consistent relationship or not; in the second step, a conversion equation for sub-regional precipitation prediction will be established, which characterize the relationship between precipitation and evaporation in the sub-areas; in the last step, if the regional evaporation, precipitation and moisture divergence (convergence) function keep stable in a certain time scale, then precipitation forecast is achieved by evaporation anomalies and moisture divergence function, which can be predicted according to the prophase sea surface temperature and atmospheric circulation. Finally, the northern and southern hemispheres seasonal precipitation, evaporation and moisture divergence (convergence) weighting coefficients are calculated using this algorithm based on European centre for medium-range weather forecasts (ECMWF) interim re-analysis (ERA-Interim) dataset, which well verifies the feasibility of the algorithm. The obtained results may provide new insights for precipitation forecast in the future.

Suggested Citation

  • Wu, Yong-Ping & Feng, Guo-Lin, 2015. "A new algorithm for seasonal precipitation forecast based on global atmospheric hydrological water budget," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 478-488.
    • Handle: RePEc:eee:apmaco:v:268:y:2015:i:c:p:478-488
    DOI: 10.1016/j.amc.2015.06.059
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    References listed on IDEAS

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    1. Halkos, George & Tsilika, Kyriaki, 2014. "Analyzing and visualizing the synergistic impact mechanisms of climate change related costs," MPRA Paper 55459, University Library of Munich, Germany.
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    Cited by:

    1. Lahmiri, Salim, 2018. "Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 444-451.
    2. ChaoJiu Da & Fang Li & BingLu Shen & PengCheng Yan & Jian Song & DeShan Ma, 2017. "Detection of a sudden change of the field time series based on the Lorenz system," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
    3. Wu, Y.P. & Hu, Y.Y. & Cao, H.X. & Fu, C.F. & Feng, G.L., 2018. "Computing entropy change in synoptic-scale system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 163-168.

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