Performance of statistical post processing techniques in improvement of monthly precipitation forecast of MRI-CGCM3 model over Khorasan-Razavi

Document Type : Original Article

Authors

1 Assistant Professor, Climate Change Division, Climate Research Institute (CRI), Mashhad, Iran

2 expert in charge of climate modeling/climate research institute

3 Expert in charge of Climate Change Modeling, Climate Change Division, Climate Research Institute (CRI), Mashhad, Iran

4 PhD Candidate, Hakim Sabzevari University, Sabzevar, Iran.

5 PhD Candidate, Hakim Sabzevari University, Sabzevar, Iran

Abstract

Precipitation forecast in monthly to seasonal time scales is one of the challenges facing the Iran meteorological organization. It is also one of the fundamental needs of water resources management in agriculture, industry and drinking water sectors. Development of numerical prediction in monthly time scale is much less than numerical short term prediction in Iran; in this regard, despite to short term weather prediction, there is no operational numerical monthly to seasonal forecast model in Iran. Lack of a reliable operational seasonal forecast system cause huge damages to water resources, agriculture and natural resources sectors in all country regions. MRI-CGCM3 is the operational dynamical seasonal forecast model which is being used in Japan Meteorological Administration (JMA). In this paper output of MRI-CGCM3 was post processed using three different techniques of multiple regressions (MR), moving average (MA) and artificial neural network (ANN) over three sites of Mashad, Sabzevar and Torbat-e-heydarieh. Post processed monthly precipitation obtained from three different methods were compared with Direct Model Output (DMO).Performance of monthly forecast has been increased by 6% up to 20% when applying post processing techniques to direct model output. Result confirmed that multiple regressions (MR) techniques have the highest performance in improvement of monthly forecast skill over selected stations among all three post processing techniques.

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References

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Iran-Water Resources Research
Volume 12, Issue 2
September 2016
Pages 83-92

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History

  • Receive Date: 22 March 2016
  • Revise Date: 08 May 2016
  • Accept Date: 11 May 2016

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