PERFORMANCE OF STATISTICAL POST PROCESSING TECHNIQUES IN IMPROVEMENT OF MONTHLY PRECIPITATION FORECAST OF MRI-CGCM3 MODEL OVER KHORASAN-RAZAVI PROVINCE

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BABAEIAN I.; KARIMIAN M.; MODIRIAN R.; BAYATANI F.; FAHIMINEJAD E.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2016 | Volume:12 | Issue:2 Page(s): 83-92

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Title

PERFORMANCE OF STATISTICAL POST PROCESSING TECHNIQUES IN IMPROVEMENT OF MONTHLY PRECIPITATION FORECAST OF MRI-CGCM3 MODEL OVER KHORASAN-RAZAVI PROVINCE

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Keywords

NUMERICAL PREDICTIONQ2

MRI-CGCM3Q2

POST PROCESSINGQ2

PRECIPITATIONQ2

KHORASAN RAZAVIQ2

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. In Iran the NUMERICAL PREDICTION in monthly time scale is much less practiced than the numerical short term prediction. Despite the need to the short term weather predictions, there is no operational numerical monthly to seasonal forecast model in Iran. Each year the lack of a reliable operational seasonal forecast system causes huge damages to water resources, agriculture and natural resources sectors all over the country. 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 in north eastern Iran. Post processed monthly PRECIPITATIONs were then compared with Direct Model Output (DMO). It is shown that the performance of monthly forecast has been increased by 6% up to 20% by applying POST PROCESSING techniques to direct model output. Result confirmed that multiple regressions (MR) techniques have the highest performance in improving the monthly forecast skill over selected stations.

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APA: Copy

BABAEIAN, I., KARIMIAN, M., MODIRIAN, R., BAYATANI, F., & FAHIMINEJAD, E.. (2016). PERFORMANCE OF STATISTICAL POST PROCESSING TECHNIQUES IN IMPROVEMENT OF MONTHLY PRECIPITATION FORECAST OF MRI-CGCM3 MODEL OVER KHORASAN-RAZAVI PROVINCE. IRAN-WATER RESOURCES RESEARCH, 12(2), 83-92. SID. https://sid.ir/paper/100120/en

Vancouver: Copy

BABAEIAN I., KARIMIAN M., MODIRIAN R., BAYATANI F., FAHIMINEJAD E.. PERFORMANCE OF STATISTICAL POST PROCESSING TECHNIQUES IN IMPROVEMENT OF MONTHLY PRECIPITATION FORECAST OF MRI-CGCM3 MODEL OVER KHORASAN-RAZAVI PROVINCE. IRAN-WATER RESOURCES RESEARCH[Internet]. 2016;12(2):83-92. Available from: https://sid.ir/paper/100120/en

IEEE: Copy

I. BABAEIAN, M. KARIMIAN, R. MODIRIAN, F. BAYATANI, and E. FAHIMINEJAD, “PERFORMANCE OF STATISTICAL POST PROCESSING TECHNIQUES IN IMPROVEMENT OF MONTHLY PRECIPITATION FORECAST OF MRI-CGCM3 MODEL OVER KHORASAN-RAZAVI PROVINCE,” IRAN-WATER RESOURCES RESEARCH, vol. 12, no. 2, pp. 83–92, 2016, [Online]. Available: https://sid.ir/paper/100120/en

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