COMPARISON OF LARS_WG AND REGCM4 MODELS IN SIMULATION AND POST-PROCESSING OF ANNUAL TEMPERATURE AND RAIN FALL DATA IN GREAT KHORASAN

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AHMADI MAHMOOD; LASHKARI HASAN; KEIKHOSRAVI GHASEM; AZADI MAJID

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Journal: GEOGRAPHICAL DATA Year:2016 | Volume:25 | Issue:98 Page(s): 157-170

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Title

COMPARISON OF LARS_WG AND REGCM4 MODELS IN SIMULATION AND POST-PROCESSING OF ANNUAL TEMPERATURE AND RAIN FALL DATA IN GREAT KHORASAN

Author(s)

AHMADI MAHMOOD | LASHKARI HASAN | KEIKHOSRAVI GHASEM | AZADI MAJID | Issue Writer Certificate

Keywords

GREAT KHORASANQ2

POST-PROCESSSINGQ2

DYNAMIC REGCM4 MODELQ1

STATISTIC LARS_WG MODELQ1

Abstract

In this study, rainfall and temperature simulated annual in north - east of Iran (GREAT KHORASAN) In the period 1987-2011. Based on the results, in LARS model during verification period (2007-2013), average annual rainfall raw bias is equal to 53.63 millimeter and post- processed is -11.25. Shortly, in annual period, in 84% of studied stations, post-processing techniques have been effective and bias error rate has decreased heavily in more stations. Based on the results, in Reg-CM4 model during verification period (2006-2011), average annual rainfall raw bias is equal to 85.3 millimeter and post- processed is 61.04. Shortly, in annual period, in 75% of studied stations, post-processing techniques have been effective and MA technique is more effective. So absolute bias error after processing an average annual rainfall of LARS equal to 6.13 and RegCM4 model is 61.average annual temperature raw bias is equal to 0.096 millimeter and post - processed is -0.432. This is more than bias without post - processing hence, post-processing is not effective in all stations and only in 46% of stations are good. Simulation of 2-- meter temperature data in climate stations with RegCM model and MA technique show high effectiveness. Average annual temperature raw bias in RegCM4 model is equal to -2.78 millimeter and post- processed is -0.05. In all stations modeled annual temperature with Observed data has difference less than 0.1. Also in rainfall simulation, LARS_WG model is more better than RegCM model and in simulation of annual temperature data, DYNAMIC REGCM4 MODEL show more clear Objectivity of researched area rather than LARS_WG model.

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APA: Copy

AHMADI, MAHMOOD, LASHKARI, HASAN, KEIKHOSRAVI, GHASEM, & AZADI, MAJID. (2016). COMPARISON OF LARS_WG AND REGCM4 MODELS IN SIMULATION AND POST-PROCESSING OF ANNUAL TEMPERATURE AND RAIN FALL DATA IN GREAT KHORASAN. GEOGRAPHICAL DATA, 25(98), 157-170. SID. https://sid.ir/paper/253221/en

Vancouver: Copy

AHMADI MAHMOOD, LASHKARI HASAN, KEIKHOSRAVI GHASEM, AZADI MAJID. COMPARISON OF LARS_WG AND REGCM4 MODELS IN SIMULATION AND POST-PROCESSING OF ANNUAL TEMPERATURE AND RAIN FALL DATA IN GREAT KHORASAN. GEOGRAPHICAL DATA[Internet]. 2016;25(98):157-170. Available from: https://sid.ir/paper/253221/en

IEEE: Copy

MAHMOOD AHMADI, HASAN LASHKARI, GHASEM KEIKHOSRAVI, and MAJID AZADI, “COMPARISON OF LARS_WG AND REGCM4 MODELS IN SIMULATION AND POST-PROCESSING OF ANNUAL TEMPERATURE AND RAIN FALL DATA IN GREAT KHORASAN,” GEOGRAPHICAL DATA, vol. 25, no. 98, pp. 157–170, 2016, [Online]. Available: https://sid.ir/paper/253221/en

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