Gh.A Fallah Ghalhary; M Habibi Nokhandan; J Khoashhal
Volume 63, Issue 1 , June 2010, , Pages 55-74
Abstract
The aim of this research is the assessment of the relation between rainfall and large scale synoptically patterns at Khorasan Razavi province. In this study, using adaptive neuro fuzzy inference system, the rainfall estimation has been done from April to June in the Area under study. Spring rainfall ...
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The aim of this research is the assessment of the relation between rainfall and large scale synoptically patterns at Khorasan Razavi province. In this study, using adaptive neuro fuzzy inference system, the rainfall estimation has been done from April to June in the Area under study. Spring rainfall data including the information of 38 synoptic, Climatologic and rain gauge stations from 1970 to 2007 has been selected from Iranian Meteorological Organization and Ministry of Energy. In this paper, we are analyzed 38 years of rainfall data at Khorasan Razavi province located in northeastern part of Iran at latitude-longitude pairs (34°-38°N, 56°- 62°E). The Adaptive Neuro-Fuzzy Inference system based on synoptically patterns with 38 years of rainfall data was trained. For performance evaluation, network predicted outputs were compared with the actual rainfall data. In this Study, at the first step, the relationship Between synoptically pattern variations including Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Surface Pressure Difference (?SLP), Sea Surface Temperature Difference (?SST), air temperature at 700 hpa, thickness between 500 and 1000 hpa level, relative humidity at 300 hpa and precipitable water were investigated .As the second step, the model was calibrated from 1970 to 1997. Finally, rainfall prediction is performed from 1998 to 2007. The model that used in this research has an input layer, one hidden layer and an output layer. The number of neuron for input layer, hidden layer and output layer was 13-28-1, respectively. The results of simulation reveal that adaptive neuro fuzzy inference systems are promising and efficient.
Gh. A. Fallah Ghalhary; M. Mousavi Baygi; M. Habibi Nokhandan; J. Khoshhal
Volume 62, Issue 1 , June 2009, , Pages 111-124
Abstract
The research show that global climate changes and atmospheric general circulation are affected by large scale phenomena that occurred in the sea surface. These large scale phenomena are often named "climate large scale signals". These signals are calculated based on criteria such as sea Level Pressure ...
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The research show that global climate changes and atmospheric general circulation are affected by large scale phenomena that occurred in the sea surface. These large scale phenomena are often named "climate large scale signals". These signals are calculated based on criteria such as sea Level Pressure (SLP), Sea Surface Temperature (SST) and so on. A method for weather forecasting is a special approach based on statistical modeling. In this study, data of 37 rainfall stations were used to model the relation between precipitation and Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Level Pressure gradient (?SLP) and the difference between sea surface temperature and air temperature at 1000 HP. The results show that statistical modeling can successfully predict the amount of annual rainfall. The mean root square error for stepwise model were obtained 49 millimeters.
