Climatology
khadijeh javan; mohammadreza Azizzadeh
Abstract
The outputs of general circulation models (GCMs) usually have a bias compared to observational data, and some corrections must be made before using them to develop future climate scenarios. The bias correction methods are the standard statistical methods for processing the output of climate models. In ...
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The outputs of general circulation models (GCMs) usually have a bias compared to observational data, and some corrections must be made before using them to develop future climate scenarios. The bias correction methods are the standard statistical methods for processing the output of climate models. In this research, the effect of five bias correction methods on the projected precipitation of the GFDL-ESM4 model in the Lake Urmia basin has been evaluated. The methods used in this research include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and delta change factor (DC). Statistical metrics such as the correlation coefficient, root mean square error (RMSE) and percentage bias (PBias) have been used to evaluate the accuracy of the corrected data in the period of 1990-2014 compared to the observational data and to choose the best method for correcting the data of future scenarios. research results showed that the delta change method significantly improved the raw estimates after correction; Therefore, this method was used to correct the data of scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5. In addition, the projection of the mean annual precipitation shows a decrease between 2 and 9 percent in SSP1-2.6, between 5 and 17 percent in SSP2-4.5, and between 8 and 26 percent in SSP2-8.5 compared to the observed data.
Climatology
Mohammad Reza Azizzadeyya Varzegan; khadijeh javan
Volume 23, Issue 70 , March 2020, , Pages 227-246
Abstract
One of the most important effects of climate change is increasing in extreme climate events. Change in the frequency or intensity of extreme events can have significant impacts on natural environments and human societies so their analysis is very important. The aim of this study is to identify the trend ...
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One of the most important effects of climate change is increasing in extreme climate events. Change in the frequency or intensity of extreme events can have significant impacts on natural environments and human societies so their analysis is very important. The aim of this study is to identify the trend of precipitation extremes in Lake Urmia basin and to investigate their relation with Teleconnection patterns. For this purpose, daily precipitation data of 7 synoptic stations in the basin during 1987-2014 was used. 11 extreme precipitation indices were extracted using the RClimDex and their trends were calculated by non-parametric Mann-Kendall test. Then the relationship between these indices with Teleconnection patterns was determined by the Pearson correlation coefficient. The results of time series analysis showed that all extreme precipitation indices in Lake Urmia basin have decreasing trend exept consecutive dry days (CDD). The spatial distribution of trend in extreme indices showed almost all indices have a significant trend at the 5% significance level in basin. There is no significant trend in consecutive dry days (CDD). The changes in extreme precipitation could be affected by the El Niño-Southern Oscillation (ENSO), Southern Oscillation Index (SOI), East Pacific-North Pacific (EP-NP), Madden and Julian Oscillation (MJO) and Pacific Decadal Oscillation (PDO).
Climatology
khadijeh javan; ali akbar rasuli; mahdi erfanian; behroz sari sarraf
Volume 22, Issue 65 , November 2018, , Pages 83-100
Abstract
Rainfall is one of the most important elements to determine the climate. Therefore, it is important to estimate its value accurately. The main purpose of this study is the evaluation of the TRMM (Tropical Rain Measurement Mission) 3B42 rainfall estimates, an exponential model and conceptual cloud model ...
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Rainfall is one of the most important elements to determine the climate. Therefore, it is important to estimate its value accurately. The main purpose of this study is the evaluation of the TRMM (Tropical Rain Measurement Mission) 3B42 rainfall estimates, an exponential model and conceptual cloud model in Lake Urmia Basin. Therefore, this study focuses on the comparison of these methods to identify and select the most appropriate model for rainfall estimation in Lake Urmia Basin. The comparison are performed during the period 2007 to 2011 and the hourly rainfall, temperature, barometric pressure and dew point temperature, the three-hourly rainfall rate of TRMM 3B42-V6 at 0.25° resolution and thermal infrared images (TIR) of Meteosat 7 at six-hour intervals are used. The results indicated acceptable match of estimated rainfall with rain-gauge data. Comparison of three methods of rainfall estimation shows that exponential model has the determination coefficient (equal to 0.61). In addition to the high correlation, due to low levels of RMSE and MAE (respectively 1.58 and 1.01), has a good performance to estimate rainfall in this basin. Therefore, this model can introduced as the most appropriate model for estimating rainfall in Lake Urmia basin.
Ali Mohammad Khorshiddoust; Gholam Hasan Mohammadi; Atefeh Hosseini Sadr; Khadijeh Javan; Abolfazl Jamali
Volume 17, Issue 46 , February 2014, , Pages 47-66
Abstract
The effective synoptic systems on dust events was investigated in the west of Iran based on observed dusty days at 50 meteorological stations using principal component analysis (PCA) and geographical information system (GIS). Results showed that the first 5 components explained 69.11% of spatial variability ...
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The effective synoptic systems on dust events was investigated in the west of Iran based on observed dusty days at 50 meteorological stations using principal component analysis (PCA) and geographical information system (GIS). Results showed that the first 5 components explained 69.11% of spatial variability of dust event variance. For detecting of affective synoptic systems, the computed correlation coefficient of stations with each component (Rotated Component Matrix) was transferred to the GIS environment, and synoptic patterns that affected spatial variability of dust events were simulated through Kriging interpolation method. It was shown that Azores high pressure system has the most effective role in frequency of days with dust in the west of Iran through the creation of surface thermal low pressures.
