Application of a Non-Parametrical New Method in Simulation models of Temperature and Precipitation Variables Data Defined in Terms of Climate Change Scenarios
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Abstract:
Introduction In absence of long-term time series of meteorological variables required for different type of studies, simulation models of these variables, generally known as weather generators (WGs), have been developed in recently. Taking into account the significant climatic variations defined in climate change scenarios, it seems necessary to define these changes upon simulated series. Since conventional weather generators do not have such a capability, new methods should be taken into consideration. Models that have been used in empirical downscaling studies include multiple linear regression, canonical correlation analysis, nearest neighbor and artificial neural network approaches. These models can account for variability in the surface variables to an acceptable level but extrapolation beyond the historical conditions may be unreliable. The ability of the downscaling models to predict climate change impacts is greatly reduced due to the coarse spatial resolution of GCMs. This paper describes the application of a non-parametric approach called strategic re-sampling, in simulation of series along with the climate change scenarios. Using this method, these changes were conditioned upon observed series and then by using a weather generator, transferred to simulated series. The basic concept of the models is the non-parametric method of Nearest Neighborhood.Material and MethodsThe study was performed in 5 synoptic stations namely Tehran-Mehrabad, Mashhad, Tabriz, Qazvin and Bushehr stations. The target variables were maximum temperature, minimum temperature and rainfall. In this non-parametric simulation method, by defining a moving window with central feature vector and The atmospheric Aerosol Optical Depth (AOD) of the city of Tehran in each station was calculated using MODIS images. The calculated AODs were compared with pollution data collected at the station where a correlation coefficient of about 0.7 through a linear equation of (PM10 (μgr/m3) = 222.78*AOD - 65.091) was achieved. This is shown in figure (1) below: Fig. 1. A Model for extraction of PM10 from MODIS AODResults and Discussion Generally it seems that this amount of correlation for low spatial resolution data such as MODIS is acceptable. It should be noted that the AOD's pixel size are usually 10*10 km2 and therefore the ground underneath is covered only with a few of these pixels in each satellite passage, while the ground based measurement data are only valid in a limited distance around the station. This in many cases renders the accuracy of comparing satellite data and ground-based data difficult. One of the main sources of uncertainties was the MODIS AOD product low spatial resolution of 10 by 10 kilometers. While the results of measurement in the stations can hardly represents 100 square kilometers. ConclusionA positive relationship between aerosol optical depth and particulate matter of less than 10 nanometer size is discovered by many workers. To supply a model for this relationship use has been made from MODIS AOD products as well as in situ measurements of pollutions specially PM10. Despite low resolution of MODIS products, a correlation of about 0.7 was found between AOD and PM10 in Tehran. The results seem acceptable in situation of lack of continuous observation due to the present financial problems.
Language:
Persian
Published:
Journal of Climate Research, Volume:1 Issue: 3, 2012
Page:
75
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