A synoptic-climatology approach to increase the skill of numerical weather predictions over Iran
Simplifications used in regional climate models decrease the accuracy of the regional climate models. To overcome this deficiency, usually a statistical technique of MOS is used to improve the skill of gridded outputs of the Numerical Weather Prediction (NWP) models. In this paper, an experimental synoptic-climatology based method has been used to calibrate, and decrease amount of errors in GFS numerical weather prediction model. Usually, physiographic characteristics, climatic behavior and synoptic climatology of the region are not included in MOS techniques. In this regard, an experimental model for Precipitation potential using Synoptic-climatology and Physiographic characteristics (PSP) of the region has been developed for statistical downscaling of the NWP outputs over the study region under study. A Climatic and Physiographic Index for surface weather stations is defined to represent their climatic and physiographic characteristics in MOS technique. CPI covers monthly mean precipitation, temperature, monthly number of wet and dry days, and latitude and height of station. CPI index which is defined in this paper can be used as climate classification index. In this study daily gridded outputs from Global Forecast System (GFS) has been used for calibration and running the PSP experimental model. Inputs of the model are gridded meteorological parameters in 500 hpa and surface layer from GFS model. Data from more than 85 daily weather systems have been used to find synoptic climatology characteristics, and coefficients needed as input for PSP equations in the period of 2002-2007. Coefficients are computed by using regression equation between observed and computed precipitation over each station with. 85.
Natural Environment Change, Volume:3 Issue: 1, Winter - Spring 2017
1 to 9
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