Development and Evaluation the Performance of Ann Based Statistical Downscaling Models For Daily and Monthly Precipitation.

Abstract Statistical downscaling techniques represent a quantitative relationship between large-scale atmospheric variables (predictors) and local-scale meteorological variables (predictand) such as precipitation. This study uses large-scale atmospheric as predictor variables derived from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data set and precipitation in Izmir city stations as predictant.The purpose of this study is to develop statistical downscaling models for daily and monthly precipitation over Izmir city by using Artificial Neural Network) ANN( methods and comparison of the performance of those models. The results revealed that the performance of the daily model improves with the aggregated of daily results. Although the performance of the daily model gives fair (not high) results (e.g., R2 ranging from 0.362 to 0.331), the aggregated model gives very good results at monthly level (e.g., R2 is more than 0.73). The monthly statistical downscaling model gives very good results for the study area (e.g. R2 ranging between 0.65 to 0.73).It was founded that a slight variation from the performance of aggregated monthly model and the monthly model. However, the aggregated monthly provided better results. Although the accuracy of the aggregated monthly model is higher, it required a significant amount of time and effort.ANN-based downscaling model could be used to determine the inputs of water potential studies, but the results of the model have to be improved by Statistical methods such as Bias correction in order to use it in floods study.