Function-Based Troposphere Tomography Technique for Optimal Downscaling of Precipitation

Precipitation is an important meteorological indicator that has a direct and significant impact on ecology, agriculture, hydrology, other vital areas of human health life. It therefore essential to monitor variations this parameter at global local scale. To predict long-term changes in climate elements, Global Circulation Models (GCMs) can provide simulated global-scale climatic processes. Due the low spatial resolution these models, downscaling methods are required convert such large-scale information regional-scale data for applications. Among methods, Statistical DownScaling Model (SDSM) Artificial Neural Networks (ANNs) widely used due their computational volume suitable output. These models mainly require training data, generally, reanalysis obtained from National Center Environmental Prediction (NCEP) European Centre Medium-range Weather Forecasts (ECMWF) purpose. With optimal method, instead applying humidity indices extracted ECMWF outputs function-based tropospheric tomography technique Navigation Satellite System (GNSS) will be used. The reconstructed then fed SDSM ANN downscaling. results both indicate increase accuracy process by about 20 mm wet months year. This corresponds average improvement 38% with regard root mean square error (RMSE) monthly precipitation.