THE OBJECTIVE OF THIS RESEARCH PROPOSAL IS TO DEVELOP A GENERAL FRAMEWORK FOR CORRECTION OF SATELLITE ESTIMATES OF OROGRAPHIC PRECIPITATION (IMERG TRMM PR AND GPM DPR) IN MOUNTAINOUS REGIONS CONSTRAINED TO MINIMIZE DISCREPANCIES AGAINST GROUNDBASED RAINFALL OBSERVATIONS AND TO CLOSE THE WATER BUDGET AGAINST STREAMFLOW OBSERVATIONS IN MOUNTAIN WATERSHEDS. TO DEVELOP THIS FRAMEWORK WE LEVERAGE 10 YEARS OF SCIENCE-GRADE OROGRAPHIC PRECIPITATION OBSERVATIONS (RAINGAUGES DISDROMETERS AND GROUND-BASED RADARS) IN THE IPHEX (INTEGRATED PRECIPITATION AND HYDROLOGY EXPERIMENT) DOMAIN STREAMFLOW OBSERVATIONS AT USGS BENCHMARK WATERSHEDS WITHIN CONUS AND RAINGAUGE OBSERVATIONS IN THE HIMALAYAS EASTERN ANDES WESTERN GHATS AUSTRIAN ALPS AND BRAZILIAN HIGHLANDS. THE RESEARCH STRATEGY IS TWO-FOLD. FIRST TO DETERMINE THE SPACE-DISTRIBUTION OF RAINFALL CORRECTIONS REQUIRED TO CLOSE THE WATER BUDGET OF BENCHMARK WATERSHEDS THAT IS TRUE RAINFALL USING A HYDROLOGIC MODEL WITH DATA ASSIMILATION. SECOND TO USE BIG DATA ANALYTICS TOOLS SPECIFICALLY MULTIFRACTAL DOWNSCALING SUPPORT VECTOR MACHINE LEARNING (SVM) SUPPORT VECTOR REGRESSION (SVR) AND ARTIFICIAL NEURAL NETWORKS (ANNS) TO MODEL THE ASSOCIATIONS BETWEEN RAINFALL INTENSITY RAINFALL CORRECTIONS AND SATELLITE QUANTITATIVE PRECIPITATION ESTIMATES (QPE) TO BUILD A TAXONOMY OF RAINFALL CORRECTIONS AND DEVELOP PREDICTOR-CORRECTOR MODELS TO IMPROVE SATELLITE QPE IN MOUNTAINOUS REGIONS. THE PREDICTOR-CORRECTOR MODELS AND CORRECTED QPE WILL BE EVALUATED AGAINST RAINFALL OBSERVATIONS IN HIGH AND MIDDLE MOUNTAINS WORLDWIDE AND FINALLY THE CORRECTED PRODUCTS WILL BE EXAMINED TO CHARACTERIZE THE INTERANNUAL VARIABILITY OF OROGRAPHIC PRECIPITATION PATTERNS AND EXTREME DAILY RAINFALL STATISTICS.