Improving remotely-sensed precipitation estimates over mountainous regions

Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Civil Engineering, Turkey

Approval Date: 2013


Supervisor: İSMAİL YÜCEL


In support of the National Oceanic and Atmospheric Administration (NOAA) National Weather Service’s (NWS) flash flood warning and heavy precipitation forecast efforts, the NOAA National Environmental Satellite Data and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) has been providing satellite based precipitation estimates operationally since 1978. Two of the satellite based rainfall algorithms are the Hydro-Estimator (HE) and the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR). Satellite based rainfall algorithms need to be adjusted for the orographic events and atmospheric variables for the continued improvement of the estimates. However, unlike the HE algorithm, the SCaMPR does not currently make any adjustments for the effects of complex topography on rainfall estimate. Bias structure of the SCaMPR algorithm suggests that the rainfall algorithm underestimates precipitation in case of upward atmospheric movements and high temperature levels. Also SCaMPR algorithm overestimates rainfall in case of downward atmospheric movements and low temperature levels. A regionally dependent empirical elevation-based bias correction technique and also a temperature based bias correction technique may help to improve the quality of satellite-derived precipitation products. In this study, an orographic correction method and a temperature correction method that will enhance precipitation distribution, improve accuracy and remove topography and temperature dependent bias is developed for the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) rainfall algorithm to be used in operational forecasting for meteorological and hydrological applications.