Investigating satellite precipitation uncertainties over a mountainous area in the eastern Italian Alps

Thursday, 17 December 2015: 17:15
3008 (Moscone West)
Viviana Maggioni, George Mason University Fairfax, Fairfax, VA, United States, Efthymios Ioannis Nikolopoulos, University of Padova, Padova, Italy, Emmanouil N Anagnostou, University of Connecticut, Department of Civil & Environmental Engineering, Groton, CT, United States and Maco Borga Sr., University of Padova, Land, Environment, Agriculture and Forestry, Padova, Italy
Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events. In situ observations over mountainous areas are limited, but currently available satellite precipitation products are able to provide precipitation estimates over those areas. However, uncertainties in satellite precipitation estimates still represent the main limitation in utilizing these products in hydrological applications. Therefore, quantifying the uncertainty in satellite precipitation products is necessary for enabling an improved use of those products. The study is conducted on the Trentino Alto-Adige region, located in the eastern Italian Alps. Rainfall observations for a 10-yr period (2000-2009) derived from a dense rain gauge network in the region are used as reference. A number of satellite precipitation error properties, typically used in error modeling, are investigated and include the probability of detection, false alarm rates, missed events, spatial correlation of the error, and hit biases are investigated as a function of seasonality, satellite precipitation algorithm, satellite rainfall rate, gauge density, and temporal resolution. Three widely used satellite-based precipitation products are employed: 1) the Climate Prediction Center morphing (CMORPH) product; 2) the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN); and 3) the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 near–real time product (3B42-RT). These products are the ones on which the new GPM level-3 precipitation product - IMERG – algorithm is based upon. Therefore, a better understanding of uncertainties associated with each single product is fundamental for improving error modeling of this merged satellite precipitation algorithm over complex terrain regions.