False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-ba...

0022-1694/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.jhydrol.2012.05.055 ⇑ Corresponding author. Tel.: +86 25 83787480. E-mail address: [email protected] (L. Ren). This study first focuses on comprehensive evaluating three widely used satellite precipitation products (TMPA 3B42V6, TMPA 3B42RT, and CMORPH) with a dense rain gauge network in the Mishui basin (9972 km) in ...

Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolu...

Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case ...

Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific sp...

We explore a set of polarimetric radar data and rain gauge readings collected in the Midwestern US over several months, while aiming to improve existing rainfall prediction techniques with various supervised learning algorithms.1 We explore preprocessing techniques and evaluate Linear Regression and Feedforward Neural Networks models of summarized data against the Marshall-Palmer baseline.