This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellitebased system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven o...

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density ground-based measurements, gridded precipitation products (GPPs) from merged satellite-/gauge-/model-based constitute an attractive alternative. The quality which must, nevertheless, be verified. objective this study was to evaluate, at dif...

The studied region is the Greater Antilles (Cuba, Hispaniola, Jamaica and Puerto Rico) Lesser (Southern part of Caribbean arc). performance MSWEP, CHIRPS, PERSIANN-CDR, ERA-5 GPM IMERG were evaluated to highlight their qualities shortcomings guide researchers in choice these rainfall datasets use for hydro-meteorological applications this study area. Five quantitative (RMSE, KGE his three compo...

Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP still significant, limiting their use without evaluation and often bias correction. The correction of remains challenge atmospheric scientists. present study evaluated performance six SBPs, namely, SM2RAIN-ASCAT, IMERG, GSMaP, CHIRPS, PERSIANN-C...

[1] In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conc...

In the present study, four high-resolution multi-sensor blended precipitation products, TRMM Multisatellite Precipitation Analysis (TMPA) research product (3B42 V7) and near real-time product (3B42 RT), Climate Prediction Center MORPHing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), are evaluated over the Yangtze Ri...