Understanding rainfall processes as the main driver of hydrological cycle is important for formulating future water management strategies; however, data availability challenging countries such Ethiopia. This study aims to evaluate and compare satellite estimates (SREs) derived from tropical measuring mission (TRMM 3B43v7), estimation remotely sensed information using artificial neural networks—...

the lack of reliable and updated precipitation datasets is the most important limitation in the study of many climatological and hydrological subjects, including climate change and temporal variability of precipitation in many data sparse areas around the globe. this is particularly valid for iran where vast areas of central-eastern country that host the iranian deserts, suffer from an inadequa...

Hydrologic models play an indispensable role in managing the scarce water resources of a region, and developing countries, availability distribution data are challenging. This research aimed to integrate compare satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate...

Predicting ground rainfall from satellite estimates is useful as input for many applications, especially for areas with sparse rain gauges. We propose a predictive model based on an Additive Gaussian process (AGP) which can be viewed as the sum of a GP for the influence of the satellite estimate and a GP for the spatial distribution of rainfall between gauges. The hyperparameters for the covari...