In this paper, we present a comprehensive review of the data sources and estimation
methods of 30 currently available global precipitation data sets, including gauge‐based …

Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation
from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products …

Abstract We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2),
a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several …

New precipitation (P) datasets are released regularly, following innovations in weather
forecasting models, satellite retrieval methods, and multi-source merging techniques. Using …

This paper explores the many aspects of precipitation measurement that are relevant to
providing an accurate global assessment of this important environmental parameter …

A system for Precipitation Estimation from Remotely Sensed Information using Artificial
Neural Networks (PERSIANN) is under development at The University of Arizona. The …

The quantification of precipitation on a global scale is critical for applications ranging from
climate monitoring to water resource management. Conventional observations through …

A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely
Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification …

An analysis of temporal variations in gamma parameters of raindrop spectra is presented
utilizing surface-based observations from the Tropical Ocean Global Atmosphere Couple …

The Tropical Rainfall Measuring Mission (TRMM) satellite is planned for an operational
duration of at least three years, beginning in the mid-1990's. The main scientific goals for it …